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Climate change leads to the frequent occurrence of natural disasters. It has had a severe impact on agricultural economic growth in China. Based on data in China from 2004 to 2020, this paper uses a fixed-effect model to discuss the effects of natural disasters on agricultural economic growth. The empirical results are as follows. (1) Natural disasters negatively affect agricultural economic growth. The regression coefficient of natural disasters on agricultural economic growth is −0.1183. (2) The inhibition of agricultural economic growth by natural disasters is more prominent in areas with low government attention and average agricultural production conditions. (3) The mechanism analysis shows that natural disasters inhibit agricultural economic growth through channels that cause the spread of diseases and pests. (4) Expanding the scale of agricultural insurance can prevent the impact of natural disasters on agricultural economic growth. This research provides policy recommendations for preventing the negative impacts of natural disasters. Agricultural producers should raise awareness of disaster management and improve the accuracy of disaster warning mechanisms. • This paper measures the level of natural disasters in China. • This paper expands the application field of functional data analysis methods. • This paper examines the mechanism of natural disasters affecting agricultural economic growth. • This paper provides policy suggestions for ensuring agricultural economic growth. [ABSTRACT FROM AUTHOR]

Due to diverse methodologies and data sources, China's agricultural greenhouse gas (AGHG) emissions estimates demonstrate significant variability. A quantitative review was conducted on the prevalence and application of various accounting methods to clarify and reduce discrepancies in AGHG emissions estimation techniques and data application. Using a systematic PRISMA framework, 4257 global studies were analyzed, yielding 681 AGHG emission records from 57 papers and four databases explicitly focusing on China. The analysis reveals similarities between Chinese and global research trends, concentrating on environmental science, soil science, and ecology. Furthermore, the spatial scale and methodologies for China's AGHG emissions estimation are relatively centralized, with 53.21% of the research targeting site-specific scales and 74.12% employing inventory methods and statistical analysis. Regarding the variability in China's AGHG emission estimates between 1996 and 2020, estimates ranged from 100 to 800 Mt. C-eq, with the highest estimate being approximately 25 times the minimum within the same period. Notably, compared to the average AGHG emissions calculated for 1996–2020 (284.7 to 548.7 Mt. C-eq), estimates from the FAO and Our World in Data were higher by an average of 114.7% and 81.9%, respectively. These findings underscore the need to enhance remote sensing and survey data application and standardize AGHG emissions estimation methodologies and boundaries to ensure comparability across various accounting methods and datasets. This study conducts a quantitative analysis to identify and bridge the gaps in AGHG emission evaluation techniques, laying the groundwork for establishing a more comprehensive and resilient framework for AGHG emissions assessments. • An analysis of 4257 studies is conducted to assess uncertainties and discrepancies in AGHG emissions in China. • 53.21% of AGHG research targets site-specific scales; 74.12% uses inventory methods and statistical analysis. • The gap between different estimates of China's annual AGHG emissions is as high as 25 times. • Emissions estimates by the FAO and OWD exceed those from 57 scholarly articles by 114.7% and 81.9%, respectively. [ABSTRACT FROM AUTHOR]

Understanding future climate change is crucial for securing and managing water supplies necessary for agricultural activities in China, particularly in rainfed agriculture regions. This paper evaluates the projected climate change for nine major agricultural zones in China, using ensembles of regional climate models (RCMs) simulated under the Coordinated Regional Downscaling Experiment-East Asia second phase (CORDEX-EA-II) framework. The simulations were conducted for two climate scenarios (RCP2.6 and RCP8.5) at a 25 km grid spacing, with three RCMs (RegCM4, REMO, and WRF) driven by multiple CMIP5 global climate models (GCMs). The study compares the RCM simulations to the driving CMIP5 and the recent CMIP6 ensembles, focusing on surface temperature, precipitation, and surface relative humidity for the present day (1981–2010), mid-future (2036–2065), and far future (2070–2099) periods. The model evaluations indicate that the GCM and RCM simulations are consistent with observations, with RCMs showing better performance than the driving GCMs. The results show that both GCMs and RCMs project increased temperature and precipitation in most agricultural zones of China in the future, with the CMIP6 ensembles projecting the largest temperature increase and the CORDEX-EA-II ensemble showing the finest spatial details. Relative humidity is projected to decrease over major rice-producing areas of South China in the CORDEX-EA-II, CMIP5, and CMIP6 ensembles. This study provides valuable information to support climate change adaptation and mitigation efforts in China, particularly in the agricultural sectors. • CORDEX-EA-II simulations demonstrate better model performance over China. • Temperature and precipitation are projected to increase in the future. • Relative humidity is projected to decrease over South China. [ABSTRACT FROM AUTHOR]

