Climate Change, Biased Technological Advances and Agricultural TFP: Empirical Evidence from China

Abstract

The impact of climate change on agricultural quality development under the constraint of China’s “Double Carbon” target has been widely discussed by policy practitioners and academic theorists. This paper attempts to deconstruct the logic of how climate change affects agricultural total factor productivity (TFP) in three dimensions—the structure of agricultural input factors, the change in the cropping system, and the stability of crop supply. This paper also reveals the mechanism through which biased technological progress increases agricultural TFP by weakening the magnitude of climate change and empirically tests it by using China’s provincial-level data from 2000 to 2021. This study showed that average annual temperature and annual precipitation had significant negative effects on agricultural TFP, that the number of sunshine hours had a significant positive effect on agricultural TFP, and that obvious regional differences existed in the effect of climate change on agricultural TFP. Further mechanism tests revealed that biased technological progress positively moderated the effect of climate change on agricultural TFP. Based on these findings, the appropriate countermeasures for improving climate early warning mechanisms, promoting the progress of appropriate technology, and fostering new agricultural management bodies.

1. Introduction

The Climate Change 2022: Impacts, Adaptation, and Vulnerability report, published by the United Nations Intergovernmental Panel on Climate Change (IPCC), states that the dangers that climate change is posing to the natural world are seriously affecting the lives of the world’s people. As global average temperatures continue to rise, regional weather becomes more extreme, precipitation characteristics continue to change, the world’s food security, poverty, and displacement problems worsen [1], economic, social, and health crises are further aggravated, and the number of people living in poverty as a result of changes in the climate environment continues to rise [2]. In recent years, climate change, which is characterized mainly by warming, has become an indisputable phenomenon and a serious challenge facing the entire globe. According to the IPCC, global average temperatures are projected to increase by 3.5–5 °C by 2100 (Climate Change 2022: Impacts, Adaptation, and Vulnerability. https://www.ipcc.ch/report/ar6/wg2/, accessed on 14 March 2023), precipitation in wet regions will decrease, and the intensity and frequency of climate extremes will increase. The dependence of agricultural production processes on the climate environment means that agriculture is the sector most directly vulnerable to climate impacts [3,4]. Notably, climate change poses increasing external environmental risks to agricultural production systems [5]. Uncertainties in climate change megatrends will directly contribute to a severe reduction in food production, which in turn will exacerbate the urgency of the food security situation [6,7].

Climate change poses a serious challenge to ensuring the sustainability of agricultural development, on which humankind depends [8,9]. According to relevant data, by 2030, the output of Chinese plantation industry may be reduced by approximately 5% to 10% overall as a result of global warming (https://www.cma.gov.cn/2011xzt/2012zhuant/20121119/2012111913/201211/t20121123_3105487.html, accessed on 25 March 2023); furthermore, the three major crops of wheat, rice and maize will experience the largest reductions in output. By the middle of the twenty-first century, the yields of several crops, such as wheat, rice, and corn, will have declined further, pests and diseases will have worsened, the effectiveness of fertilizers and water will have been reduced, the amount of fertilizer and irrigation water used in agriculture will have increased, and the cost of production will have risen [10,11]. These situations amply demonstrate that climate change is severely impacting food security. China is a largely agricultural country in which climate change continues to somewhat alter the spatial and temporal distributions of temperature and precipitation, increasing the frequency and intensity of extreme events, such as heavy rainfall, floods, droughts, and outbreaks of pests and diseases [12,13]. However, China’s reliance on a high-input and high-pollution agricultural development model has enabled it to feed 22% of the world’s population on only 7% of the world’s land but at a large resource and environmental cost.

In the future, Chinese agriculture will need to shift to a sustainable development model that is efficient, resource-saving, and environmentally friendly [14,15,16]. The key to maintaining sustained economic growth in agriculture lies in increasing agricultural total factor productivity (TFP) [17,18]. Therefore, this study considers three specific conflicts, i.e., the shortage of farmland, the deterioration of food production conditions, and the increase in food demand, as well as three challenges: resources and environmental constraints, price and cost constraints, and aggravated climatic conditions [19]. These factors are important in the study of TFP in Chinese agriculture from the perspective of climate change. Furthermore, biased technological advances should be included in the existing research framework to increase TFP in agriculture. These advances, in the context of climate change responses, can address agricultural constraints and improve agricultural productivity by mitigating the limitations of agricultural households. This framework also has important practical and theoretical value for promoting climate-resilient development, guaranteeing food security, and realizing high-quality agricultural production transformation.

