Adapting Seasonal Rice Cultivation Strategies for Food Security in Response to Climate Change Impacts

Abstract

An in-depth examination of the effects of climate change on rice yield in China, encompassing various rice types, is crucial for ensuring the nation’s food security. This study develops an “economy-climate” theoretical model and employs Panel Corrected Standard Error Estimation (PCSE) on panel data spanning from 1978 to 2018, sourced from China’s primary grain-producing regions. The analysis delves into the impact of climate variables, including precipitation, temperature, and sunshine duration, on overall rice production and different rice types. Overall, the findings reveal a nonlinear relationship between precipitation, temperature, sunshine duration, and rice yield, characterized by an “inverted U-shaped” pattern. However, significant variations exist in the effects on different rice types across China’s main grain-producing areas. Increasing precipitation generally enhances early rice production across provinces and also augments mid-season and one-season-late rice production in the Inner Mongolia Autonomous Region, Hebei, Jilin, Heilongjiang, and Shandong Province. Conversely, it reduces mid-season and one-season-late rice output in Liaoning, Jiangsu, Anhui, Jiangxi, Henan, Hubei, and Hunan. Sichuan Province sees a rise in temperature favoring early and double-season-late rice production, unlike other provinces. For mid-season and one-season-late rice, temperature increases benefit output in Heilongjiang Province but not in other regions. Additionally, prolonged sunshine duration boosts early and double-season-late rice production across all provinces but does not have the same effect on mid-season and one-season-late rice in China’s primary grain-producing areas.

1. Introduction

From the Copenhagen Climate Conference to the Seoul Climate Conference, climate change has emerged as a prominent environmental concern, garnering continued attention from the international community. The Intergovernmental Panel on Climate Change’s (IPCC) fifth assessment report underscored a significant 0.85 °C increase in global temperatures over the past 130 years, accompanied by a gradual rise in extreme climate events [1,2]. Given the substantial sensitivity of food production to climate variability, academia and relevant governmental bodies have long been engaged in examining the implications of climate change on food production since the 1970s. Rice stands as China’s primary food crop, and ensuring its consistent growth holds paramount importance in safeguarding the nation’s food security [3,4]

Nonetheless, as the impact of climate change on rice production deepens, a series of troubles have emerged. For example, as global warming intensifies, temperatures show an upward trend year after year, leading to a shortening of the rice growth cycle and insufficient nutrient uptake by rice, thus affecting the yield and quality of rice [5,6]. At the same time, rice is prone to pests and diseases, such as rice blast and red-eye borer, during extremely hot weather, thus leading to increased rice production costs [7]. In addition, insufficient precipitation is prone to problems such as delayed rice maturity and slow rice production, which can lead to serious reductions in rice yields [8,9]. The range of problems associated with climate change is highlighted by significant instability in rice yields. Between 1997 and 2004, China experienced a consecutive seven-year decline in rice yield, plummeting from 200,734,800 tons to 179,087,600 tons, marking a 12% reduction. Despite a subsequent 12-year consecutive increase in rice yield from 2004 to 2015, the growth rate during this period has exhibited varying degrees of decline.

This raises a key question: is climate change having a significant impact on rice production in China? In addition, are there significant differences in the effects of climate change on different rice varieties? Through the existing meteorological data and information, the above problems are comprehensively analyzed in order to achieve the following research objectives. First, analyzing the effects of climate change (including precipitation, temperature, and sunshine duration) on total rice production and yields of different types of rice helps us to accurately understand the specific impacts of climate change on rice production and the reasons behind them. Second, based on the results of the study on the impact of climate change on the total rice output and the yield of different types of rice, it will provide a reference for the formulation of differentiated climate strategies to promote the sustainable development of rice production. Therefore, by establishing the “economy-climate” theory and utilizing the panel data of major grain-producing regions in China from 1978 to 2018, this research conducted an in-depth exploration of the linear and nonlinear impacts of climate change (precipitation, temperature, and duration of sunshine) on rice yield and the disparity of rice types, thereby providing reference experiences for promoting the stable increase in rice production in China and guaranteeing China’s food security.

2. Literature Review

As the global climate gradually warms and extreme weather events become more frequent, an increasing number of scholars have conducted research on the effects of climate change on rice production, yielding diverse and abundant findings. In terms of research methodologies, most studies employ either crop mechanism models or econometric models. Crop mechanism models are grounded in crop production theory and involve controlled production experiments. Researchers utilize experimental data and set relevant parameters to dynamically simulate the development process of rice, elucidating the relationship between environmental factors such as temperature, light, radiation, soil, and rice yield [10,11,12,13]. However, due to inherent uncertainties in crop production processes, scholars often rely on their own experience to describe these processes, leading to a reliance on assumptions for a large number of parameters, thus limiting the universality of conclusions drawn from crop mechanism models. In contrast, econometric models leverage historical statistical data and objective laws, employing sampling surveys and quantitative estimation methods to more accurately gauge the impact of climate change on rice production compared to crop mechanism models.

Most quantitative studies investigating the influence of climate change on rice production typically operate at the national, provincial, or county level. These investigations primarily examine the impact of climate change on rice yield, production efficiency, and climate-related factors affecting rice yield. Regarding the impact of climate change on rice production, emphasis is placed on both total rice yield and rice yield per unit area. For instance, Yang et al. (2008) [14] observed a significant adverse effect of climate change on rice output in Anhui Province, with the growing season of double-cropping rice being notably shortened amidst climate warming. Furthermore, they highlighted the substantial negative impact of extreme weather events on rice yield, a finding corroborated by Pickson et al.’s (2021) [15] research. Zhou et al. (2013) [1] investigated the influence of climate change on total rice yield in southern China. Their findings revealed that increased precipitation negatively affected South, Central, and East China yet had a positive impact on Southwest China. Conversely, rising temperatures adversely affected Southwest, South, East, and Central China. These conclusions regarding climate change’s effects on total rice yield in the south extend to rice yield per unit area. Yin et al. (2017) [16] analyzed farmer-level survey data from Hubei Province, highlighting significant variations in the impact of climatic factors on rice yield. They noted that rising temperatures significantly enhanced rice yield per unit area, with an inverted “U” relationship occurring between the two elements. While increased precipitation had no significant effect on rice yield per unit area, high-temperature disasters exerted a considerable negative impact. Yang et al. (2008) [14] demonstrated that increased precipitation and sunshine intensity contributed to higher rice yields per unit area. They concluded that the greater intensity of sunshine in the dry season was more effective in increasing the yield of rice per unit area. Secondly, climate factors exert a significant influence on rice production efficiency. Jiang et al. (2015) [17] investigated the impact of climate variables on rice production efficiency using rice input yield and climate data from eight regions in Jiangsu Province. Their findings revealed that the ratio of light to temperature in June, August, and September, along with July precipitation, positively affected the technical efficiency of rice production. Conversely, the average July temperature and the ratio of light to air temperature had an inhibitory effect on production efficiency. Similarly, Li & Zhou (2014) [18] utilized China’s provincial panel data to employ the stochastic frontier trans-log production function, measuring the production efficiency of Chinese japonica rice from 1980 to 2012. Their study showed that increased precipitation and sunshine decreased the productivity of Japonica rice and increased average temperature increased the productivity of Japonica rice, which is in line with Ankrah et al.’s (2023) [19] study. Ankrah et al. (2023) [19], based on survey data from rice farmers in Ghana, West Africa, concluded that climate change positively influences agricultural production technology, thereby enhancing rice production efficiency.

