Scientific measurement and analysis of the spatial and temporal distribution characteristics of agricultural carbon emissions (ACEs) and the influencing factors are important prerequisites for the formulation of reasonable ACEs reduction policies. Compared with previous studies, this paper fully considers the heterogeneity of rice carbon emission coefficients, measures and analyzes the spatial and temporal characteristics of ACEs in Jiangsu Province from three carbon sources, including agricultural land use, rice cultivation, and livestock and poultry breeding, and explores spatial clustering patterns and driving factors, which can provide a reference for agricultural low-carbon production. The results indicate that from 2005 to 2020, Jiangsu’s ACEs showed a decreasing trend, with an average annual decrease of 0.32%, while agricultural carbon emission density (ACED) showed an increasing trend, with an average annual increase of 0.16%. The area with the highest values for ACEs is concentrated in the northern region of Jiangsu, while the areas with the highest values for ACED are distributed in the southern region. The spatial clustering characteristics of ACEs have been strengthening. The “H-H” agglomeration is mainly concentrated in Lianyungang and Suqian, while the “L-L” agglomeration is concentrated in Zhenjiang, Changzhou, and Wuxi. Each 1% change in rural population, economic development level, agricultural technology factors, agricultural industry structure, urbanization level, rural investment, and per capita disposable income of farmers causes changes of 0.112%, −0.127%, −0.116%, 0.192%, −0.110%, −0.114%, and −0.123% in Jiangsu’s ACEs, respectively. To promote carbon emission reduction in agriculture in Jiangsu Province, we should actively promote the development of regional synergistic carbon reduction, accelerate the construction of new urbanization, and guide the coordinated development of agriculture, forestry, animal husbandry, and fisheries industries.

The COVID-19 pandemic triggered a weakening of expectations of market players and local governments. It is necessary to explore some effective paths to stabilize China’s market expectations. This paper draws on computable general equilibrium (CGE) model elasticity indicators and marginal utility indicators to simulate the impact of the COVID-19 pandemic on market agents and the impact of shocks brought about by the COVID-19 pandemic on various industry sectors. Our results first show that the Keynesian closure is still valid, with the impact values of the COVID-19 pandemic on GDP, urban consumption, rural consumption, investment, imports, and exports being 2.35%, 7.96%, 9.79%, 4.10%, −3.13%, and 6.15%, respectively, with the COVID-19 pandemic shock having a particularly strong impact on rural consumption. Second, the impact of the COVID-19 pandemic on different industries is comprehensive and non-equilibrium. In consumption, urban and rural residents have the largest changes in consumption demand in the tertiary and primary industries, and the overall change in consumption of rural residents is larger than that of urban residents. In investment, the tertiary industry investment changes most significantly, while the primary industry has a gentle change. The impact of weaker import and export expectations on industry imports and exports is concentrated in the secondary and primary industries. Third, the weakening expectation induces a decline in the multiplier effect, making it difficult for economic growth to return to pre-pandemic levels, which needs to be accompanied by fiscal policies such as reducing taxes, lowering fees, and raising fiscal spending to achieve growth targets. In addition, while fiscal policy significantly boosts import and export trade, it has a significantly greater impact on exports than imports.

Resource misallocation is one of the important manifestations of agricultural supply-side distortion and an important causal factor that hinders food production increase and affects food security. Did the COVID-19 pandemic intensify China’s food production misallocation? The extent and consequences require quantitative assessment and scenario analysis. In this paper, we use a combination of input-output model and computable general equilibrium (CGE) model, and further incorporate the most important input factor in agriculture—intermediate inputs—into the model. At the same time, simulation of the pandemic impact from the demand and supply sides, respectively, and scenario analysis of the impact of the COVID-19 pandemic on China’s food production. The results of the study show that: first, compared with the baseline level before the epidemic, the overall TFP growth of China’s food industry chain decreased, and the TFP growth rate of the food distribution sector decreased most significantly. Second, there are significant factor misallocation distortions of capital, labor, and intermediate inputs. Third, in the short term, the period of the COVID-19 pandemic led to a decline in the vitality of the national labor market, but the return of non-farm employed labor in rural areas instead reduced the degree of labor misallocation in the food sector. Fourth, the demand side has a greater impact on China’s food production, among which the consumer demand has a particularly strong impact on the resource allocation of food production, and the short-term shock will mainly have a more obvious impact on the allocation of labor factors and the allocation of intermediate input factors in the food industry chain. Accordingly, this paper proposes that in order to guarantee China’s food security and adapt to the short-term characteristics of the era when the COVID-19 pandemic is rampant, China should make efforts in four areas: rational allocation of food production resources and factors, solid construction of the whole food industry chain, stable guarantee of the food market system and transfer to enhance social expectations.

This study considers consumers’ risk perceptions and safety preferences as external shock factors in food safety incidents. These factors are incorporated into a general equilibrium model defined by the food safety hierarchy, and the computational experiment method is employed to examine the direction of spillover effects. According to the findings, the spillover direction and intensity of food safety incidents are jointly influenced by the characteristics of consumers, food and the market. When an incident raises consumers’ safety concerns, a negative effect occurs throughout all food sectors. When an incident has a specific impact on consumers’ risk perception, the direction of the spillover is contingent upon the safety level of the product in question. In the event that the food involved in an incident is extremely secure, it may have a detrimental effect on unrelated food goods; conversely, it may have a beneficial effect on unrelated food goods. The incident’s impact has increased in proportion to the market share of the affected food. When the market share remains constant, the impact intensity increases as the degree of food safety improves. Higher market-wide risk levels are associated with more pronounced and quicker effects. This study improves understanding of spillover patterns in food safety situations, which aids in the formulation of focused policy responses and initiatives.