2. Literature Review

Climate change is of significant concern to academics and governments. The literature has analyzed the following aspects of this topic:

First, we address the impact of climate change on TFP in agriculture. There is an academic debate on whether climate change will have a negative impact on agricultural production. One viewpoint believes that climate change has a positive effect on agricultural TFP, and another viewpoint believes that climate change has a negative effect on agricultural TFP. Joshi et al., (2022) studied the effect of climate change on agricultural TFP in the USA and found that annual precipitation has a positive effect on agricultural TFP, but precipitation density has a significant negative effect on agricultural TFP in most parts of the U.S [20]. However, Kumar and Maiti (2024) showed that rising temperatures significantly reduce agricultural TFP, with agricultural TFP decreasing by 3.22% for every 1-degree increase in temperature [21]. Ahmed et al. (2022) showed that annual changes in moderate precipitation play a negative and significant role in crop yields and that agricultural technological progress is the main driving force promoting agricultural TFP [22]; however, integrated climate change has a negative impact on agricultural TFP in the southern United States [23]. Moreover, the impact of climate change on agricultural TFP is regional and seasonal, especially in the USA and China, where the longitudinal and latitudinal spans are the most obvious. Ogundari and Onyeaghala (2021) analyzed the impact of climate change on agricultural TFP through the use of relevant data from the African region from 1981–2010 and explained it from regional and seasonal levels [24]. This result is more consistent with the real development situation.

Second, we conducted research on the impact of agricultural technological progress on agricultural TFP. Agricultural TFP is the core driving force of development in modern agriculture. In light of agricultural production factors, agricultural technological progress addresses the spatial and temporal limitations of traditional agricultural production [25,26] through digital agriculture, intelligent agriculture, greenhouse agriculture, and other modern agricultural technologies [27], thus, enabling the traditional mode of agricultural production to overcome the drawbacks of traditional small-scale agricultural production [28]. This process can realize the vertical specialization of the division of labor in agricultural production. It also encourages farmers to use their relevant agricultural knowledge to make smarter decisions regarding the rational allocation of input factors such as capital, labor, and land, thereby optimizing the input structure of factors [29] and improving technical efficiency [30,31]. Moreover, the progress of agricultural technology has given rise to more advanced agricultural production machinery [32] and efficient chemical agricultural materials [33], which improve agricultural production efficiency and agricultural machinery [34]. These actions lead to precise agricultural labor production regarding intelligent spraying and spreading of pesticides and fertilizers [35,36], thus, improving agricultural resource use efficiency while simultaneously reducing agricultural operating costs [37]. In addition, technological progress in agriculture not only increases agricultural machinery use but also enables agricultural production to become standardized [38,39] and specialized through agricultural technology training and education, thereby increasing agricultural labor productivity, which in turn contributes to the TFP of agriculture [40].

In summary, few studies have analyzed the impact of agricultural TFP by incorporating both climate change and agricultural technological progress into the research framework. Therefore, the potential marginal contributions of this paper are as follows: first, with the help of panel data from 31 provinces from 2000 to 2021, we measure and decompose China’s agricultural TFP by using the serial SGM-DEA model and explore the impacts of climatic factors (average temperature, annual precipitation, and hours of sunshine) on agricultural TFP. These impacts have important theoretical value and practical significance for reasonably evaluating and analyzing the growth of agricultural productivity in China under climate change. Second, the study clarifies the logic of the impact of climate change on agricultural TFP from the three dimensions of the structure of agricultural input factors, the change in planting system, and the stability of crop supply and reveals the mechanism through which biased technological progress increases agricultural TFP by weakening the magnitude of climate change, thus, providing an opportunity for China to improve the efficiency of agricultural production and overcome the impact of climate on agricultural development. Practical policy recommendations for China are proposed to guarantee national food security and promote sustainable agricultural development.