Thirdly, we examine the contribution rate of climatic factors to rice yield. Fu et al. (2016) [20] investigated the contribution rates of climate factors to rice yield in different periods, focusing on Jiangxi Province. They found varying contribution rates of climate factors to rice output between 1978 and 1997 and between 1998 and 2013, with temperature and precipitation playing differential roles. Additionally, Zhu et al. (2013) [21] explored the impact of climatic factors on rice yield at different developmental stages, revealing regional variations in the contribution rates of climatic factors to rice yield. Chen et al. (2021) [22], studying rice wheat cropping in the Jianghuai region, reached similar conclusions based on the climatic characteristics and crop production dynamics of the region.

In summary, both domestic and international experts and scholars have extensively researched the impact of climate change on rice yield, providing valuable insights for further investigation. However, several shortcomings persist [14,15,17,23,24,25]. Existing research primarily focuses on either nationwide or provincial levels, with limited consideration being given to specific geographic areas as research subjects. In addition, Zhu et al. (2013) [21] discussed the effects of climate factors on rice yield at different development stages. They revealed that regional differences in rice yield among different rice types in the same region are often overlooked. Furthermore, some examples in the literature predominantly explore the linear effects of climatic factors, such as temperature and precipitation on rice yield, neglecting the in-depth examination of nonlinear characteristics. Therefore, investigating both linear and nonlinear effects of climate change (including precipitation, temperature, and daylight hours) on rice yield, as well as their impact on various rice types, can offer valuable insights for enhancing stable rice production and ensuring food security in the country.

3. Material and Method

3.1. “Economy-Climate” Model

Rice production is the result of the interaction between social, economic, and natural factors. Socio-economic factors mainly include inputs of material elements such as fertilizers, labor, and agricultural machinery power. Among the natural factors, the climate factor is the most important. Climate elements mainly include precipitation, sunlight, and temperature. These factors not only provide necessary energy for rice production but also directly affect rice yield. The C-D production function is used to analyze the relationship between input and output. However, the traditional C-D production function only considers some controllable and limited factors and pays insufficient attention to uncontrollable factors such as climate factors. However, in the context of climate change, changes in climatic elements such as precipitation, light and temperature have an increasing impact on rice production. Therefore, based on the C-D production function, this article incorporates climate elements and establishes an “economy-climate” model. This model is used to more accurately show the relationship between rice production input and output.

Suppose that the production function is the Cobb–Douglas production function with constant returns to scale. Its general form can be expressed as:

$$Y=A{L}^{\alpha }{K}^{\beta }\mu$$

Among them, $Y$ represents yield, $A$ represents technology level, $L$ represents labor input, $K$ represents capital input, and $\mu$ mainly represents other factors that can affect yield. $\alpha$ and $\beta$ are estimated parameters which, respectively, represent the output elasticity of $L$ and $K$. Later, climate factors were further incorporated into the model. At the same time, in order to more comprehensively and scientifically reflect the factors affecting rice production, the model also introduced variables such as technological progress and institutional changes.

$$Y=F(X_j,C_n,T_r,T_e)$$

In the mode, $Y$ represents rice yield, $X_j$ represents input factors such as labor, fertilizer, mechanical power, etc., $C_n$ represents climate factors such as temperature, sunshine duration, and precipitation, $T_r$ represents policy dummy variables, and $T_e$ represents rice production technology progress variables. This paper refers to the benchmark regression model of Chen et al. (2016) [26] to establish a linear model that takes into account climate change $\left(1\right)$:

$$ln\left(Y_{i,t}\right)={\alpha }_0+{\alpha }_1{lnC}_{n,i,t}+\beta {lnX}_{j,i,t}+\gamma {lnT}_{r,i,t}+\delta {lnT}_{e,i,t}+u_i+{\epsilon }_{i,t}$$

(1)

This paper further expands model $\left(1\right)$ to model $\left(2\right)$:

$$\begin{array}{c}ln\left({ryp}_{i,t}\right)={\alpha }_0+{\alpha }_1{lnmat}_{i,t}+{\alpha }_2{lnmp}_{i,t}{+\alpha }_3{lnacl}_{i,t}+{\beta }_1{lnrlf}_{i,t}+{\beta }_2{lnrcf}_{i,t}+{\beta }_3{lnrm}_{i,t}{{+\beta }_4{lnri}_{i,t}+\gamma }_1{lnpp1}_{i,t}+{\gamma }_2{lnpp2}_{i,t}\end{array}+\delta {lnrtp}_{i,t}+u_i+{\epsilon }_{i,t}$$

(2)

In order to further verify whether there is a nonlinear relationship between climate change and rice yield, this paper further expands the “economy-climate” model, introduces the quadratic term of the climate factor, and establishes a logarithmic process to establish model $\left(3\right)$ for research.

$$\begin{array}{c}ln\left({ryp}_{i,t}\right)={\alpha }_0+{\alpha }_1{lnmp}_{i,t}+{\alpha }_2{lnmat}_{i,t}{+\alpha }_3{lnacl}_{i,t}+{\alpha }_4{\left({lnmat}_{i,t}\right)}^2+{\alpha }_5{\left({lnmp}_{i,t}\right)}^2+{\alpha }_6{\left({lnacl}_{i,t}\right)}^2+{\beta }_1{lnrlf}_{i,t}\\ +{\beta }_2{lnrcf}_{i,t}+{\beta }_3{lnrm}_{i,t}{{+\beta }_4{lnri}_{i,t}+\gamma }_1{lnpp1}_{i,t}+{\gamma }_2{lnpp2}_{i,t}+\delta {lnrtp}_{i,t}+u_i+{\epsilon }_{i,t}\end{array}$$

