Sustainable Livelihoods in Rural Areas under the Shock of Climate Change: Evidence from China Labor-Force Dynamic Survey

Abstract

The threat of climate change to the sustainability of farmers’ livelihoods is becoming more significant. Research on the impact of climate change on the sustainability of farmers’ livelihoods could provide a scientific basis for enhancing farmers’ adaptability to climate change, reducing farmers’ livelihood vulnerability, and promoting the formulation of governmental adaptation strategies. Although studies have assessed the impact of climate change on the sustainability of farmers’ livelihoods, their analysis units have been aggregated. Therefore, this study was grouped based on geographical location (north and south regions), and then an additional grouping was conducted according to the internal economic factors of each region. Using data from China’s labor-force dynamic survey as our sample, this study measured the sustainable livelihood in agricultural households. This research provided a method to quantify the sustainability of farmers’ livelihoods based on measurements of poverty vulnerability. Additionally, using the annual average temperature as the core explanatory variable to describe climate change, this study evaluated the impact and heterogeneity of climate change on the sustainability of farmers’ livelihoods and replaced the annual average temperature with the normalized vegetation index to conduct a robustness test. The empirical study showed that the average annual temperature significantly decreased the sustainability of farmers’ livelihoods. The average annual temperature change had a greater impact on farmers in the southern provinces as compared to those in the north. Southern coastal regions, eastern coastal regions, the middle reaches of the Yangtze River, and the northeast regions were the key areas of concern. Finally, considering the current risk vulnerability of farmers, we concluded that crop breeding should be oriented to the trend of climate change, farmers’ risk prevention awareness should be increased, financial tools should be enhanced to mitigate the impact of meteorological disasters, an appropriate sustainability developmental evaluation index should be implemented, and the construction of agrometeorological disaster prevention and mitigation infrastructure should be advanced.

1. Introduction

Climate changes present huge challenges for humankind: global surface temperatures in 2011–2020 will be 1.09 °C warmer than in 1850–1900 (Climate Change 2021: Fundamentals of Natural Science). The global average temperature is likely to continue to rise over the next 20 years, exceeding the 1.5 °C target set in the Paris Agreement. Global warming significantly impacts human survival and development. Climate change will continue to strengthen the global water cycle, increasing the frequency of heavy rainfall, floods, and droughts. However, Asia is predicted to be hit the hardest. By 2030, as many as 758 million people around the world will experience once-in-a-century flood disasters. China may experience more flood events, and the proportion of China’s population affected by flood disasters will increase by 6% [1]. The annual mean temperature in China has increased by 0.24 °C from 1951 to 2019, with a significantly higher rate than the global average during the same period (Blue Book of China on Climate Change 2020). In addition, if emissions continue at their current rate, 10–45 million people could be exposed to extreme heat in the future. The probability of heavy rainfall, similar to the one-in-50-year event in 1980, will also rise rapidly, increasing 2–3 times by 2030 and 3–6 times by 2050 (Coping with Climate Change: China’s Response). Climate change had huge negative effects on population, resources availability and farmers [2]. In July 2021, Zhengzhou encountered a rare rainstorm, exceeding the extreme value of land precipitation in China, causing serious harm to agriculture, industry and transportation. Agricultural production is highly dependent on climate conditions, and climate change has become the main reason for the reduction in agricultural production [3,4,5]. As the issue of climate change becomes increasingly prominent, the negative impacts of climate changes cannot be ignored if we are to maintain the reduction of poverty in China on a large scale.

Climate change affects the sustainability of farmers’ livelihoods by impacting the two most important environmental factors, temperature and precipitation [6]. A high temperature heat wave, the high variability of rainfall, and the frequent extreme events caused by climate change increase the uncertainty in agricultural production, particularly small farmers of rain dependent agriculture [7]. As farmers’ living and development are reliant on their agricultural employment, any threats to their agricultural production seriously endangers their income stability and their livelihood sustainability [8]. Globally, the correlation between average global temperatures and climate as well as hydrological and meteorological disasters has increased significantly since the 1970s (as shown in Figure 1). Global warming will lead to more frequent extreme weather in the future, increased precipitation variability (more uneven precipitation), increased extreme drought and rainstorm weather [9], while the available water resources will be reduced (a large amount of water resources brought by extreme rainstorm/flood will be diverted and cannot be effectively utilized, and a large amount of groundwater needs to be extracted during extreme drought). However, farmers may not associate the frequency of extreme meteorological disasters with climate change, instead regarding them as accidental extreme events. Therefore, they may not realize their increased risk and be prepared to respond to potential threats [10,11,12].

There are two main types of risks caused by climate change: transition risk and physical risk [13,14]. Transition risk is also known as mitigation risk. As industries with high carbon emissions reduce and control their greenhouse gas emissions, they will experience a decline in risk [15,16]. Physical risk results from not addressing climate change effectively. Climate change will increase the frequency and intensity of extreme meteorological disasters, endangering life and health, reducing agricultural production efficiency [17,18,19], and harming food security [20,21,22,23,24].

Agricultural development faces both transition and physical risks. Methane from agricultural production is one of the main sources of global greenhouse gas emissions. Methane has become the second largest greenhouse gas after carbon dioxide, accounting for approximately one-fifth of global emissions, and methane’s warming potential is approximately 25 times that of carbon dioxide [25]. According to the Food and Agriculture Organization (FAO) of the United Nations, agricultural production and land use account for one-quarter of all carbon emissions from human activities [26]. Unlike other industries, agricultural production may neutralize greenhouse gases (e.g., agricultural ecosystems can neutralize 80% of greenhouse gas emissions from agricultural production) [27], and China’s crops neutralize approximately 2.28 billion tons of carbon dioxide every year. In view of this, the uncertainty of agricultural production mainly comes from physical risks. Physical risks caused by climate change affect agriculture in two ways: first, climate change has increased the frequency and intensity of extreme meteorological disasters, thus directly reducing agricultural production [28]. Second, climate change alters agricultural production conditions such as light resources, temperature, soil quality, and water access and availability [29], thus reducing the input–output efficiency of agriculture [30]. Empirical studies have shown that temperature and precipitation had a significant impact on the total-factor productivity of American agriculture, which can explain 70% of the total-factor productivity growth from 1981 to 2010 [4]. Increasing temperature have leads to a minimum reduction of 30% in major crops in the United States [3].