3. Mechanism Analysis

3.1. Mechanism of Climate Change Impacts on Agricultural TFP

(1)

Climate change affects the input structure of agricultural factors for production

As shown in Figure 1, on the one hand, factors such as land, labor, and capital participate in the agricultural production process as traditional production input factors, and climate change indirectly changes the traditional input structure of production factors by affecting farmers’ planting behavior [41]. To effectively cope with the adverse effects of climate change on crops, farmers adjust their planting behavior according to the principle of maximizing returns, which changes the input factor structure in the production process and increases the cost of planting crops [42]. On the other hand, climate change exacerbates pests and diseases, leading to an increase in the use of fertilizers, pesticides, and other input factors indispensable to the growth process of crops [43], thus, increasing the cost of inputs in the production process of crops and changing the cost structure of production input factors [44]. In addition, extreme weather conditions, such as high temperatures, can cause discomfort for farmers engaging in agricultural labor, which may reduce the input of labor factors and decrease their labor capacity. In other words, climate change can cause serious losses in agricultural production systems [45], leading to increases in the management costs of human capital and the production costs of other input factors, which ultimately affect the growth of TFP in agriculture.

(2)

Crop planting system changes due to climate change

Climate change alters the climate resource endowment structure of the crop-growing environment [46]; specifically, warming conditions increase the cumulative temperature during the crop-growing period, which is conducive to the expansion of crop planting areas and replanting indices, which further extends the crop-growing period, planting boundaries and other changes [47]. According to related research, by 2050, most of the two-maturing-a-year regions in China are likely to become three-maturing-a-year regions, causing the migration of planting system boundaries. Climate change will also have an impact on crop fertility, such as by shortening the fertility period of grain crops such as rice and wheat, resulting in a decrease in yield [48]. Extreme heat accelerates the rate of evaporation of preserved water and loss of nutrients from arable land, indirectly affecting the territorial conditions of agricultural production by reducing land productivity [49]. Moreover, light, heat, water, and other climatic elements provide the necessary energy for crop growth and development. Climate change through different combinations of climatic elements affects the physiological adaptability of crops during the growing period and changes the suitability of the climate conditions required for crop growth, which in turn produces the long-term effect of changes in planting varieties in the region, which further leads to changes in the layout of agricultural production in China. For instance, the planting range of rice in China has gradually shown a northwards trend. Therefore, climate change will affect crop planting systems, leading to the adjustment of agricultural structure and function [50], causing changes in factor inputs and outputs in the agricultural production process [51], and ultimately affecting the growth of TFP in agriculture.

(3)

Chain reactions in the climate environment threaten the stability of crop supplies

The frequency and intensity of extreme climatic events have shown an increasing trend, and the adverse effects of climate change on agricultural production have gradually emerged [52,53]. In the past 30 years, both the disaster area and the rate of disaster have shown obvious increasing trends, and the overall number of agrometeorological disasters in China has been increasing. The frequent occurrence of extreme weather events directly leads to damage in agricultural production, which is mainly reflected in the drastic reduction in crop output, food, and other crop production, further resulting in an increase in the crop production gap caused by crop market price fluctuations and exacerbating the tension of food security in China [54]. Climate change has led to increased vulnerability of agricultural production, and the irreversibility of such impacts has made production subject to greater risk and uncertainty in the future [55]. Moreover, the adverse impacts of climate change on farming are becoming increasingly evident, as climate change affects livestock metabolism, feed intake, and mortality by reducing the quality of livestock feed, which ultimately affects the yield and quality of livestock products [56,57]. Climate disasters can have a significant impact on the effective output of agriculture and can also have a direct impact on TFP growth.

3.2. Internal Logic of Biased Technological Progress in Weakening the Impact of Climate Change on Agricultural TFP

Theoretically, technological progress can optimize the structure of agricultural production, which is conducive to mitigating the adverse impact of climate change on agricultural TFP [58]. However, the role of technological progress in coping with climate change needs to be established under certain preconditions; policymakers should be able to accurately judge the reasons why climate change affects agricultural TFP based on factor endowments in the region or targets ways to improve agricultural TFP. However, the latter decision may be difficult to realize because of management costs, information asymmetry, and other problems. Due to the vastness of China’s geographical area, there is spatial variability in the impact of climate change on agricultural production [59], and the existing agricultural technologies in China lack effectiveness in addressing climate change in a holistic and coordinated manner. Additionally, the role of appropriate biased agricultural technologies in the growth of agricultural TFP is undeniable, as these technologies can effectively reduce the cost of agricultural production and increase agricultural output [60].

The biased technological progress exerts a pronounced influence on the magnitude of agricultural TFP, fundamentally altering the comparative remuneration structure associated with agricultural output, which subsequently modulates the motivational landscape for farmers to engage in crop cultivation. Refinements in crop varietals, coupled with advancements in cultivation techniques, not only broaden the spectrum of viable crop options but also significantly impact the spatial allocation of cultivated land, thereby altering the extent of area sown to various crops.