(3)

In models $\left(2\right)$ and $\left(3\right)$, $i$ and $t$ represent province and year. $ryp$ stands for rice yield (including the sum of early rice, mid-season rice, one-season-late rice and two-season-late rice); $C_n$ represents the climatic factor, and mp, mat and acl represent precipitation, temperature and sunlight duration; $X_j$ delegates the input elements of rice production, rlf, rcf, $rm$ and $ri$ delegate the labor force, fertilizer application, agricultural machinery power, and irrigation used for rice production; $T_r$ denotes the policy dummy variable, $pp1$ and $pp2$ represent grain purchase price policy and grain direct subsidy policy; $T_e$ means rtp represents the progress of rice production technology; ${\alpha }_0$ represents the constant term; ${\alpha }_1$, $\beta$, $\gamma$, $\delta$ are all parameters to be estimated; $u_i$ represents the error term; and ${\epsilon }_{i,t}$ represents the random interference term.

3.2. Data

This study utilizes panel data spanning from 1978 to 2018, focusing on the primary grain-producing regions designated by China’s grain circulation reform system. The research variables encompass dependent variables, climate variables, input variables, and other control variables. The dependent variable comprises rice yield, encompassing early rice, mid-season rice, one-season-late rice, and double-season-late rice. According to the research content of Yang et al. (2008), Jiang et al. (2015) and Hu et al. (2021) [14,17,27], this manuscript divides rice types into early rice, middle rice, one-season-late rice and double-season-late rice. It is divided according to the length of the rice growing period, seasonality of sowing and harvesting period, etc. Specifically, early rice is sown in early spring, harvested in summer, and has a short growing period. Middle rice and one-season-late rice are generally sown in summer and harvested in autumn or winter, with moderate growth periods. Late rice is usually sown in summer or autumn, harvested in winter, and has a longer growing period. Annual average precipitation, temperature, and cumulative sunshine duration are constructed using monthly observation data from 470,844 meteorological stations between 1978 and 2018. Because variables such as labor force, fertilizer, agricultural machinery, irrigation area and technological progress engaged in rice production are closely related to rice production, this paper, by referring to the research content of Zhu et al. (2013) [21], Pickson et al. (2021) [15] and Hu et al. (2021) [27], Variables such as labor force, fertilizer, agricultural machinery, irrigated area, and technological progress were selected for rice production. The specific reasons include, for example, that the labor force engaged in rice production can directly increase the frequency and precision of farmland management, affecting rice production. When the irrigated area is increased, it is helpful to ensure that the rice can receive enough water in the production process and improve the rice yield. When the amount of fertilizer is increased, the lost nutrients in the soil can be replenished in time and the photosynthesis of rice leaves can be promoted, thus increasing the rice yield. Input variables include agricultural labor force, fertilizer application, agricultural machinery power, and irrigation, calculated using the weight coefficient method based on the percentage of rice sown area to total crop sown area [27,28]. Original data for input variables are sourced from agricultural labor force, fertilizer application, agricultural machinery power, and agricultural irrigation records. Additionally, three control variables are incorporated: rtp, representing rice production technology progress; $pp1$, indicating the protection price policy for grain purchases (implemented since 1998); and $pp2$, denoting the direct grain subsidy policy (implemented since 2004). The control variables are binary, with pp1 set to 1 if the year is 1998 or later and $pp2$ set to 1 if the year is 2004 or later; otherwise, they are set to 0. Data sources for all variables, except climate variables, are derived from the “China Statistical Yearbook” and “China Rural Statistical Yearbook”, while climate variable data are obtained from the China Meteorological Science Data Center.

Table 1. Descriptive Statistics.

$\mathit{V}\mathit{a}\mathit{r}\mathit{i}\mathit{a}\mathit{b}\mathit{l}\mathit{e}$	$\mathit{M}\mathit{e}\mathit{a}\mathit{s}\mathit{u}\mathit{r}\mathit{e} \mathit{U}\mathit{n}\mathit{i}\mathit{t}$	$\mathit{O}\mathit{b}\mathit{s}$	$\mathit{M}\mathit{e}\mathit{a}\mathit{n}$	$\mathit{S}\mathit{t}\mathit{d}.\mathit{D}\mathit{e}\mathit{v}.$	$\mathit{M}\mathit{i}\mathit{n}$	$\mathit{M}\mathit{a}\mathit{x}$
$ryp$	ten thousand tons	533	983.6577	836.2865	0.5	2819.3
$eryp$	ten thousand tons	246	4769.653	1789.178	0	7647.059
$mryp$	ten thousand tons	533	6507.731	1450.082	746.269	9578.332
$leyp$	ten thousand tons	246	5013.31	1722.229	0	8571.429
$mp$	millimetre	533	73.43	33.99	19.505	179.546
$mat$	celsius	533	11.745	5.018	0.978	18.503
$acl$	hour	533	54,955.93	51,825.11	18,973.44	1,080,000
$rlf$	million population	533	184.636	196.712	0.796	790.861
$rcf$	ten thousand tons	533	36.76	34.542	0.011	146.071
$rm$	ten thousand kilowatts	533	461.218	556.362	0.52	3185.814
$ri$	thousand hectares	533	528.767	479.967	1.82	1612.699
$rtp$	--	533	1.939	1.69	0.441	11.598
$pp1$	--	533	0.512	0.5	0	1
$pp2$	--	533	0.336	0.482	0	1

Data source: summarized according to “China Statistical Yearbook”, “China Rural Statistical Yearbook”, and China Meteorological Science Data Center.

presents a statistical description of the sample interval.

4. Results and Discussion

4.1. The Impact of Climate Change on the Rice Yield

This paper constructs two econometric models by introducing different forms of climate variables and then uses the Panel Corrected Standard Error Estimation Model (PCSE) to investigate the impact of climate change on rice yield. Model (1) is a one-time term of precipitation, temperature, and duration of sunshine, focusing on the linear impact of climate change on rice yield. Model (2) is based on model (1) and adds the quadratic form of climate variables to investigate the nonlinear impact of climate change on rice yield. In order to enhance the robustness of the research conclusions, this paper conducts a robustness test on the structural model (3), replaces the rice yield with the rice yield per unit area for regression, and reaches consistent research conclusions. The analysis of research conclusions is mainly based on model (1) and model (2). The specific analysis results are as follows:

4.1.1. The Impact of Precipitation on the Rice Yield

It can be seen from model (1) (see

Table 2. Baseline regression 1.