The existing literature provides a theoretical basis for assessing the impact of climate change on the sustainability of farmers’ livelihoods. However, due to the complexity of assessing the sustainability of farmers’ livelihoods, it has been difficult to quantitatively assess, and there is a lack of widely used quantitative methods. In addition, few theoretical and case studies have been published concerning how to identify people with sustainable livelihoods that are at risk as well as analyze their ability to cope with climate change, particularly within the context of various geographical and economic concerns. Under the impact of climate change, the uncertainty of farmers’ income will increase, and the range of income fluctuation will also expand. The objective of this paper was to review the relevant domestic and international research, to summarize the theoretical framework and evaluation methods suitable to evaluate the sustainability of farmers’ livelihoods, to select appropriate variables to describe climate change, and to provide references for the evaluation of the sustainability of farmers’ livelihoods while under climate shock. The contributions of this paper included three aspects: (1) combined with a quantitative method of expected poverty vulnerability, we estimated the probability that the per capita income of farmers would fall below the social poverty line and quantified the sustainability of farmers’ livelihoods; (2) empirically, the impact of climate change on the sustainability of farmers’ livelihoods was evaluated to determine whether climate change had a significant impact; and (3) further group regression was performed according to the various geographic and economic concerns to identify key areas for risk prevention and control. As a result, we were able to provide a sound basis for decision-making in the mitigation of adverse impacts of climate change on the sustainability of farmers’ livelihoods as well as for national strategies and initiatives.

This paper consists of three parts. Firstly, we describe the research design for measuring the sustainability of farmers’ livelihoods according to expected poverty vulnerability and the construction of an empirical model. Secondly, taking the standardized annual average temperature as the core explanatory variable and the data of China labor-force dynamic survey as the sample, the benchmark model was estimated, and the annual average temperature is replaced by the normalized vegetation index to test the robustness of the empirical research results. Thirdly, group regression was performed for the north and south regions, and the bootstrap method tested whether there were significant differences in the coefficients between the two groups. According to the north–south grouping, group regression was further conducted according to the regional economies to study the heterogeneity of climate change on the sustainability of farmers’ livelihoods and to test the robustness of the empirical research results after grouping. Finally, we summarize the research conclusions and provide corresponding policy suggestions.

2. Methods

The objective of this paper was to assess the impact of climate change on the sustainable livelihoods of farmers. The key aspects of the research design were selecting appropriate variables to describe livelihood sustainability and the degree of climate change as well as building a suitable empirical model.

2.1. Measures of Sustainable Livelihoods

Sustainable livelihoods are those that are stable in the face of change with the goal of poverty alleviation, with reference to a sustainable standard for farmers according to whether the per capita income of the individual or household were below the social poverty line. This determined the unsustainable state of farmers’ livelihood, that is, the continuous variable that was transformed into a 0–1 variable $I_{\left\{Y_{i,t}<Z\right\}}$ ($Y_{i,t}$ is the income level, $Z$ is the social poverty line). Compared with whether the income is lower than the social poverty line, the probability that the income is lower than the social poverty line ($\mathrm{Pr}\left(Y_{i,t}<Z\right)$) can better describe the unsustainable degree of the farmers. Similar to the connotation of poverty vulnerability, poverty vulnerability focuses on the possibility of an individual or family falling into poverty in the future, so it can continue the measurement idea of vulnerability to quantify the unsustainable livelihood of farmers. Vulnerability as expected poverty (VEP) is a common method for measuring poverty vulnerability [31,32]. This paper also used this method to estimate the probability of farmers being impoverished in the future, as shown in Formula (1):

$$V_{it}=\mathrm{Pr}\left(Y_{i,t+1}\le Z\right),$$

(1)

where $V_{it}$ represents the vulnerability of the ith peasant household in period t, and $Y_{i,t+1}$ represents the welfare level of the ith peasant household in period t + 1. In the context of measuring the probability of farmers falling into poverty in the future, $Y_{i,t+1}$ represents the income level of farmers, $Z$ represents the poverty line, and $\mathrm{Pr}(\cdot )$ represents the probability value. The income level of a farmer is determined by the individual characteristics of the farmer, the characteristic variables of the community where the farmer lives, and climatic conditions. If the per capita income of the farmer is used to measure the welfare level of the farmer, and it is assumed that the per capita income of the farmer obeys the log-normal distribution, then the farmer’s welfare decision function could be expressed as:

$$\ln Y_{i,t}={\beta }_1{X}_{i,t}+{\beta }_2{M}_{i,t}+{\beta }_3{C}_{i,t}+{\epsilon }_{i,t},$$

(2)

where $X_{i,t}$ represents the characteristic vector of the individual farmer, $M_{i,t}$ represents the characteristic vector of the community where the farmer lives, and $C_{i,t}$ represents the climate variable of the region where the farmer lives. Considering that the model may have heteroscedasticity, a feasible generalized least squares (FGLS) estimation, Formula (2), could be adopted, assuming that the variance of the random disturbance term ${\xi }_{it}$ is

$${\sigma }_{\xi ,it}^2={\theta }_1{X}_{i,t}+{\theta }_2{M}_{i,t}+{\theta }_3{C}_{i,t}$$

(3)

According to the feasible process of generalized least squares method, the following equation could be obtained:

$$E\left(\ln Y_{i,t}|X_{i,t},M_{i,t},C_{i,t}\right)={\widehat{\beta }}_1{X}_{i,t}+{\widehat{\beta }}_2{M}_{i,t}+{\widehat{\beta }}_3{C}_{i,t}$$

(4)

$$Var\left(\ln Y_{i,t}|X_{i,t},M_{i,t},C_{i,t}\right)={\widehat{\sigma }}_{\xi ,it}^2={\theta }_1{X}_{i,t}+{\theta }_2{M}_{i,t}+{\theta }_3{C}_{i,t}$$

(5)

Since income $Y_{i,t}$ is assumed to be a logarithmic income distribution, combined with the estimated results of Equations (4) and (5), the estimated value of vulnerability was:

$$\begin{array}{ll}{V}_{it}& =\mathrm{Pr}\left(Y_{i,t}\le Z|X_{i,t},M_{i,t},C_{i,t}\right)\\ & =\mathsf{\Phi }\left(\frac{\ln Z-\ln Y_{i,t}}{\sqrt{{\widehat{\sigma }}_{\xi ,it}^2}}\right)\\ & =\mathsf{\Phi }\left(\frac{\ln Z-{\widehat{\beta }}_1{X}_{i,t}+{\widehat{\beta }}_2{M}_{i,t}+{\widehat{\beta }}_3{C}_{i,t}}{\sqrt{{\theta }_1{X}_{i,t}+{\theta }_2{M}_{i,t}+{\theta }_3{C}_{i,t}}}\right)\end{array}$$

(6)

where $\mathsf{\Phi }(\cdot )$ represents the cumulative distribution function of the standard normal distribution, and probability $V_{it}$ represents the estimate of unsustainability. Unsustainability estimation depends on the poverty line, and this paper intends to use the social poverty line ($L_{Social}$) provided by the World Bank to set the poverty line. The social poverty line = Max {USD 1.90, USD 1.0, + 0.5 × median income (or consumption)} and the lower limit of the social poverty line was the extreme poverty line of USD 1.90 (2011 PPP). Since the poverty line is denominated in US dollars, it is necessary to convert dollars into renminbi. The “Big MAC Index” is used as the conversion index, considering that the consumption of low-income groups is mainly daily consumption (as shown in www.economist.com/big-mac-index, 2 February 2022). Taking the years 2011, 2013, 2015, and 2017 as examples, the poverty lines were CNY 4407.41, 5522.72, 6458.87, and 7340.85, respectively.