The agricultural sector is extremely vulnerable to climate change and also plays a very important role in the operation of the entire economic system, leading many studies to evaluate the possible agricultural direct economic damage (ADED) of climate change. However, this damage may have an amplification impact through the intricate industrial linkages that exist among sectors, and this impact may be even greater than the original damage. Therefore, to elucidate the economic cascading effect (ECE) in China caused by ADED and industrial linkage, this paper used the ARIO model to evaluate the total ECE and analyse the differences in the 19 sectors by taking the ADED under future annual mean temperature (AMT) increases of 1–5 °C as the input condition and adjusting the exogenous parameters to enhance the adaptability of the model; the goal was to assess the economic impact of climate change in China. The results show that (i) there is a significant amplifying impact of ECE due to industrial linkage under the influence of warming, ECE will be 4.24–5.25 times that of ADED, and the total economic impact (ADED + ECE) will exceed the GDP of the Chinese capital of Beijing in 2012 when the AMT increases by 1.5 °C, while it will exceed one tenth of China's GDP when it increases by 4 °C. (ii) Manufacturing will be the sector most affected by the industrial linkage under ADED, which accounts for 26.58% of total ECE, followed by agriculture (10.39%) and construction (8.5%). (iii) The decrease in cultivated land area will have a significant impact on the economy; when it is reduced to the minimum demand (1.41% less than 2012), ADED increases by 1.65 times and ECE increases by 2.35 times. This paper aims to provide a new perspective from which to evaluate the economic impact of climate change by studying ECEs behind sectoral interactions. [ABSTRACT FROM AUTHOR]

The potentially devastating impacts of climate change on crop production and food security are now widely acknowledged. An important component of efforts to mitigate these impacts is the production of new varieties of crops which will be able to thrive in more extreme and changeable environmental conditions. There is therefore an urgent need to find new sources of genetic diversity for crop improvement. Wild plant species closely related to crops (crop wild relatives) contain vital sources of such genes, yet these resources themselves are threatened by the effects of climate change, as well as by a range of other human-induced pressures and socio-economic changes. The flora of China comprises more than 20,000 native higher plant species, a proportion of which have known or potential value as gene donors for crop improvement. However, until now, the full range of these valuable crop wild relative species had not been identified. In this paper we present a methodology for creating a checklist of, and prioritizing China’s crop wild relatives, and reveal that 871 native species are related to crops that are of particularly high socio-economic importance in China—including rice, wheat, soybean, potato, sweet potato, millet and yam—crops which are also of notably high value for food and economic security in other parts of the world. Within this list we have identified species that are in particular need of conservation assessment based on their relative Red List status and potential for use in crop improvement programs. Endemic species that have particularly high economic value potential in China and that are under severe threat of genetic erosion and thus in need of urgent conservation action include wild relatives of tea ( Camellia fangchengensis S. Yun Liang et Y.C. Zhong and C. grandibracteata H.T. Chang et F.L. Yu), apple ( e.g ., Malus honanensis Rehder, M. ombrophila Hand.-Mazz. and M. toringoides (Rehder) Hughes), and pear ( Pyrus pseudopashia T.T. Yu). We provide recommendations for developing a systematic and comprehensive national CWR conservation strategy for China, highlighting the challenges and requirements of taking the strategy forward to the implementation phase. [ABSTRACT FROM AUTHOR]

The farming-pastoral region of North China is a region that has suffered from serious desertification, and the multiple transitional characteristics in this region make it very difficult to link the progress of desertification to the driving forces behind it. This paper mainly focuses on the dynamics of desertification over the past 10 years and their relationship to climate change and human activity. According to the MODIS images in 2000 and 2010, the farming-pastoral region had experienced significant desertification dynamics over the past 10 years. The area of regions that had undergone desertification reversion and expansion were 186,240 km 2 and 199,525 km 2 respectively, and the spatial distribution of these regions showed great heterogeneity. The relationship between desertification dynamics and their driving forces was investigated by comparing the change in the NPP that was induced by climate change and human activity for each pixel that experienced desertification reversion and expansion. From 2000 to 2010, the coupling of climate change and human activity was the dominant factor behind desertification reversion. However, the human activity was the dominant factor that controlled the desertification expansion process between 2000 and 2010. The driving processes of desertification also had considerable spatial heterogeneity, and the dominant factors behind desertification reversion and expansion in each sub-region were not completely the same. So, the scale effect must be considered when explaining the results from similar studies. [ABSTRACT FROM AUTHOR]