$\mathit{E}\mathit{x}\mathit{p}\mathit{l}\mathit{a}\mathit{n}\mathit{a}\mathit{t}\mathit{o}\mathit{r}\mathit{y} \mathit{V}\mathit{a}\mathit{r}\mathit{i}\mathit{a}\mathit{b}\mathit{l}\mathit{e}\mathit{s}$	$\mathit{M}\mathit{o}\mathit{d}\mathit{e}\mathit{l}\left(1\right)$	$\mathit{M}\mathit{o}\mathit{d}\mathit{e}\mathit{l}\left(2\right)$	$\mathit{M}\mathit{o}\mathit{d}\mathit{e}\mathit{l}\left(3\right)$
$lnmp$	0.1033 *** (0.0309)	0.5450 * (0.2787)	1.4206 *** (0.2464)
$lnmat$	−0.4315 *** (0.0181)	−0.0830 (0.1063)	0.4392 *** (0.0907)
$lnacl$	0.0080 (0.0258)	2.9632 ** (1.3437)	3.6532 *** (1.0311)
$lnmp$2		−0.0630 ** (0.0319)	−0.1749 *** (0.0288)
$lnmat$2		−0.0958 *** (0.0322)	−0.0993 *** (0.0260)
$lnacl$2		−0.1395 ** (0.0633)	−0.1709 *** (0.0485)
$lnrlf$	0.4120 *** (0.0220)	0.4617 *** (0.0337)	−0.0892 *** (0.0245)
$lnrcf$	0.3882 *** (0.0209)	0.2433 *** (0.0240)	0.1801 *** (0.0188)
$lnrm$	−0.0106 (0.0258)	0.1496 *** (0.0284)	0.0954 *** (0.0245)
$lnri$	0.2416 *** (0.0270)	0.2074 *** (0.0404)	−0.1382 *** (0.0297)
$lnrtp$	0.0236 *** (0.0054)	0.0730 *** (0.0113)	0.0653 *** (0.0083)
$pp1$	−0.0087 (0.0250)	−0.0495 * (0.0295)	−0.0243 (0.0288)
$pp2$	0.1233 *** 0.0246)	0.0647 ** (0.0304)	−0.0127 (0.0289)
$\_cons$	2.4251 *** (0.3626)	−14.7162 ** (6.9348)	−14.0220 *** (5.3237)
$N$	533	533	533
$R-sq$	0.990	0.996	0.998

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

) that increased precipitation has a positive effect on the rice yield and passed the 1% significance level test, indicating that, when the precipitation increases by 1%, the rice yield increases by 0.1033. In model (2), both the primary and secondary terms of precipitation have reached significant levels. In other words, the relationship between precipitation and rice yield is “inverted U” nonlinear. In model (3), both the primary and secondary terms of precipitation reached significant levels, which verified the “inverse U” nonlinear relationship between precipitation and total rice output. This shows that, when the amount of precipitation is low, increasing the amount of precipitation can increase the output. However, when the precipitation increases to the turning point, continuing to increase the precipitation will lead to reduced production. The reason is that drought and low rainfall will inhibit the growth and development of rice. When rice production is in the jointing–booting stage, rice is particularly more sensitive to water requirements [17]. A moderate increase in precipitation is beneficial in regard to increasing the number of rice grains per panicle and increasing the rice seed setting rate, thereby increasing the rice yield. However, but when the precipitation is too high, it will cause the oxygen content of the paddy field to decrease and the number of rice tillers to decrease. This eventually causes a reduction in rice production [7]. According to the studies of Yang et al. (2008) [14] and Fu et al. (2016) [20], the increase in precipitation is conducive to the increase in rice yield. However, only the linear relationship between precipitation and rice yield was described in this study, and no nonlinear relationship was involved. It can be seen from this part of the manuscript that there is an “inverted U-shaped” nonlinear relationship between precipitation and total rice production, which means that, when precipitation is low, an appropriate increase in precipitation can increase total rice production, but when precipitation reaches a certain inflection point, a further increase in precipitation will lead to a decrease in total rice production.

4.1.2. The Effect of Temperature on the Output of Rice

It can be seen from model (1) (see Table 2) that temperature rise has a negative impact on the rice yield and passes the 1% significance level test, indicating that when the temperature rises by 1%, the rice yield decreases by 0.4315. In model (2), the quadratic term of temperature is significant at the 5% level. In other words, the relationship between temperature and rice yield is an “inverted U-shaped” nonlinear relationship. In model (3), both the primary and secondary terms of precipitation reached significant levels, which verified the “inverted U”-type nonlinear relationship between temperature and total rice yield. This shows that, when the temperature is low, the increase in temperature can increase the rice yield. However, when the temperature rises to the turning point, the continued increase in temperature will lead to a decline in rice production. The reason is that an appropriate temperature rise will shorten the rice production season, increase the multiple rice cropping index, and increase the rice output. However, when the temperature is too high, the following reasons will reduce the total rice yield. First, although rising temperatures will accelerate rice production, they are not conducive to increasing the number of rice tillers and will reduce the dry weight and ear weight of rice. If local farmers do not crop their rice multiple times, the rice yield will decrease [8]. Second, high temperature is prone to breed diseases, insect pests, and rice-associated weeds, resulting in a decrease in the rice output [15,24]. According to the study of [16], from the perspective of a linear relationship, air temperature has a significant promoting effect on rice yield per unit area; however, from the perspective of a nonlinear relationship, there is a significant “inverted U” relationship between the two. This research conclusion is only based on the micro-survey data of farmers in Hubei Province, China, and the reliability of the research conclusion is not high. Therefore, based on the panel data of major grain-producing areas in China from 1978 to 2018, this part of the manuscript concludes that there is an “inverted U-shaped” nonlinear relationship between temperature and total rice production, which verifies the conclusion of this study and increases the reliability of the conclusion.