2.2. Variable Selection and Empirical Model Construction

2.2.1. Variable Selection and Assignment

This paper takes the data of the China Labor-Force Dynamic Survey (CLDS) (the CLDS database comes from The China Social Science Survey Platform, which is the first national tracking survey focusing on labor force in China) as the sample, which includes the survey data from 2012, 2014, 2016, and 2018, while the corresponding survey years were 2011, 2013, 2015, and 2017. As mentioned above, in the estimation of sustainability, the dependent variable is per capita household income; the explanatory variables include the individual characteristic variables of farmers, the characteristic variables of the communities they live in, and the climate variables of the geographical units they live in. Among them, this paper first uses the average temperature from 2000 to 2019 as the benchmark, and then describes the climate variables by investigating the fluctuation degree of temperature during this period. The specific indicators and assignment methods are shown in

Table 1. Definition of variables.

Category	Variable Symbol	Assignment Method
Family characteristic variables	lnIncome	ln(Total household income/number of household members + 1)
	Size	Number of family members
	Age18	Number of family members younger than 18
	Age60	Number of family members older than 60
	Edu	Average education level of labor force (out-of-work and non-school household members)
	NotHealth	The number of family members with self-rated health as relatively unhealthy or very unhealthy
	Capital	Original value of tractors, large farm implements (such as harvesters, rice transplanters, seeders, large combine harvesters)
	Agr	The proportion of income from agriculture, forestry, animal husbandry, sideline, and fishery in total income
	Internet	It is represented as 1 when using the Internet at home, otherwise it is 0
Community characteristic variables	Library	It is represented as 1 if there is a library (room) within the administrative division of the village, otherwise it is 0
	Clinic	It is represented as 1 if there is a clinic or hospital within the village administrative division, otherwise it is 0
	Bank	It is represented as 1 if there is a rural credit cooperative within the scope of the village administrative division, otherwise it is 0
	Bus	It is represented as 1 if there is a bus stop within the administrative division of the village, otherwise it is 0
	BankNum	Number of banks and financial institutions in prefecture-level cities/area of prefecture-level cities (km2)
	lnDistant1	Distance from the village to the nearest county/district government (km)
	lnDistant2	Distance from the village to the nearest town/street (km)
	Terrain	The terrain where the farmer is located, Plain, Hills, Mountains
Climate variables	Temp	(annual average temperature of the city where the farmer is located—perennial temperature value)/standard deviation of annual average temperature
	Prec	(annual precipitation of the city where the farmer is located—perennial precipitation value)/standard deviation of precipitation
	NDVI	(annual average NDVI of the city where the farmer is located—perennial NDVI value)/standard deviation of NDVI

.

Climate change was the core variable. Our intention was to characterize climate change in two ways. The first was to use the average annual temperature as the core index to quantify climate change, and then apply the standardized annual average temperature (the degree to which the annual average temperature deviated from the annual value) mean as the index to describe the degree of climate change: “(annual average temperature of the city where the farmer was located—perennial temperature value)/standard deviation of annual average temperature”. The sample data in this paper were from 2011–2017. According to the regulations of the World Meteorological Organization, it was necessary to calculate the perennial temperature value (mean) and the standard deviation of temperature based on the meteorological data from 1981 to 2010. Both temperature and precipitation affected agricultural production, so in addition to temperature as an explanatory variable, precipitation should also be used as an explanatory variable, as well as to describe an abnormal degree of precipitation. The temperature and precipitation data were sourced from the National Meteorological Science Data-Sharing Service, “China’s Surface Climate Data Daily Value Dataset (V3.0)”.

The second way to describe climate change was to use the normalized difference vegetation index (NDVI) as an indicator to quantify climate change (Xu Xinliang, China Annual vegetation index (NDVI)) spatial distribution data set. The data registration and publishing system of the resource and environmental science data center of the Chinese Academy of Sciences (http://www.resdc.cn/DOI, accessed on 6 June 2018) was used. Vegetation has been used as an “indicator” in global change research. Dynamic monitoring of vegetation has indicated trends in climate changes [33,34]. The NDVI reflects the proportion of effective radiation absorbed by vegetation. It is sensitive to the growth of vegetation and represents the changes in surface vegetation cover [35]. The NDVI index has often been used to estimate land cover area, vegetation photosynthesis capacity, vegetation biomass, regional evapotranspiration, soil water content and drought disaster [36]. Many studies have shown that the NDVI was significantly correlated with temperature and precipitation [37,38,39,40]. The NDVI contains both temperature and precipitation information, which has been used to describe the degree of climate change [41], and NDVI index better reflected the nonlinear and non-stationary complex change trends of certain factors [42]. Figure 2 shows the correlation between standardized annual temperature, standardized annual precipitation and the NDVI index from 2011 to 2019. There was a significant positive correlation among the standardized annual temperature, the standardized annual precipitation and the NDVI index. Therefore, the degree to which the NDVI was higher than the perennial value was used as an index to quantify the degree of climate change, that is: “(NDVI—perennial NDVI value)/standard deviation of NDVI”. Since the NDVI index could only be traced back to 1998, the perennial value and standard deviation were calculated based on the NDVI index from 1998 to 2010. According to the steps given in Equations (1)–(6), combined with the sample data, the estimated value of unsustainable livelihoods (Vsocial) was obtained. The descriptive statistical results for each of the variables are shown in

Table 2. Descriptive statistics of variables.

Variable	Observations	Mean Value	Standard Deviation	Max	Min
lnIncome	19,365	8.3919	1.6824	0	13.7431
VSocial	19,365	0.5343	0.4283	0	1
Size	19,365	4.4244	2.1137	1	22
Age18	19,365	0.8695	1.0063	0	10
Age60	19,365	0.7973	0.8903	0	6
Edu	19,365	2.7904	1.2171	1	10
NotHealth	19,365	0.5423	0.8609	0	8
Capital	19,365	1.0409	2.8903	0	15.7614
Agr	19,365	0.3523	0.4176	0	1.0007
Internet	19,365	0.4033	0.4906	0	1
Library	19,365	0.7897	0.4076	0	1
Clinic	19,365	0.8663	0.3403	0	1
Bank	19,365	0.1649	0.3711	0	1
Bus	19,365	0.3613	0.4804	0	1
BankNum	19,365	0.0982	0.1038	0.0031	0.6614
lnDistant1	19,365	2.9216	0.8698	0	4.7958
lnDistant2	19,365	1.6321	0.7403	0	6.2166
Plain	19,365	0.4873	0.4999	0	1
Hills	19,365	0.2661	0.4419	0	1
Mountain	19,365	0.2466	0.4310	0	1
Temp	19,365	0.4848	0.9371	−2.5013	2.8117
Prec	19,365	−0.0758	1.0681	−4.4303	2.4239
NDVI	19,365	1.0188	1.1272	−4.3839	4.7108

:

2.2.2. Empirical Model

The benchmark model for empirical research is shown in Formula (7):

$$V_{i,t}={\gamma }_{1}Tem{p}_{i,t}+{\gamma }_2{K}_{i,t}+{\varsigma }_{i,t}$$

(7)

where $V_{it}$ represents the unsustainable livelihoods of farmers; $Tem{p}_{i,t}$ represents the index for quantifying the degree of abnormal temperatures, which was also the core explanatory variable of the benchmark model; $K_{i,t}$ is the control variable, including the precipitation abnormal index, family characteristic variables and community characteristic variables, as shown in Table 1. At present, climate warming is the core issue of climate change. In the existing literature, the average annual temperature was the main index to measure climate warming. Therefore, this paper used the average annual temperature of a prefecture-level city, in which farming would be a common trade, as the quantitative index for the degree of local climate change. For agriculture, temperature and precipitation were the main indicators affecting agricultural production, but the abnormal precipitation was also a result of climate warming to a great extent. If the temperature anomaly index and precipitation index were simultaneously used as explanatory variables, it would not only affect the significance test of variables but also make it difficult to distinguish the individual impacts of each explanatory variable on the explained variable. In view of this, this paper used a method reported by O’Donnell et al. [43]. to separate the temperature anomaly information from the precipitation anomaly, that is, the estimated value ${\widehat{e}}_{i,t}$ of the random disturbance term in the fixed effect model $Tem{p}_{i,t}=\mathrm{c}+\alpha \mathrm{Pr}e{c}_{i,t}+e_{i,t}$ was used as the control variable, rather than directly taking the precipitation anomaly index as the ($\mathrm{Pr}e{c}_{i,t}$) control variable. For farmers, climate variables were strictly exogenous, and the exogenous nature of the variables provided convenience for the estimation of the model. First, it was important to take into account the differences in production patterns, rural infrastructures, and climatic conditions for farmers among districts and counties, prefecture-level cities, provinces, economic regions, as well as between the north and the south. Therefore, in the process of estimation, the clustering standard deviation was determined according to the village committee, district and county, prefecture-level city, and province to ensure the robustness of the estimated results. Furthermore, through grouping estimation, comparative analysis was made to determine whether the impact of climate change would be heterogeneous in different regions.

3. Results

3.1. Estimation of the Benchmark Model

According to the estimation process of the panel data model, the model type had to be determined first. “A mountain has four seasons and ten miles of different days”. Therefore, within prefecture-level cities, the microclimate between districts and counties and between village committees would be inconsistent, and the soil production capacity, irrigation conditions and lighting conditions would also vary. Therefore, in this study, we estimated the benchmark model using fixed effects [44,45]. Moreover, the Hausmann test showed that fixed-effects models were better suited than random effects models. The specific estimation results are shown in

Table 3. Estimated results of the benchmark model.

Variable	(1)	(2)	(3)	(4)
	VSocial	VSocial	VSocial	VSocial
Temp	0.0328 ***	0.0190 ***
	(0.0032)	(0.0029)
NDVI			0.0266 ***	0.0183 ***
			(0.0050)	(0.0040)
${\widehat{e}}_{i,t}$	0.0165 ***	0.0110 ***
	(0.0029)	(0.0025)
Size	0.0496 ***	0.0560 ***	0.0528 ***	0.0579 ***
	(0.0028)	(0.0037)	(0.0027)	(0.0036)
Age18	−0.0020	−0.0077	−0.0042	−0.0086 *
	(0.0038)	(0.0050)	(0.0038)	(0.0051)
Age60	0.0595 ***	0.0411 ***	0.0633 ***	0.0428 ***
	(0.0046)	(0.0067)	(0.0046)	(0.0066)
Edu	−0.0108 ***	0.0075 ***	−0.0083 ***	0.0088 ***
	(0.0024)	(0.0026)	(0.0023)	(0.0025)
NotHealth	0.0547 ***	0.0442 ***	0.0554 ***	0.0446 ***
	(0.0027)	(0.0028)	(0.0026)	(0.0029)
Capital	−0.0084 ***	−0.0067 ***	−0.0092 ***	−0.0072 ***
	(0.0008)	(0.0008)	(0.0008)	(0.0008)
Agr	0.1531 ***	0.1301 ***	0.1513 ***	0.1285 ***
	(0.0065)	(0.0064)	(0.0063)	(0.0062)
Internet	−0.0652 ***	−0.0337 ***	−0.0551 ***	−0.0288 ***
	(0.0046)	(0.0045)	(0.0047)	(0.0044)
Library	−0.0262 ***	−0.0260 ***	−0.0270 ***	−0.0259 ***
	(0.0087)	(0.0082)	(0.0087)	(0.0082)
Clinic	−0.0262 ***	−0.0189 **	−0.0257 ***	−0.0183 **
	(0.0092)	(0.0087)	(0.0097)	(0.0090)
Bank	−0.0529 ***	−0.0412 ***	−0.0533 ***	−0.0400 ***
	(0.0103)	(0.0109)	(0.0109)	(0.0107)
Bus	−0.0335 ***	−0.0253 ***	−0.0296 ***	−0.0220 ***
	(0.0066)	(0.0060)	(0.0066)	(0.0061)
BankNum	−0.3835 ***	0.4540 *	−0.3515 ***	0.6473 ***
	(0.0633)	(0.2441)	(0.0617)	(0.2389)
lnDistant1	−0.0103	−0.0225 ***	−0.0108 *	−0.0239 ***
	(0.0063)	(0.0071)	(0.0062)	(0.0068)
lnDistant2	−0.0087	−0.0087 *	−0.0039	−0.0030
	(0.0071)	(0.0048)	(0.0070)	(0.0049)
Hills	0.0492 ***	0.0398 ***	0.0515 ***	0.0412 ***
	(0.0098)	(0.0096)	(0.0092)	(0.0089)
Mountain	0.0251 *	−0.0168	0.0302 **	−0.0132
	(0.0138)	(0.0137)	(0.0137)	(0.0131)
Constant	0.3295 ***	0.2319 ***	0.2935 ***	0.1900 ***
	(0.0250)	(0.0290)	(0.0269)	(0.0291)
Observations	19,365	19,365	19,365	19,365
R2		0.4587		0.4560
Objects	10,505	10,505	10,505	10,505
Model	Random effects	Fixed effects	Random effects	Fixed effects

Note: Standard errors in parentheses, *** p < 0.01, ** p < 0.05, * p < 0.1.

.

According to the estimation results of the fixed effect, at the 1% confidence level, the fluctuation range of the annual average temperature had a significant impact on the sustainable livelihoods of farmers. Specifically, with reference to the perennial temperature values and perennial precipitation values from 1981 to 2010, if the annual temperature was higher than its perennial value by one standard deviation, the sustainability of farmers’ livelihoods would decrease by 1.90%. Climate warming would significantly decrease the sustainability of farmers’ livelihoods.

