Xizhao Wang

The advancement of the digital economy is vital for decreasing agricultural carbon emissions and fostering high-quality agricultural development. Using panel data from 31 Chinese provinces between 2000 and 2021, this paper employs a dual machine learning model for causal inference to analyze the impact of digital financial inclusion on agricultural carbon emissions intensity, its underlying mechanisms, and the characteristics of heterogeneity. The study finds that digital inclusive finance significantly reduces agricultural carbon intensity through two main channels: enhancing scientific and technological innovation and narrowing the urban-rural income gap. Additionally, the expansion of arable land management and the acceleration of economic structural transformation positively moderate these effects. These conclusions remain robust after a series of robustness tests. Further combining factors such as resource endowment, geographic location, economic concentration, and food production areas in the heterogeneity test, the study found that regional differences significantly influence the effect of financial inclusion on agricultural carbon intensity. Therefore, it is essential to enhance the development of inclusive finance, break down regional barriers to promote synergistic development, actively support economic transformation and large-scale operations, strengthen scientific and technological innovation, and narrow the urban-rural income gap to support China’s agricultural green transformation.

There is a strong connection between the Federal Reserve’s monetary policy and the trend of international food prices. Employing the average information share model, EGARCH(Exponential Generalized Autoregressive Conditional Heteroskedasticity), and DCC-MGARCH(Dynamic Conditional Correlation-Multivariate Generalized Autoregressive Conditional Heteroskedasticity) models, this study investigates the relationship between the U.S. dollar index, international grain futures prices, and spot prices in the context of Federal Reserve monetary policy adjustments from 2000 to 2023. The findings reveal that, first, under conditions of long-run cointegration, the U.S. dollar index exerts a strong pricing influence over international grain futures, while grain futures demonstrate a significant price discovery function over spot prices. Second, both international grain futures and spot markets exhibit asymmetric volatility, with price increases being more pronounced than decreases in response to external shocks. Additionally, the U.S. dollar index has a unidirectional and inverse influence on grain futures prices, while futures and spot prices interact bidirectionally and move in the same direction. This paper contributes to understanding the impact of Federal Reserve monetary policy adjustments on international food prices and offers policy insights for countries to manage food import risks and maintain price stability.

Against the backdrop of increasing financialization of grain markets, the cross-cycle and cross-market contagion among commodities has been intensifying. To investigate the risk spillover among commodities across different cycles, this study selected UK WTI crude oil and soybean, corn, and wheat futures prices from the Chicago Board of Trade as research subjects. Using ensemble empirical mode decomposition (EEMD), the original sequences were decomposed into sub-sequences of different frequencies. Based on these frequency characteristics, long-term, medium-term, and short-term fluctuations were constructed. The BEKK-GARCH model was then applied to explore the volatility spillover across markets under different cycles. The results indicate that in terms of pricing mechanisms, crude oil futures dominate the price fluctuations of grain futures. In terms of risk spillover across different cycles, there is a bidirectional risk spillover effect between crude oil and grain futures in short-term and medium-term fluctuations, while in long-term fluctuations, there is only a unidirectional transmission from crude oil futures to grain futures. Based on the research findings, this paper proposes relevant policy recommendations, aiming to provide government regulatory authorities and futures investors with policy guidance and a theoretical foundation across different cycles.