4.1.3. The Effect of Sunshine Duration on the Rice Yield

It can be seen from model (1) (see Table 2) that increased sunshine duration has a positive effect on the rice yield. This shows that, when the sunshine duration increases by 1%, the rice yield will increase by 0.008. Model (2) shows that both the primary and secondary terms of precipitation reached a significant level. In other words, the sunshine duration and the rice yield have an “inverted U-shaped” nonlinear relationship. In model (3), both the primary and secondary terms of precipitation reached significant levels, which verified the “inverted U”-type nonlinear relationship between sunshine duration and total rice yield. This shows that, when the number of sunshine hours is small, increasing it can increase rice yield. However, when the sunshine hours increase to a turning point, its continued increase will lead to a reduction in production. The reason for this is that appropriately increasing the sunshine time is beneficial to the heading of rice and increases the heading rate of rice. However, when the sunshine time is too long, it will lead to accelerated evaporation of rice water and an insufficient water supply for the rice. Rice grains can dry out easily, which is not conducive to the increase in rice output [9]. According to the study of Pickson et al. (2021) [15], the increase in sunshine duration has a positive effect on the total yield of rice. However, only the linear relationship between sunshine duration and rice yield was described in this study, and no nonlinear relationship was involved. It can be seen from this part of the manuscript that there is an “inverted U-shaped” nonlinear relationship between sunshine duration and total rice production, which means that, when the sunshine duration is small, increasing the sunshine duration appropriately can increase the total rice production, but when the sunshine duration reaches a certain inflection point, continuing to increase the sunshine duration will lead to a decrease in total rice production (Figure 1).

4.1.4. The Impact of Other Variables on the Rice Yield

It can be seen from the model (2) that the labor force, chemical fertilizers, agricultural machinery, irrigation area and technological progress variables engaged in rice production all have a positive effect on the rice yield and have all passed the 1% significance level test. In other words, over time, the contributions of the labor force, fertilizer, agricultural machinery, irrigated area, and technological progress to rice production will increase. When the labor force engaged in rice production is increased, the frequency and precision of farmland management can be directly increased; for example, the work of weeding and pest control can be carried out in a more timely manner to improve rice yield [5,19]. When the amount of fertilizer is increased, the lost nutrients in the soil can be replenished in time and the photosynthesis of rice leaves can be promoted, thus increasing the rice yield [21]. When the degree of agricultural mechanization is increased, the scale, standardization, and efficiency of agricultural production can be improved so as to reduce the loss of rice caused by human factors and increase rice yield [25]. Since rice is a crop with large water demand, increasing the irrigated area will help ensure that rice can obtain enough water during production and improve rice yield [9]. When the level of technological progress is improved, elements such as the cultivation and cultivation of new rice varieties can significantly improve the yield and quality of rice [23]. However, the protective price policy for grain purchases has a negative effect on the rice output. In other words, with the development of time, the policy of grain purchase protection price has a more and more negative effect on the total output of rice. The reason is that, due to the inadequate distribution of subsidies, the subsidies that actually reach the farmers engaged in rice production are limited and the actual effects of the protection price for grain purchases have not been fully utilized [29,30,31].

4.2. The Differential Impact of Climate Change on the Yield of Different Types of Rice

Due to the notable disparities in sowing time, growth period and maturity period among different types of rice, this paper classifies rice types into early rice, middle rice, single-cropping-late rice and double-cropping-late rice.

4.2.1. The Difference of Precipitation on the Yield of Different Types of Rice

It can be seen from

Table 3. The effects of increased precipitation on different types of rice.

$\mathit{P}\mathit{r}\mathit{o}\mathit{v}\mathit{i}\mathit{n}\mathit{c}\mathit{e}$	$\mathit{R}\mathit{i}\mathit{c}\mathit{e} \mathit{T}\mathit{y}\mathit{p}\mathit{e}$
	$\mathit{E}\mathit{a}\mathit{r}\mathit{l}\mathit{y} \mathit{S}\mathit{e}\mathit{a}\mathit{s}\mathit{o}\mathit{n} \mathit{R}\mathit{i}\mathit{c}\mathit{e}$	$\mathit{M}\mathit{e}\mathit{d}\mathit{i}\mathit{u}\mathit{m} \mathit{R}\mathit{i}\mathit{c}\mathit{e} \mathit{a}\mathit{n}\mathit{d} \mathit{S}\mathit{e}\mathit{a}\mathit{s}\mathit{o}\mathit{n} \mathit{L}\mathit{a}\mathit{t}\mathit{e} \mathit{R}\mathit{i}\mathit{c}\mathit{e}$	$\mathit{D}\mathit{o}\mathit{u}\mathit{b}\mathit{l}\mathit{e} \mathit{S}\mathit{e}\mathit{a}\mathit{s}\mathit{o}\mathit{n} \mathit{L}\mathit{a}\mathit{t}\mathit{e} \mathit{R}\mathit{i}\mathit{c}\mathit{e}$
$Liaoning$	⚫
$Hebei$	⚫	⚫
$Shandong$	⚫	⚫
$Jilin$	⚫	⚫
$Inner Mongolia$	⚫	⚫
$Jiangxi$	⚫
$Hunan$	⚫
$Sichuan$	⚫
$Henan$	⚫
$Hubei$	⚫
$Jiangsu$	⚫
$Anhui$	⚫
$Heilongjiang$	⚫	⚫

and Table 6 that, in models (4), (6) and (8), the increase in precipitation has a positive impact on the yield of early rice and double-season-late rice but it has a negative impact on the yield of mid-season rice and single-season-late rice. All three have passed the 1% significance level test. This shows that, when the precipitation increases by 1%, the yield of early rice will increase by 1.5679, the yield of double-season-late rice will increase by 1.6721, and the yield of mid-season rice and single-season-late rice will decrease by 1.185%. In models (5) and (7), both the primary and secondary terms of precipitation have reached significant levels. In other words, precipitation has an “inverted U-shaped” nonlinear relationship with the yield of early rice, mid-season-rice and late-season rice. After calculation, the author found that the best inflection point between precipitation and early rice yield is 142.17 mm. The optimal inflection point between precipitation and the yield of mid-season rice and late-season rice is 54.98 mm. This shows that, when the precipitation is low, the increase in precipitation can increase the yield of early rice, mid-season rice and one-season-late rice. However, the increase in precipitation has reached a turning point, and the increase in precipitation will lead to a reduction in production. The reason for this is that a moderate increase in precipitation is beneficial in terms of increasing the number of rice grains per ear and the increase in rice seed setting rate, thereby increasing rice yield. However, when precipitation continues to increase, it will increase the frequency and intensity of extreme events such as heavy rainfall, typhoons, and floods in coastal areas, which is not conducive to rice production. For example, July and August coincide with the maturation of mid-season rice. A super typhoon can destroy the annual rice harvest in coastal areas. In recent years, there has been a decreasing trend in rice production in coastal areas of China, which is closely related to the increase in precipitation in coastal areas to a certain extent [32]. According to the provinces where different types of rice are located, the following conclusions can be further drawn. The increase in precipitation will increase production of early rice in all provinces and also increase the production of mid-season rice and one-season-late rice in Inner Mongolia Autonomous Region, Hebei, Jilin, Heilongjiang, and Shandong Province, but it will also decrease the production of mid-season rice and one-season-late rice in Liaoning, Jiangsu, Anhui, Jiangxi, Henan, Hubei, Hunan and Sichuan Province.