In the control variables, at the 1% confidence level, the abnormal values of the annual precipitation stripped of temperature fluctuation had a significant impact on the sustainability of farmers’ livelihoods. If it was a single standard deviation higher than the perennial value, the sustainability of farmers’ livelihoods would decrease by 1.10%. Regarding the family characteristic variables, at the 1% confidence level, family size, the number of elderly people, the average education level of the working population, the number of unhealthy people, productive fixed assets, income structure and whether the family accessed the Internet had a significant impact on the sustainable livelihood of farmers. With regard to the community characteristics variables, at the 1% confidence level, whether there were libraries, rural credit cooperatives or commercial banks, bus stops, the distance from government offices, the distance from the township or street, and the terrain within the administrative division of the village, all had a significant impact on the sustainable livelihoods of farmers. At the 5% confidence level, whether there was a clinic within the administrative division of the village where the farmer was located also had a significant impact on sustainable livelihood of farmers. Perfect infrastructure and public services could reduce the risk factors for farmers, which would also have an impact on the sustainability of farmers’ livelihoods and alleviate the impact of climate change to a great extent.

Based on the benchmark model, 1 km NDVI was further used to replace the annual average temperature and the annual precipitation to test the robustness of the estimation results. The estimation results are shown in Table 3. According to the estimation results of the fixed-effects model, at the 1% confidence level, the fluctuation range of the NDVI had a significant impact on the sustainability of farmers’ livelihoods. Using the perennial NDVI values (1998–2010) as a reference, if the NDVI index was higher than its perennial value by one standard deviation, the sustainability would decrease by 1.83%, and the estimation results were highly consistent with the estimation results of the benchmark model. We could therefore conclude that the estimation results of the benchmark model had good robustness.

3.2. Group Regression

3.2.1. Grouped by North and South

Due to China’s vast territory, agricultural production methods and crops varied from region to region. In terms of agricultural production—“the Qinling–Huaihe River”—is the dividing line between dryland and paddy agriculture, rice production and wheat production. Geographically, it is customary to divide China into north and south by “the Qinling–Huaihe River” (in

Table 4. Grouped regression by north–south.

Variables	(1)	(2)	(3)	(4)	(5)	(6)
	VSocial			VSocial
	I Northern	II Southern	Coefficient Difference	I Northern	II Southern	Coefficient Difference
Temp	0.0146 ***	0.0236 ***	−0.0090 ***
	(0.0024)	(0.0020)
NDVI				0.0072 ***	0.0254 ***	−0.0182 ***
				(0.0025)	(0.0029)
Control variables	√	√	√	√	√	√
Observations	8300	11,065		8300	11,065
R2 Objects	0.5008 4288	0.4472 6217		0.4987 4288	0.4377 6217

Note: Standard errors in parentheses, *** p < 0.01; the sampling number of the Bootstrap method was 500 times; the estimated results of control variables are omitted from the table; the north is expressed as I Northern and the south is expressed as II Southern.

, the north is expressed as I Northern and the south is expressed as II Southern). The southern region included Jiangsu, Anhui, Hubei, Chongqing, Sichuan, Tibet, Yunnan, Guizhou, Hunan, Jiangxi, Guangxi, Guangdong, Fujian, Zhejiang, Shanghai, Hainan, Hong Kong, Macao, and Taiwan, while the northern region includes Xinjiang, Gansu, Qinghai, Shaanxi, Ningxia, Inner Mongolia, Shandong, Henan, Shanxi, Hebei, Beijing, Tianjin, Liaoning, Jilin, and Heilongjiang. Firstly, the group regression was performed according to the north and south regions. On the basis of the group regression, the bootstrap method was used to test whether there was a significant difference in the coefficients between the two groups. The coefficient estimates and the difference test results are shown in Table 4.

According to the estimation results of the grouping regression, at a confidence level of 1%, the ratio of annual average temperature to annual precipitation (when higher than its perennial values) was significant to the sustainability of farmers’ livelihoods. For southern provinces with high accumulated temperatures and abundant precipitation, the sustainability of farmers’ livelihoods would decrease more under the impact of annual average temperature increases. As compared to the historical average of the average annual temperature, if the average annual temperature was higher than its standard deviation, the sustainability in the north would decrease by 1.46% while for farmers in the south, their sustainability would decrease by 2.36%, with a difference of 0.9%. Therefore, as compared to farmers in the north, climate warming had a greater negative impact on the livelihood sustainability of farmers in the south.

Furthermore, the 1 km NDVI was used as the core explanatory variable to test the robustness of the grouped regression results. The estimated results are shown in Table 4. For farmers in the northern provinces, if NDVI was a single standard deviation higher than its annual value, sustainability would decrease by 0.72% while for farmers in southern provinces, their sustainability would decrease by 2.54%, with a difference of 1.82%. The test results, based on a bootstrap method, showed that at the 1% confidence level, the difference between the coefficient estimates was significantly not zero. As compared to farmers in northern provinces, the sustainability of farmers’ livelihoods in southern provinces was more sensitive to changes in the NDVI, which verified the robustness of grouped regression results.

3.2.2. Grouped by Economic Region

In addition to the difference between the north and the south, there were also significant differences in precipitation and temperature. The northern coastal regions and the northeast regions are adjacent to the sea, and the climate is relatively humid, while the climate of the northwest provinces is dry. Therefore, it was necessary to further perform a grouping regression based on north–south grouping. According to the grouped method of economic regions by the National Bureau of Statistics, the northern region could be divided into the great northwest region, the middle reaches of the Yellow River, the northern coastal region, and the northeast region (expressed as I-I, I-II, I-III, and I-IV, respectively, in

Table 5. Grouped regression by economic region grouping.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
	I Northern				II Southern
	I-I	I-II	I-III	I-IV	II-I	II-II	II-III	II-IV
	VSocial	VSocial	VSocial	VSocial	VSocial	VSocial	VSocial	VSocial
Temp	−0.0072	−0.0006	0.0321 ***	0.0732 ***	0.0159 ***	0.0404 ***	0.0880 ***	0.1008 ***
	(0.0070)	(0.0042)	(0.0063)	(0.0122)	(0.0029)	(0.0068)	(0.0241)	(0.0113)
Control variables	√	√	√	√	√	√	√	√
Observations	1604	2820	2333	1543	3099	2827	1527	3612
R2	0.5687	0.4375	0.5984	0.5438	0.4585	0.5610	0.4704	0.4244
Objects	784	1446	1317	741	1631	1514	979	2093
NDVI	0.0094 *	0.0066 *	0.0178 ***	0.0113	0.0279 ***	0.0537 ***	0.0153	0.0136 ***
	(0.0055)	(0.0035)	(0.0062)	(0.0147)	(0.0050)	(0.0102)	(0.0131)	(0.0051)
Control variables	√	√	√	√	√	√	√	√
Observations	1604	2820	2333	1543	3099	2827	1527	3612
R2	0.5655	0.4373	0.5961	0.5184	0.4588	0.5458	0.4535	0.3921
Objects	784	1446	1317	741	1631	1514	979	2093

Note: Standard errors in parentheses, *** p < 0.01, * p < 0.1; the estimated results of control variables are omitted from the table. The great northwest region is expressed as I-I, the middle reaches of the Yellow River as I-II, the northern coastal regions as I-III, and the northeast regions as I-IV, while the southwest region is expressed as II-I, the middle reaches of the Yangtze River as II-II, the southern coastal regions as II-III, and the eastern coastal regions as II-IV.