The biased technological progress encapsulates a pivotal transmission mechanism in mediating the influence of climate change on agricultural TFP. First, biased technological progress enhances the resilience of crop species to climatic variabilities. By fostering the development and dissemination of crop varieties that exhibit tolerance to adverse conditions, biased technological progress mitigates the detrimental effects of extreme weather events on agricultural output. For instance, drought-resilient crop strains sustain higher yields amidst drought conditions, whereas flood-tolerant varieties minimize crop losses during inundation events. The cultivation and implementation of such crop varieties have partially decoupled agricultural production from climate-driven fluctuations, thereby bolstering the stability and sustainability of agricultural systems [61]. Second, there are biased technological advances that have improved farm water and fertilizer management. Through the application of technologies such as precision irrigation soil testing and formula fertilizer application, farmers can scientifically and reasonably adjust water and fertilizer inputs according to the needs of crop growth and changes in climatic conditions. This not only improves the efficiency of water and fertilizer use and reduces resource wastage, but also improves the soil environment and enhances the soil’s ability to retain water and fertilizer. Under unfavorable climatic conditions, these technologies can help farmers stabilize yields and even achieve yield increases. In addition, there are biased technological advances that have also promoted the transformation of agricultural production methods. With the development of agricultural mechanization and intelligence, agricultural production has gradually achieved scale, standardization, and intelligence. Modern production methods can not only improve production efficiency and reduce production costs, but also reduce the impact of human factors on agricultural production. In the case of regional differences in climate endowment, appropriate production methods can adapt to climate change faster and maintain the stability and continuity of agricultural production [62]. Finally, there are biased technological advances that promote the optimization of agro-ecosystems. Through the promotion of eco-agriculture, circular agriculture, and other models, agricultural technology can optimize the structure and function of agroecosystems and improve the stability and resilience of ecosystems. Under unfavorable climatic conditions, these models can mitigate the impact of the climate environment on agricultural production through the self-regulating capacity of the ecosystem and achieve the sustainable development of agriculture.

4. Model, Method and Data

4.1. Model and Method

Based on the above description of the relationships between climate change, biased technological progress, and agricultural TFP, this paper attempts to construct an extended Cobb–Douglass production function model, which lays the foundation for an extended discussion of the impacts of climate change and biased technological progress on agricultural TFP. The agricultural production process is assumed to satisfy the Cobb–Douglass production function form and the climate factor is introduced into the function as an exogenous variable:

$$\mathrm{F}=\mathrm{A}\left(\mathrm{C}\mathrm{l}\mathrm{i},\mathrm{T}\mathrm{e}\mathrm{c}\right){\mathrm{L}}^{\mathsf{\alpha }}{\mathrm{K}}^{\mathsf{\beta }}{\mathrm{T}}^{\mathsf{\gamma }}{\mathrm{E}}^{\mathsf{\theta }\mathrm{C}\mathrm{l}\mathrm{i}}$$

(1)

where $\mathrm{F}$ is the agricultural output; $\mathrm{A}$ is the agricultural TFP, which is a function of climate change $\mathrm{C}\mathrm{l}\mathrm{i}$ and biased technological progress $\mathrm{T}\mathrm{e}\mathrm{c}$; L, K and T represent the inputs of labor, capital, and land in agricultural production, respectively; and $\mathsf{\alpha },\mathsf{\beta }$ and $\mathsf{\gamma }$ are the elasticity coefficients of the outputs of labor, capital, and land, respectively, and satisfy $\mathsf{\alpha }+\mathsf{\beta }+\mathsf{\gamma }=1$.

First, the impact of climate change on agricultural TFP can be discussed and expressed through Equation (2).

$${\displaystyle \frac{\mathrm{d}\mathrm{A}}{\mathrm{d}\mathrm{C}\mathrm{l}\mathrm{i}}}={\displaystyle \frac{𝜕\mathrm{A}}{𝜕\mathrm{C}\mathrm{l}\mathrm{i}}}+{\displaystyle \frac{𝜕\mathrm{A}}{𝜕\mathrm{T}\mathrm{e}\mathrm{c}}}·{\displaystyle \frac{𝜕\mathrm{T}\mathrm{e}\mathrm{c}}{𝜕\mathrm{C}\mathrm{l}\mathrm{i}}}$$

(2)