For middle rice and one-season-late rice, Hebei Province, Inner Mongolia Autonomous Region, Jilin Province, Heilongjiang Province, and Shandong Province are all on the left side of the turning point, i.e., with the increase in precipitation, rice yield will increase. Additionally, in Sichuan Province, rice yield will decrease with the increase in precipitation; the reasons for this are that Sichuan Province is located in southwest China, the climate is humid, and precipitation is relatively abundant. In this case, excess precipitation may adversely affect the growth of rice. First of all, excessive rain may lead to the accumulation of water in the fields, making the soil aeration poor, affecting the normal growth and respiration of rice roots. Secondly, too much water can also cause rice plants to grow barren, reducing lodging resistance and increasing the risk of pests and diseases. In addition, the long-term humid environment may also affect the flowering and pollination process of rice, resulting in lower seed setting rate. Therefore, in humid areas such as Sichuan Province, rice yields may decrease as precipitation increases.

4.2.2. The Difference of Temperature on the Yield of Different Types of Rice

It can be seen from

Table 4. The effects of elevated temperature increase on different types of rice.

$\mathbf{P}\mathbf{r}\mathbf{o}\mathbf{v}\mathbf{i}\mathbf{n}\mathbf{c}\mathbf{e}$	$\mathit{R}\mathit{i}\mathit{c}\mathit{e} \mathit{T}\mathit{y}\mathit{p}\mathit{e}$
	$\mathit{E}\mathit{a}\mathit{r}\mathit{l}\mathit{y} \mathit{S}\mathit{e}\mathit{a}\mathit{s}\mathit{o}\mathit{n} \mathit{R}\mathit{i}\mathit{c}\mathit{e}$	$\mathit{M}\mathit{e}\mathit{d}\mathit{i}\mathit{u}\mathit{m} \mathit{R}\mathit{i}\mathit{c}\mathit{e} \mathit{a}\mathit{n}\mathit{d} \mathit{S}\mathit{e}\mathit{a}\mathit{s}\mathit{o}\mathit{n} \mathit{L}\mathit{a}\mathit{t}\mathit{e} \mathit{R}\mathit{i}\mathit{c}\mathit{e}$	$\mathit{D}\mathit{o}\mathit{u}\mathit{b}\mathit{l}\mathit{e} \mathit{S}\mathit{e}\mathit{a}\mathit{s}\mathit{o}\mathit{n} \mathit{L}\mathit{a}\mathit{t}\mathit{e} \mathit{R}\mathit{i}\mathit{c}\mathit{e}$
$Liaoning$
$Hebei$		⚫
$Shandong$
$Jilin$
$Inner Mongolia$
$Jiangxi$
$Hunan$
$Sichuan$	⚫		⚫
$Henan$
$Hubei$
$Jiangsu$
$Anhui$
$Heilongjiang$		⚫

and Table 6 that, in models (4), (6) and (8), a temperature increase has a positive effect on the yield of early rice and double-season-late rice but has an adverse effect on the yield of mid-season rice and one-season-late rice. All passed the 1% significance level test. This shows that, when the temperature rises by 1%, the output of early rice will increase by 12.2034, the output of double-season-late rice will increase by 13.7600, and the output of mid-season rice and one-season-late rice will decrease by 0.431. In models (5), (7), and (9), the first and second terms of temperature have reached significant levels. However, the quadratic coefficients of models (5) and (9) are positive and the quadratic coefficients of (7) are negative. In other words, there is a “ U-shaped” nonlinear relationship between temperature and the yield of early rice and double-season-late rice, and the relationship between temperature and the output of mid-season rice and one-cropping-late rice is an “inverted U-shaped” nonlinear relationship. According to the author’s calculation, the best inflection point between temperature and early rice yield and double-cropping-late rice yield is 14 °C. The best inflection point between temperature and the yield of mid-season rice and late-season rice is 13.64 °C. This shows that, when the temperature is at a low level, the increase in temperature can increase the yield of mid-season rice and one-season-late rice but will reduce the yield of early rice and double-season-late rice. However, when the temperature rises to the turning point, the continued increase in temperature will increase the output of early rice and double-season-late rice but will reduce the yield of mid-season rice and one-season-late rice. The reason is that the threshold for the multiple cropping of mid-season rice and one-season-late rice is relatively low. The rising temperature shortened the production time of mid-season rice and late-season rice and increased the multiple rice cropping index. Eventually, it increased the yield of rice. However, as the temperature continued to rise, the dry weight and ear weight of rice decreased. Eventually it led to a reduction in rice production. Early rice is an early maturing and early maturing rice type, while double-season-late rice itself is a type of rice that is planted and harvested twice on the same land, and the threshold for cropping of the two multiple times is higher. A small increase in temperature will not increase the multiple rice cropping index but will reduce the number of rice tillers and reduce rice yield. However, when the temperature rises at a higher rate and the multiple cropping crosses the threshold, the rice re-ripening index will increase, and the rice yield will increase [33,34]. According to the provinces where different types of rice are located, the following conclusions can be further drawn. The rise of temperature is conducive to the increase in early rice and double-season-late rice production in Sichuan Province but is not for provinces other than Sichuan Province. For mid-season rice and one-season-late rice, rising temperatures are conducive to increasing output in Heilongjiang Province but are not in provinces other than Heilongjiang Province.

For early rice and double-season-late rice, rice yield will increase with the increase in temperature outside Sichuan Province. The reasons are as follows: In Sichuan Province, due to the influence of geographical location, terrain, and climate characteristics, the rice growing areas of the province may still be in a more suitable growing environment under the current temperature. Sichuan Province has a subtropical climate with high temperatures and high humidity, which is generally favorable for crop growth. With the rise of temperature in particular, when the rise is within a certain range (for example, the suitable range of temperature for rice is 20–35 degrees Celsius), such climatic conditions are conducive to the growth and development of early rice and double-season-late rice. The formation of rice yield is mainly the result of the accumulation of photosynthetic products. With the rise of temperature, the efficiency of photosynthesis may be increased, thus increasing the accumulation of photosynthetic products, which is a natural advantage of Sichuan Province.