), while the southern region could be divided into the southwest region, the middle reaches of the Yangtze River, the southern coastal region, and the eastern coastal region (great northwest region: Tibet, Qinghai, Xinjiang, Ningxia; middle reaches of the Yellow River: Inner Mongolia, Shaanxi, Shanxi, Henan; north coastal region: Shandong, Hebei, Tianjin, Beijing; northeast region: Liaoning, Jilin, Heilongjiang; southwest region: Guangxi, Chongqing, Sichuan, Yunnan, Guizhou; middle reaches of the Yangtze River: Anhui, Hubei, Hunan, Jiangxi; southern coastal region: Guangdong, Fujian, Hainan; eastern coastal region: Jiangsu, Zhejiang, Shanghai; expressed as II-I, II-II, II-III, and II-IV, respectively, in Table 5). First, the average annual temperature was used as the core variable to measure climate change for grouping regression, and the estimated results are shown in Table 5.

As shown in Table 5, at a confidence level of 1%, the average annual temperature fluctuation significantly reduced the sustainability of farmers’ livelihoods in six regions, except in the northwest and the middle reaches of the Yellow River. As compared to the grouping estimation results in the north, for farmers in the northeast, their livelihood sustainability was the most sensitive to temperature fluctuations, followed by farmers in the northern coastal provinces. According to the grouping estimation results of the southern region, at the 1% confidence level, for the four regions of the southern province, the average annual temperature fluctuation significantly reduced the livelihood sustainability of farmers. The estimated value of the comparison coefficient shows that for farmers in the eastern coastal areas, their livelihood sustainability was the most sensitive to temperature fluctuations, followed by the southern coastal provinces, the middle reaches of the Yangtze River, and the southwest.

Secondly, using 1 km NDVI as the core explanatory variable, the estimation results are shown in Table 5. In the four northern regions, at the 1% confidence level, the fluctuation of the NDVI only significantly affected the farmers in the northern coastal areas, and the other three regions were not significant. In the four regions of southern provinces, at the 1% confidence level, the fluctuation of NDVI was not only significant in the southern coastal area, but also in the other three regions.

The estimation results of the comprehensive grouping regression showed that under the impact of climate warming, the eastern coast, the southern coast, the middle reaches of the Yangtze River, and the northeast region were the key areas to alleviate relative poverty and prevent a large-scale return to poverty.

4. Conclusions and Policy Implications

The impact of climate change on agriculture is very prominent, but the negative impact of climate is not paid enough attention, and it is still regarded as an accident. In fact, under the background of increasingly warming climate, extreme meteorological disasters are not so accidental. If the trend of climate change is not fundamentally reversed, climate will be the main exogenous factor affecting the income level of farmers and their sustainable livelihoods. In view of this, this paper examined climate change as a shock variable to assess its impact on the sustainability of farmers’ livelihoods. The main conclusions were as follows: (1) climate warming and precipitation fluctuations significantly decreased sustainability of farmers’ livelihoods; (2) as compared to farmers in the northern region, there was a greater impact on farmers in the southern region due to annual mean temperature changes; and (3) the grouped regression showed that the east coast, the south coast, the middle reaches of the Yangtze River, and the northeast region were the key regions that would require assistance under the impact of climate change.

The above conclusions have important policy implications. In view of the innate vulnerability of agricultural production to risk, China should also consider how to increase the sustainability of farmers’ livelihoods by focusing on reduction strategies for impact events. In future poverty reduction strategies and policies, the focus should include increasing farmers’ income and improving livelihood sustainability.

Our research showed that the impacts of climate change on agricultural communities could be mitigated according to following four aspects:

First, crop breeding should be oriented to the trend of climate change, and varieties should be developed with stronger drought, flood, and pest resistance. In key areas of concern, such as the eastern coast, the southern coast, the middle reaches of the Yangtze River and the northeast, we should strengthen the promotion of crops with drought resistance, waterlogging resistance, and disease and insect resistance, and institutionalize agricultural adaptation policies for different climates in vulnerable areas. (i): Select stress resistant varieties with good quality, high temperature resistance, drought and flood resistance and disease and insect resistance in key areas, so as to make full use of natural resources and improve the ability to resist adverse environmental impacts. The selection of seeds should be based on the idea that stable yield is more important than high yield. The degree of Fusarium wilt is different in different plots, and the varieties selected are different in different climates. There is a certain negative correlation between disease resistance and early maturity and high yield. In areas with low accumulated temperature and dry and yellow plots, varieties with good disease resistance should be selected, while in areas with high accumulated temperature, varieties with short growth period and great growth potential can be selected. In order to test the quality and adaptability of seeds, it is suggested that farmers can do germination rate test before sowing, check the germination rate and uniformity of seeds, and estimate the amount of sowing in the next year; (ii): Pest identification method based on infrared sensor, audio sensor and image classification to monitor the change trend of crop diseases, insects, grass pests and livestock and poultry diseases in key areas [46]. At present, the use of pheromone traps and manual inspection is the most widely and effectively used technology. For example, the use of entomopatho-genic nematodes in green spaces in Spain has successfully controlled a variety of pests. In addition, the overall efficiency and speed of detection can be improved by using promising automatic detection technologies such as thermal camera and acoustic detector [47]; (iii): Adhere to the method of prevention first and comprehensive prevention and control and carry out effective treatment in combination with chemical prevention and control, physical prevention and control, biological prevention and control and other means. There outbreak of American white moth in 2021 proves that there are some loopholes in our past prevention and control, and the use of Chou’s rodent wasp, the natural enemy of American white moth pupa, is one of the effective measures to control American white moth. Therefore, in order to optimize the means of crop safety control, we should not only optimize chemical control and physical control, but also vigorously advocate biological control, because biological control has the advantages of economy, safety, usefulness and low pollution. Specifically, to improve the technical safety of crop disease and pest control and better carry out crop disease and pest control by means of insect control, bacteria control, virus control and biological agents, we must improve the yield and quality of crops [48].