The impact of climate change on agricultural TFP depends on two factors. The first component is the direct effect of climate change on agricultural TFP, and the second component is the indirect effect of climate change on agricultural TFP through biased technological progress. The direct effect of climate change on agricultural TFP may have different impacts depending on geographical differences; for example, increased precipitation in southern China will increase the risk of agricultural disasters, but increased precipitation in northwestern China may improve agricultural output. However, in terms of overall impacts, the literature finds that the direct effect of climate change on agricultural TFP is negative; that is, ${\displaystyle \frac{𝜕\mathrm{A}}{𝜕\mathrm{C}\mathrm{l}\mathrm{i}}}<0$. The indirect effect of climate change on agricultural TFP is ${\displaystyle \frac{𝜕\mathrm{A}}{𝜕\mathrm{T}\mathrm{e}\mathrm{c}}}·{\displaystyle \frac{𝜕\mathrm{T}\mathrm{e}\mathrm{c}}{𝜕\mathrm{C}\mathrm{l}\mathrm{i}}}$. Among them, theoretically biased technological progress can effectively increase agricultural TFP to satisfy ${\displaystyle \frac{𝜕\mathrm{A}}{𝜕\mathrm{T}\mathrm{e}\mathrm{c}}}>0$. Moreover, biased technological progress can weaken the adverse effects of climate change on agricultural TFP ${\displaystyle \frac{𝜕\mathrm{T}\mathrm{e}\mathrm{c}}{𝜕\mathrm{C}\mathrm{l}\mathrm{i}}}>0$. Therefore, the indirect effect of climate change on agricultural TFP ${\displaystyle \frac{𝜕\mathrm{A}}{𝜕\mathrm{T}\mathrm{e}\mathrm{c}}}·{\displaystyle \frac{𝜕\mathrm{T}\mathrm{e}\mathrm{c}}{𝜕\mathrm{C}\mathrm{l}\mathrm{i}}}$ is positive.

Second, a benchmark regression model is constructed based on the derivation of the theoretical model. Promoting agricultural TFP is a timely and inevitable choice for promoting high-quality agricultural development, and agricultural production is affected by many factors, such as climate change and technological progress. Based on the research of Huffman and Evenson (2006) [63], the following benchmark regression model is constructed according to Equation (3):

$$\mathrm{l}\mathrm{n}{\mathrm{A}\mathrm{g}\mathrm{r}\_\mathrm{T}\mathrm{F}\mathrm{P}}_{\mathrm{i}\mathrm{t}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{C}\mathrm{l}\mathrm{i}}_{\mathrm{i}\mathrm{t}}+\mathsf{\gamma }\sum {\mathrm{x}}_{\mathrm{i}\mathrm{t}}+{\mathrm{u}}_{\mathrm{t}}+{\mathsf{\omega }}_{\mathrm{i}}+{\mathsf{\mu }}_{\mathrm{i}\mathrm{t}}$$

(3)

In Equation (3), $\mathrm{A}\mathrm{g}\mathrm{r}\_\mathrm{T}\mathrm{F}\mathrm{P}$ denotes the agricultural $\mathrm{T}\mathrm{F}\mathrm{P}$ of the province (urban area) $\mathrm{i}$ in year $\mathrm{t}$; ${\mathrm{C}\mathrm{l}\mathrm{i}}_{\mathrm{i}\mathrm{t}}$ is the climatic variable of the province (urban area) $\mathrm{i}$ in year $\mathrm{t}$, including the average annual temperature, sunshine hours, and annual precipitation; ${\mathrm{B}\mathrm{T}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$ is the biased technological progress of province (urban area) $\mathrm{i}$ in year $\mathrm{t}$; ${\mathrm{x}}_{\mathrm{i}\mathrm{t}}$ denotes the set of control variables, including rural electric power facilities, the agricultural structure, facility agriculture, per capita net income of rural residents, and rural road density; ${\mathsf{\beta }}_0$ is the parameter to be estimated; ${\mathrm{u}}_{\mathrm{t}}$ and ${\mathsf{\omega }}_{\mathrm{i}}$ denote the fixed effects of time and each province (urban area), respectively; and ${\mathsf{\mu }}_{\mathrm{i}\mathrm{t}}$ is the random error term.