4.2.3. The Duration of Sunshine Has Different Effects on the Yield of Different Types of Rice

It can be seen from

Table 5. The effects of increasing sunshine duration on different types of rice.

$\mathit{P}\mathit{r}\mathit{o}\mathit{v}\mathit{i}\mathit{n}\mathit{c}\mathit{e}$	$\mathit{R}\mathit{i}\mathit{c}\mathit{e} \mathit{T}\mathit{y}\mathit{p}\mathit{e}$
	$\mathit{E}\mathit{a}\mathit{r}\mathit{l}\mathit{y} \mathit{S}\mathit{e}\mathit{a}\mathit{s}\mathit{o}\mathit{n} \mathit{R}\mathit{i}\mathit{c}\mathit{e}$	$\mathit{M}\mathit{e}\mathit{d}\mathit{i}\mathit{u}\mathit{m} \mathit{R}\mathit{i}\mathit{c}\mathit{e} \mathit{a}\mathit{n}\mathit{d} \mathit{S}\mathit{e}\mathit{a}\mathit{s}\mathit{o}\mathit{n} \mathit{L}\mathit{a}\mathit{t}\mathit{e} \mathit{R}\mathit{i}\mathit{c}\mathit{e}$	$\mathit{D}\mathit{o}\mathit{u}\mathit{b}\mathit{l}\mathit{e} \mathit{S}\mathit{e}\mathit{a}\mathit{s}\mathit{o}\mathit{n} \mathit{L}\mathit{a}\mathit{t}\mathit{e} \mathit{R}\mathit{i}\mathit{c}\mathit{e}$
$Liaoning$	⚫		⚫
$Hebei$	⚫		⚫
$Shandong$	⚫		⚫
$Jilin$	⚫		⚫
$Inner Mongolia$	⚫		⚫
$Jiangxi$	⚫		⚫
$Hunan$	⚫		⚫
$Sichuan$	⚫		⚫
$Henan$	⚫		⚫
$Hubei$	⚫		⚫
$Jiangsu$	⚫		⚫
$Anhui$	⚫		⚫
$Heilongjiang$	⚫		⚫

and

Table 6. Baseline regression 2.

$\mathit{E}\mathit{x}\mathit{p}\mathit{l}\mathit{a}\mathit{n}\mathit{a}\mathit{t}\mathit{o}\mathit{r}\mathit{y}$ $\mathit{V}\mathit{a}\mathit{r}\mathit{i}\mathit{a}\mathit{b}\mathit{l}\mathit{e}\mathit{s}$	$\mathit{E}\mathit{a}\mathit{r}\mathit{l}\mathit{y} \mathit{R}\mathit{i}\mathit{c}\mathit{e}$		$\mathit{M}\mathit{i}\mathit{d}-\mathit{S}\mathit{e}\mathit{a}\mathit{s}\mathit{o}\mathit{n} \mathit{R}\mathit{i}\mathit{c}\mathit{e} \mathit{a}\mathit{n}\mathit{d} \mathit{O}\mathit{n}\mathit{e}-\mathit{S}\mathit{e}\mathit{a}\mathit{s}\mathit{o}\mathit{n}-\mathit{L}\mathit{a}\mathit{t}\mathit{e} \mathit{R}\mathit{i}\mathit{c}\mathit{e}$		$\mathit{D}\mathit{o}\mathit{u}\mathit{b}\mathit{l}\mathit{e}-\mathit{C}\mathit{r}\mathit{o}\mathit{p}\mathit{p}\mathit{i}\mathit{n}\mathit{g} \mathit{L}\mathit{a}\mathit{t}\mathit{e} \mathit{R}\mathit{i}\mathit{c}\mathit{e}$
	$\mathit{M}\mathit{o}\mathit{d}\mathit{e}\mathit{l}\left(4\right)$	$\mathit{M}\mathit{o}\mathit{d}\mathit{e}\mathit{l}$(5)	$\mathit{M}\mathit{o}\mathit{d}\mathit{e}\mathit{l}$(6)	$\mathit{M}\mathit{o}\mathit{d}\mathit{e}\mathit{l}\left(7\right)$	$\mathit{M}\mathit{o}\mathit{d}\mathit{e}\mathit{l}\left(8\right)$	$\mathit{M}\mathit{o}\mathit{d}\mathit{e}\mathit{l}\left(9\right)$
$lnmp$	1.5679 *** (0.4358)	19.0538 ** (8.8423)	−1.185 *** (0.0986)	12.0716 *** (0.7848)	1.6721 *** (0.3891)	6.5468 (8.1385)
$lnmat$	12.2034 *** (1.2117)	−198.741 *** (25.1109)	−0.431 *** (0.0577)	1.4631 *** (0.2149)	13.7604 *** (1.0403)	−211.71 *** (24.0332)
$lnacl$	0.0931 (0.4510)	145.2960 *** (18.5201)	−0.0677 (0.0825)	−32.041 *** (2.6005)	−0.0706 (0.4022)	128.92 *** (17.0529)
$lnmp$2		−1.9218 ** (0.9503)		−1.5065 *** (0.0905)		−0.5867 (0.8752)
$lnmat$2		37.6559 *** (4.6547)		−0.5418 *** (0.0623)		40.5043 *** (4.4476)
$lnacl$2		−6.9549 *** (0.8806)		1.4830 *** (0.1215)		−6.2059 *** (0.8117)
$lnrlf$	1.8560 *** (0.4033)	1.2226 *** (0.3244)	0.5539 *** (0.0702)	0.5036 *** (0.0547)	2.3998 *** (0.3447)	1.8419 *** (0.2821)
$lnrcf$	−0.5799 * (0.3201)	−0.2485 (0.2534)	0.7596 *** (0.0669)	0.3571 *** (0.0547)	−0.4335 (0.2924)	−0.1491 (0.2350)
$lnrm$	0.0265 (0.3259)	−0.0815 (0.2838)	−0.0001 (0.0825)	0.2891 *** (0.0626)	1.0836 *** (0.2797)	1.0982 *** (0.2475)
$lnri$	−1.7568 *** (0.5996)	−1.3394 ** (0.5346)	−0.301 *** (0.0861)	−0.1629 ** (0.0662)	−3.3207 *** (0.4694)	−3.1181 *** (0.4382)
$lnrtp$	−0.3351 (0.6375)	−0.9560 * (0.5617)	−0.053 *** (0.0172)	−0.0257 (0.0186)	0.4880 (0.5791)	−0.2716 (0.5199)
$pp1$	−0.2927 (0.3291)	−0.0396 (0.2732)	0.0700 (0.0800)	0.1703 *** (0.0588)	−1.4196 *** (0.2821)	−1.1512 *** (0.2408)
$pp2$	0.5811 * (0.3114)	0.4615 * (0.2531)	0.2332 *** (0.0786)	0.0278 (0.0583)	0.0159 (0.2752)	−0.2387 (0.2260)
$\_cons$	−32.775 *** (6.6165)	−533.77 *** (91.4127)	9.6283 *** (1.1585)	150.5518 *** (13.7659)	−36.216 *** (5.8733)	−400.95 *** (84.3389)
$N$	219	219	533	533	233	233
$R-sq$	0.720	0.829	0.865	0.929	0.792	0.869