Second, we must improve farmers’ awareness of risk prevention. We should popularize the knowledge of disaster prevention and reduction, prepare materials for disaster prevention and reduction, do a good job in the pre disaster management of crops and livestock, and respond to the impact of meteorological disasters scientifically and reasonably. (i): The government and farmers should work together to strengthen the climate risk management and condition system in rural areas. The government should strengthen the early warning of natural disasters, smooth the “last mile” of climate information dissemination, and integrate the risk of climate change into the Rural Revitalization Strategy. Farmers should be good at using their own knowledge, resources and net-works to adapt to climate change, and deal with the impact of climate fluctuations through planting structure adjustment, wild resource utilization, diversified management methods and sharing risks with informal institutions [49]. When a single rural family group cannot adapt, the government or other external forces must help them plan and implement corresponding adaptation and bottom-up measures; (ii): Increase education, training and publicity on climate risk prevention. Strengthening climate change education is the inevitable choice to deal with the current climate change risks. It is necessary to strengthen the skills training of new professional farmers to deal with climate risks, including the current situation, impact, adaptation and mitigation measures, international situation and other aspects of climate change. At the same time, it is also necessary to use participatory methods to encourage farmers to improve their prevention awareness and practical ability through online and offline climate classroom education and practical activities, and let farmers obtain real measures to deal with climate change. We should increase publicity efforts to create a good climate risk prevention atmosphere, encourage “professional farmers”, “soil experts” and “large growers” to teach good practices and experience in climate risk prevention, strengthen popular science publicity such as township meteorological information service platform, meteorological electronic display screen, meteorological early warning horn and network promotion, and give full play to the role of meteorological observatory (station) popular science education base. Furthermore, we need to improve its popular science education and publicity function. According to the types of local meteorological disasters and the impact of climate change, special exhibitions on disaster prevention and mitigation and climate change should be held, and publicity activities for disaster prevention and mitigation such as emergency drills should be carried out [50]. We need to widely mobilize science popularization volunteers, cultivate a meteorological science popularization publicity team combining professional scientific and technological workers and science popularization volunteers, spread the scientific knowledge of meteorological disaster prevention and mitigation and climate change to thousands of households and penetrate into the daily life of the public. We need to minimize the losses caused by meteorological disasters and improve the public participation in dealing with climate change; (iii): Farmers should be encouraged to use “climate smart agriculture” (CSA) to actively obtain climate impact information and its countermeasures. CSA can help farmers build their ability to cope with climate change and find ways to adapt to climate change. Countries all over the world have promoted the use of climate intelligent agriculture. In August 2017, the people’s Government of Balinzuo banner in Inner Mongolia and the nature conservation association signed a cooperation framework agreement on jointly exploring climate intelligent agriculture projects, and jointly committed to establishing a climate intelligent agriculture demonstration area suitable for arid and semiarid areas in Inner Mongolia. In September 2021, the U.S. Department of agriculture announced a new plan to fund climate smart agriculture practices to help sell CSA commodities. Therefore, we should vigorously promote climate smart policies, support a series of pilot projects, encourage the implementation of climate smart protection measures on cultivated land through technology introduction and mechanism innovation, and explore and practice the development model of climate smart agriculture [51,52]. The government subsidizes the purchase and maintenance costs of climate smart equipment, guides scientific research institutions, leads enterprises and social organizations to participate together, ensures the long-term and effectiveness of the use of equipment, makes farmers aware of the importance of climate smart agriculture, and provides soil for the application of relevant scientific and technological achievements.

Third, the impact of meteorological disasters should be mitigated through financial instruments. On the one hand, we should develop more insurance products to meet the differentiated needs of farmers, build a multi-level agricultural insurance system according to local conditions, improve the coverage of agricultural and catastrophe insurance, reduce the rate, and increase the compensation rate. We should promote insurance institutions to accelerate the establishment of new concepts and models of risk assessment, integrate climate change risk into their governance system, risk management system, self-risk and solvency assessment (ORSA), and make good business development planning. We should not ignore the long-term impact of climate change on farmers’ production and life based on the existing underwriting business and reinsurance arrangements. Insurance institutions are encouraged to strengthen the management of climate change risks in ORSA, continuously track and analyze the major climate change risk exposures faced by insurance institutions, conduct risk assessment on major climate change risk exposures, and formulate corresponding risk mitigation and response solutions. We should strengthen the selection and analysis of agricultural indicators and parameters related to climate change risk, formulate a set of standardized climate change risk stress test parameter system, and gradually improve the corresponding parameter index system according to the development trend of climate change risk, so as to improve the accuracy, comprehensiveness and consistency of risk analysis and assessment. We should support regulators to study and formulate policies, systems, supporting mechanisms and action plans that help reduce physical risks and transformation risks according to agricultural climate change risk assessment, so as to promote agriculture to gradually improve its ability to deal with climate change risks. We should speed up the implementation and full deployment of agro-meteorological index insurance, increase the premium subsidy scope of agricultural insurance in key regions, and strengthen the construction of grass-roots front-line teams of agricultural insurance [53]. For example, in June 2021, the Yongdeng branch of PICC Property and the casualty insurance Lanzhou Branch successfully issued the first order of pea meteorological index insurance in Wushengyi Town, Yongdeng County, underwriting 15,000 mu of peas and realizing the signing premium of 405,000 yuan, which provided about 9 million yuan of risk guarantee for 922 farmers and 10 farmers’ professional cooperatives in three townships of Yongdeng County. Due to the severe drought in 2020, pea triggered a claim of 1.1349 million yuan, and the compensation rate reached 280.22%. The pilot meteorological index insurance in Lanzhou has been highly praised and recognized by growers with a simple, rapid and open claim settlement mechanism. In order to ensure the effective promotion of agricultural climate index insurance, four suggestions are put forward: local governments should attach great importance to it, not only provide corresponding financial subsidies, but also invite professional institutions to guide the pilot work. The product research work should be full and accurate, so as to lay a solid foundation for the later insurance scheme design and claim settlement. The climate index insurance product scheme should be formulated by a professional team, which is operable and scientific. Before carrying out the work, we should publicize in place, unify ideas, create an atmosphere, and implement the undertaking and claim settlement work in place [54]. On the other hand, we should focus on the development concept of “ecology, empowerment, digital and platform”, realize the high-quality development of inclusive finance, pay attention to the coordination of policy finance, commercial finance, and cooperative finance and micro finance, realize the integrated development of inclusive finance, green finance and digital finance, and continuously deliver appropriate inclusive financial services and products to increase farmers’ income. We should establish the concept of responsible finance, provide farmers with products and services matching their risk tolerance, reasonably adjust the access threshold for financial products with low risk, enhance the universality of financial services, and create conditions for effectively increasing farmers’ property income [55]. Large commercial financial institutions should form bank-government, bank-bank and bank-enterprise cooperation modes, such as government credit enhancement, household loan, farmers’ consumption loan and “two rights” mortgage loan, and develop and promote more “Internet +” products and services. Commercial financial institutions should strengthen cooperation with financial institutions such as insurance, leasing, funds and securities, and innovate risk compensation system and mechanism. Cooperative financial institutions should further promote rural information construction by establishing rural information and credit database, make full use of information advantages, and let farmers with different credit grades enjoy different credit loans and different interest rates. Micro financial institutions should establish a sound long-term mechanism, a scientific and reasonable corporate governance structure, identify their own market positioning, establish a scientific risk prevention and control mechanism, rely on the resource advantages of core enterprises, industrial chains, supply chains and professional markets, and use advanced technologies such as big data and cloud computing to provide efficient and appropriate financial services for farmers.

