Under the dual impacts of climate change and COVID-19, there are great risks to the world’s food security. Rice is one of the three major food crops of the world. Assessing the impact of climate change on future rice production is very important for ensuring global food security. This article divides the world’s main rice-producing regions into four regions and uses a multivariate nonlinear model based on historical economic and climatic data to explore the impacts of historical extreme climatic events and economic factors on rice yield. Based on these historical models, future climatic data, and economic data under different shared socioeconomic pathways (SSPs), the yields of four major rice-producing regions of the world under different climate change scenarios (SSP126, SSP245, and SSP585) are predicted. The research results reveal that under different climate change scenarios, extreme high-temperature events (Tx90p) and extreme precipitation events (Rx5day, R99pTOT) in the four major rice-producing regions have an upward trend in the future. Extreme low-temperature events (Tn10p) have a downward trend. In the rice-producing regions of Southeast Asia and South America, extreme precipitation events will increase significantly in the future. The prediction results of this model indicate that the rice output of these four major rice-producing regions will show an upward trend in the future. Although extreme precipitation events will have a negative impact on rice production, future increases in rice planting areas, economic development, and population growth will all contribute to an increase in rice production. The increase in food demand caused by population growth also brings uncertainty to global food security. This research is helpful for further understanding climate change trends and risks to global rice-production areas in the future and provides an important reference for global rice-production planning and risk management.

Understanding the temporal and spatial distribution in disasters plays an important role in disaster risk management. The present study aims to explore the long-term trends in drought and floods over China and estimate the economic losses they cause. A peak-over-threshold approach is used to identify flood peaks, and the relationship between the disasters and climate indices is investigated using Poisson regression. The major results are as follows: (1) the northeastern part of China was severely affected by drought disasters (average damaged area was 6.44 million hectares); (2) the northern part of East China and Central China upstream of the Yangtze River were severely affected by flood disasters (average damaged area was 3.97 million hectares); (3) in the Yangtze River Basin, there are increasing trends in terms of drought and extreme precipitation, especially upstream of the Yangtze River, accompanied by severe disaster losses; and (4) by combining the trends in drought and extreme precipitation days with the spatial distribution of damaged areas, the study indicates that the increasing trend in droughts has shifted gradually from north to south, and the increasing trend in extreme precipitation gradually has shifted from south to north.

Drought risk analysis can help improve disaster management techniques, thereby reducing potential drought risk under the impacts of climate change. This study analyses observed and model-simulated spatial patterns of changes in drought risk in vulnerable eco-regions in China during 1988–2017 and 2020–2050 using an analytic hierarchy process (AHP) method. To perform a risk assessment and estimation of a drought disaster, three subsystems—namely hazard, vulnerability and exposure—are assessed in terms of the effects of climate change since the middle of the 21st century: (i) Hazards, represented by climate anomalies related to the drought process, such as changes in rainfall averages, temperature averages and evaporation averages; (ii) vulnerability, encompassing land use and mutual transposition between them; (iii) exposure, consisting of socioeconomic, demographic, and farming. The results demonstrated that high hazards continue to be located in the arid zone, high vulnerability levels occur in the Junggar Basin and Inner Mongolia Plateau, and high exposure levels occur Loess Plateau and southern coastal area. In this way, the results provide exhaustive measures for proactive drought risk management and mitigation strategies.

Environmentally extended input-output analysis (EEIOA) has long been used to quantify global and regional environmental impacts and to clarify emission transfers. Structural path analysis (SPA), a technique based on EEIOA, is especially useful for measuring significant flows in this environmental-economic system. This paper constructs an imports-adjusted single-region input-output (SRIO) model considering only domestic final use elements, and it uses the SPA technique to highlight crucial routes along the production chain in both final use and sectoral perspectives. The results indicate that future mitigation policies on household consumption should change direct energy use structures in rural areas, cut unreasonable demand for power and chemical products, and focus on urban areas due to their consistently higher magnitudes than rural areas in the structural routes. Impacts originating from government spending should be tackled by managing onsite energy use in 3 major service sectors and promoting cleaner fuels and energy-saving techniques in the transport sector. Policies on investment should concentrate on sectoral interrelationships along the production chain by setting up standards to regulate upstream industries, especially for the services, construction and equipment manufacturing sectors, which have high demand pulling effects. Apart from the similar methods above, mitigating policies in exports should also consider improving embodied technology and quality in manufactured products to achieve sustainable development. Additionally, detailed sectoral results in the coal extraction industry highlight the onsite energy use management in large domestic companies, emphasize energy structure rearrangement, and indicate resources and energy safety issues. Conclusions based on the construction and public administration sectors reveal that future mitigation in secondary and tertiary industries should be combined with upstream emission intensive industries in a systematic viewpoint to achieve sustainable development. Overall, SPA is a useful tool in empirical studies, and it can be used to analyze national environmental impacts and guide future mitigation policies.

Quantitatively assessing the spatial divergence of the sensitivity of crop yield to climate change is of great significance for reducing the climate change risk to food production. We use socio-economic and climatic data from 1981 to 2015 to examine how climate variability led to variation in yield, as simulated by an economy–climate model (C-D-C). The sensitivity of crop yield to the impact of climate change refers to the change in yield caused by changing climatic factors under the condition of constant non-climatic factors. An ‘output elasticity of comprehensive climate factor (CCF)’ approach determines the sensitivity, using the yields per hectare for grain, rice, wheat and maize in China’s main grain-producing areas as a case study. The results show that the CCF has a negative trend at a rate of −0.84/(10a) in the North region, while a positive trend of 0.79/(10a) is observed for the South region. Climate change promotes the ensemble increase in yields, and the contribution of agricultural labor force and total mechanical power to yields are greater, indicating that the yield in major grain-producing areas mainly depends on labor resources and the level of mechanization. However, the sensitivities to climate change of different crop yields to climate change present obvious regional differences: the sensitivity to climate change of the yield per hectare for maize in the North region was stronger than that in the South region. Therefore, the increase in the yield per hectare for maize in the North region due to the positive impacts of climate change was greater than that in the South region. In contrast, the sensitivity to climate change of the yield per hectare for rice in the South region was stronger than that in the North region. Furthermore, the sensitivity to climate change of maize per hectare yield was stronger than that of rice and wheat in the North region, and that of rice was the highest of the three crop yields in the South region. Finally, the economy–climate sensitivity zones of different crops were determined by the output elasticity of the CCF to help adapt to climate change and prevent food production risks.

Global emergencies have a profound impact on exacerbating food insecurity, and the protracted Russia–Ukraine conflict has emerged as a significant driver of a global food crisis. Accurately quantifying the impact of this conflict is crucial for achieving sustainable development goals. The multi-indicator comprehensive evaluation approach was used to construct a grain security composite index (GSCI). Moreover, econometric model was used to predict the potential impacts of the conflict on global grain security in 2030 under two scenarios: with and without the “Russia-Ukraine conflict”. The results conclude that global food prices reached unprecedented levels as a consequence of the conflict, leading to notable fluctuations in food prices, especially with a significant surge in wheat prices. The conflict had a negative impact on global grain security, resulting in a decline in grain security from 0.538 to 0.419. Predictions indicate that the influence of the conflict on global grain security will be substantially greater compared to the scenario without the conflict in 2023–2030, ranging from 0.033 to 0.13. Furthermore, grain security will first decrease and then increase under the sustained consequences of the conflict. The achievement of the 2030 sustainable development goals will encounter significant challenges in light of these circumstances.