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

that, in models (4), (6) and (8), the increase in sunshine duration has a positive impact on the yield of early rice but it has a negative impact on the yield of mid-season rice, one-season-late rice and double-season-late rice. This shows that, when the sunshine duration increases by 1%, the yield of early rice will increase by 0.0931, the yield of mid- season rice and one-season-late rice will decrease by 0.0677, and the yield of double-season-late rice will decrease by 0.0706. In models (5), (7) and (9), the first and second terms of sunshine duration reached significant levels. However, the coefficients of the quadratic terms in models (5) and (9) were negative, and the coefficient of the quadratic term of model (7) is positive. In other words, the sunshine duration has an “inverted U-shaped” nonlinear relationship with the yield of early rice and double-season-late rice and a “U-shaped” nonlinear relationship with the yield of mid-season rice and single-season-late rice. The author found that the optimal inflection point between sunshine duration and early rice yield is 34,406.47 h, the optimal inflection point with mid-season rice and one-season-late rice yield is 32,435.22 h, and the optimal inflection point with double-season-late rice yield is 32,435.22 h. This shows that, when the sunshine duration is lower, the increase in sunshine duration can increase the yield of early rice and double-season-late rice, but it will reduce the yield of mid-season rice and one-season-late rice. When the sunshine time reaches the turning point, the continued increase in sunshine time will lead to reduced yields of early rice and double-cropping rice, but it will also increase the middle-season rice and late rice production. The reason for this is that the sowing seasons of early rice and double-season-late rice are in spring and autumn, and the sunshine duration is less than that of mid-season rice and single-season-late rice in the summer of the sowing growth period, which is not conducive to rice heading. If the duration of sunshine is appropriately increased, it is beneficial to increase the heading rate of the two types of rice. However, when the sunshine duration is sufficient during the sowing and growing seasons of mid-season rice and one-season-late rice, increasing the sunshine duration will cause the rice to evaporate too quickly, which will easily lead to dry rice grains and other conditions, which is not conducive to the increase in rice production. When the sunshine duration is increased too much, although it coincides with the young panicle differentiation period of mid-season rice and one-season-late rice, it will increase the amount of rice panicle differentiation and increase rice yield, but it will also cause the accelerated evaporation of water in early rice and double-season-late rice, resulting in insufficient water supply and reduced production of rice [25,27]. According to the provinces where different types of rice are located, the following conclusions can be further drawn. The longer duration of sunshine will increase the output of early rice and double-season-late rice in all provinces; however, it will not increase the output of mid-season rice and one-season-late rice in all provinces in the main grain-producing areas.

5. Conclusions

This study utilizes a vast dataset comprising 470,844 monthly observations from 319 weather stations spanning the period of 1978–2018. These observations are leveraged to construct key climate indicators, including precipitation, temperature, and daylight duration. By combining panel data from 13 provinces in China’s primary grain-producing regions with the development of an “economy-climate” model, econometric methods are employed to probe the impact of climate change on rice yields and various rice types. The findings underscore several key insights. Firstly, the relationship between precipitation, temperature and sunshine duration and total rice production in China is an inverse-U-shaped nonlinear relationship. The effect of these variables on different rice types varies from region to region. For example, while increased precipitation usually increases rice production in provinces such as the Inner Mongolia Autonomous Region and Hebei, it leads to a decline in rice production in provinces such as Liaoning and Jiangsu. Similarly, the relationship between temperature and different rice types was nonlinear. Rising temperatures may increase early rice and two-season-late rice yields in Sichuan Province but adversely affect rice yields in other areas. In addition, the effect of daylight duration on rice yield varies by region and type of rice. While longer daylight hours generally increase rice yields, the relationship is U-shaped in middle and late rice.

In order to meet these challenges and ensure China’s food security, measures must be taken according to local conditions. First of all, the construction of a climate monitoring system should be strengthened, and a more complete climate monitoring system should be established nationwide to monitor climate change trends and extreme climate events in real time and accurately. Secondly, in regard to the implementation of water resource protection and management strategy, in response to the impact of climate change on water resources, the government has adopted a series of measures, such as the establishment of a water resources management system and strengthening water resources protection laws and regulations, to ensure the sustainable use of water resources. Thirdly, to promote the use of renewable energy, the government should introduce more incentive policies, such as tax breaks, subsidies, etc., to encourage enterprises and individuals to use renewable energy, such as solar energy, wind energy, etc., in order to reduce dependence on fossil fuels and reduce greenhouse gas emissions. At present, China has a certain capacity of climate monitoring and forecasting, which lays a good foundation for further improving the climate monitoring system. At the same time, our government has been committed to promoting the use of renewable energy and has achieved remarkable results. However, there are still many potential challenges, such as the construction of a climate monitoring system (which requires a large amount of investment and technical support and long-term maintenance and renewal) and the promotion of the use of renewable energy needs to solve the cost, technology, and market issues, among others. Zhejiang Province, for example, has achieved remarkable results in its climate adaptation strategy. In order to implement the “National Strategy for Adaptation to Climate Change 2035”, Zhejiang Province issued the “Guidelines for the Preparation of Provincial Action plans for Adaptation to Climate Change” and formulated a series of climate adaptation policies and action plans. These include measures to strengthen climate monitoring and early warning systems, promote renewable energy and energy-saving technologies, implement water resource protection and utilization plans, and strengthen ecosystem protection and restoration. The implementation of these policies and actions has effectively improved the climate adaptation capacity of Zhejiang Province and mitigated the impact of climate change on local economic and social development.