Fourth, exploring an appropriate evaluation index system should increase the sustainable development of farmers’ livelihoods. A scientific and robust index system should be based on the understanding of farmers’ livelihoods, the combination and flow of livelihood assets, as well as the impact of climate change and other risks. Based on the principles of the unity of scientificity and practicability, systematization and hierarchy, comparability and quantification, guidance and pertinence, combining quantitative assessments with qualitative assessments, we should build a sustainable developmental assessment index to identify vulnerable groups, conduct targeted sustainable livelihood capacity-building training, and improve their adaptability [56]. The measurement of farmers’ livelihood sustainability needs to break through the limitation of the traditional single evaluation of the governance effect of post livelihood state, and add the evaluation of elements such as poverty risk early warning and exact defense ability. The first is the follow-up assessment of external climate risk early warning, which mainly assesses the sensitivity and vulnerability of poverty-stricken areas and farmers in the face of climate change and natural disasters. The second is the evaluation of farmers’ endogenous resilience, including psychological endurance, adaptability, resilience and so on. The evaluation index system of farmers’ livelihood sustainability should be multidimensional, integrated and dynamic. Therefore, the evaluation system of farmers’ livelihood sustainability should not only take into account efficiency and equity, but also highlight the value orientation of sustainable development, so as to provide a guarantee for farmers’ sustainable development and rural revitalization. A scientific and reasonable evaluation index system for the sustainable development of farmers’ livelihoods should be used as an action guide for policy assistance. Farmers should be stratified and classified, should distinguish between sustainable families, highly sensitive families, more vulnerable families and vulnerable families, should find out the reasons for the weak ability of families to resist risks, and should implement policies by categories, so as to carry out targeted assistance and education. For families with weak anti-risk ability, a long-term assistance mechanism should be given from the aspects of industrial assistance, policy disclosure, training and education. The assessment results of farmers’ livelihood sustainable development should be filed and updated in real time, so as to grasp the trend of farmers’ sustainable livelihood in time and give strong and accurate support under different climatic conditions and different family conditions.

Fifth, we should accelerate the construction of agrometeorological disaster prevention and mitigation infrastructure. (i): Prepare emergency plans for towns and villages, conduct regular emergency drills, and increase the investment in emergency facilities. Over the past 20 years, more than 80% of the casualties and economic losses caused by disasters in China have been distributed in rural areas. More than 70% of China’s 832 poverty-stricken counties are located in areas vulnerable to rainstorm and flood geological disasters. The risk of returning to poverty due to disasters is a serious challenge, especially in key rural areas such as the eastern coast, the southern coast, the middle reaches of the Yangtze River and the northeast. The main reason is that key rural areas are highly sensitive to climate change, while the engineering and non-engineering measures for disaster prevention and reduction are relatively weak. In major natural disasters, rural roads in some key areas have been destroyed, and power, communication and traffic have been interrupted to form an “isolated island” situation. Therefore, effectively improving the ability of disaster prevention and reduction and emergency management in rural areas has become one of the important topics to promote China’s high-quality development. Efforts should be made to build township emergency management offices, emergency rescue stations, traffic management stations, emergency command centers, emergency rescue centers and emergency material reserve centers. We should strengthen the reserve of emergency materials in rural areas, and store emergency rescue materials such as fire motor pumps, emergency generators, emergency lights, flashlights and satellite phones, as well as necessary disaster relief materials such as cotton clothes, quilts, tents and beds in township and village emergency material reserves in batches and stages, so as to open up the “last kilometer” of emergency rescue [57]. We should strengthen the construction of emergency rescue teams, integrate and upgrade the voluntary fire-fighting teams of all townships and villages (residents) into emergency rescue teams, strengthen the professional ability training of emergency rescue teams, and strive to build a rural emergency rescue team with “excellent style, ability to fight and win”; (ii): Strengthen the construction of comprehensive agrometeorological monitoring network, promote multi-sectoral data sharing, jointly strengthen the capacity-building of forecasting and early warning services, and help improve the anti-risk ability of agriculture. We should jointly build a national weather, climate and climate change, professional meteorological and space meteorological observation network, form a land, sea, air and space integrated, coordinated and efficient precision meteorological monitoring system, strengthen global meteorological monitoring, and improve the ability to obtain and share global meteorological data [58]. We should strengthen the capacity-building of the earth system numerical prediction center, develop an independent and controllable earth system numerical prediction model, and gradually form the accurate prediction ability of “five ones”, so as to realize the early warning of local strong weather one hour in advance, hourly weather one day in advance, disastrous weather one week in advance, major weather processes in January in advance and global climate anomalies one year in advance. We should improve the monitoring, forecasting and early warning system for meteorological disasters by disaster types and key industries, and improve the ability to forecast and early warning meteorological risks such as extreme weather and climate events, small and medium-sized river floods, mountain floods, geological disasters, marine disasters, regional floods in river basins, forest and grassland fires. For example, Mudan District of Heze City has promoted the prevention and control of agricultural pests through “digital empowerment”, realized the standardization, digitization, intelligence, visualization and scientization of agricultural pest monitoring, achieved the goal of monitoring coverage of major agricultural pest prone areas in the province to reach 100% and disaster prediction accuracy to reach more than 95% in 2020, and completed the monitoring and control of major diseases and pests in the first half of 2021; (iii): For weak links and regions vulnerable to climate impact, formulate meteorological services with “accurate forecast and fine service”. We should carry out refined and differentiated agrometeorological services by region, crop, disaster and link, and strengthen characteristic agrometeorological services [59]. We should promote the digital and intelligent transformation of meteorological services, develop scene-based and impact-based meteorological service technology, study and build a platform for big data, intelligent product production and financial media release of meteorological services, and develop intelligent meteorological services with intelligent research and judgment and accurate push. We should strengthen the implementation of “meteorology +” enabling action, improve the ability of Rural Revitalization of meteorological services, do a good job in key agricultural time and special agricultural services, refine the meteorological forecast service indicators of main crops in different growth periods, and provide more targeted and refined technical support for agricultural production. We should strengthen the application of advanced technologies such as hyperspectral remote sensing and related equipment in agricultural monitoring, improve the ability of fine meteorological disaster prediction and grain yield prediction in the whole process of grain production in key areas, and strengthen meteorological services for agricultural production. We should carry out personalized and customized meteorological services, continue to further promote cooperation with emergency management, ecological environment, transportation, agriculture and rural areas, natural resources, tourism and other departments, carry out refined services for rainstorm (snow), urban waterlogging, geological disasters, drought, cold wave, fog and other disasters, and promote the upgrading of meteorological services to high quality and diversification [60]. For example, Qianjiang District Meteorological Bureau and the District Agricultural Committee of Chongqing jointly built four characteristic agrometeorological observation stations and one flue-cured tobacco landscape observation station in the Yangtoushan modern agricultural park, the Zhuoshui Modern Agricultural Expo Park, the Shaba characteristic agricultural park, the modern sightseeing and leisure agriculture demonstration belt in Apeng River Basin and other areas. At the same time, the regional meteorological bureau also actively cooperates with the Regional Bureau of culture and broadcasting to promote the construction of loudspeakers. According to statistics, since the opening of the megahorn, Huangxi town alone has prevented more than 40 meteorological and other disasters and recovered economic losses of more than 1.3 million yuan. Qianjiang District has effectively improved the ability of meteorology to serve agriculture by strengthening the construction of “two systems” of meteorology to serve agriculture, strengthening departmental cooperation, and establishing and improving the rural meteorological disaster prevention mechanism.