On this basis, this paper further examines whether biased technological progress can weaken the impact of climate change on agricultural $\mathrm{T}\mathrm{F}\mathrm{P}$, i.e., whether climate change affects agricultural $\mathrm{T}\mathrm{F}\mathrm{P}$ by moderating biased technological progress. Based on Equation (4), the benchmark regression model, the moderating effect model is constructed. The details are as follows:

$$\begin{gathered} \mathrm{l}\mathrm{n}{\mathrm{A}\mathrm{g}\mathrm{r}\_\mathrm{T}\mathrm{F}\mathrm{P}}_{\mathrm{i}\mathrm{t}}={\mathsf{\beta }}_0+{\mathsf{\beta }}_1{\mathrm{C}\mathrm{l}\mathrm{i}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_2{\mathrm{B}\mathrm{T}\mathrm{P}}_{\mathrm{i}\mathrm{t}}+{\mathsf{\beta }}_3{\mathrm{C}\mathrm{l}\mathrm{i}}_{\mathrm{i}\mathrm{t}}\times {\mathrm{B}\mathrm{T}\mathrm{P}}_{\mathrm{i}\mathrm{t}} \\ +\mathsf{\gamma }\sum {\mathrm{x}}_{\mathrm{i}\mathrm{t}}+{\mathrm{u}}_{\mathrm{t}}+{\mathsf{\omega }}_{\mathrm{i}}+{\mathsf{\mu }}_{\mathrm{i}\mathrm{t}} \end{gathered}$$

(4)

where ${\mathrm{B}\mathrm{T}\mathrm{P}}_{\mathrm{i}\mathrm{t}}$ is the moderating variable biased technological progress, ${\mathsf{\beta }}_3$ denotes the key coefficient used to test the moderating effect, and the remaining variables have the same meaning as in the baseline regression model.

4.2. Variable Selection

(1)

Dependent variable

Agricultural TFP (Agr_TFP) refers to the contribution rate of other factor inputs to the growth of agricultural output excluding traditional input factors such as land, labor, and capital. The core of this paper is to examine the impact of climate change on agricultural TFP; accurately measuring the agricultural TFP of each province is the basis of this paper. The Malquist-Luenberger (ML) and Global Malquist-Luenberger (GML) are widely used in panel data efficiency research. The ML index is based on the directional distance function, and the results obtained are often not cyclic in the rate of change of the latter period relative to the former period and often suffer from the problem of unsolvable linear programming. Traditionally, agricultural TFP is measured by the ML index, but there is no solution for linear programming and the index is not transferable. The GML index is based on the construction of a global production technology set, which can effectively avoid the shortcomings of linear programming and the “technological regression” phenomenon. Because the reference of each period is the same global frontier, the GML index is transferable. Therefore, the study chooses the GML index as the measurement method of agricultural TFP. In addition, this paper adopts the non-radial, non-angular, slacks-based (slacks-based measure, SBM) efficiency evaluation model proposed by Tone (2001) [64], which can incorporate slack variables into efficiency evaluation. Based on this, the GML index is constructed according to the non-radial, non-angular SBM directional distance function, which is called the SGM productivity index, and decomposed into technical efficiency change (SEC) and technical change (STC) under the condition of constant returns to scale.

$$\mathrm{S}\mathrm{G}\mathrm{M}\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1};{\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\right)={\displaystyle \frac{\mathrm{S}\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1}\right)}{\mathrm{S}\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\right)}}={\displaystyle \frac{{\mathrm{S}}^{\mathrm{t}+1}\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1}\right)}{{\mathrm{S}}^{\mathrm{t}}\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\right)}}\times \left({\displaystyle \frac{\mathrm{S}\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1}\right)}{{\mathrm{S}}^{\mathrm{t}+1}\left({\mathrm{x}}^{\mathrm{t}+1},{\mathrm{y}}^{\mathrm{t}+1}\right)}}\times {\displaystyle \frac{{\mathrm{S}}^{\mathrm{t}}\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\right)}{\mathrm{S}\left({\mathrm{x}}^{\mathrm{t}},{\mathrm{y}}^{\mathrm{t}}\right)}}\right)=\mathrm{S}\mathrm{E}\mathrm{C}\times \mathrm{S}\mathrm{T}\mathrm{C}$$

(5)

In this paper, we use MaxDEA software 5.2 to measure the SGM index under the condition of constant returns to scale and transform it into a cumulative growth index with 2000 as the base period as the explanatory variable of the empirical analysis model. and uses data for a panel of 31 provincial units from 2000–2021. Agricultural output variables are expressed as gross agricultural product at constant prices in 2000. The agricultural input variables include six factors: land, labor, agricultural machinery, fertilizer use, irrigated area, and agricultural water use. Among them, land input is calculated by the sown area of crops; labor input is calculated by the number of employees in agriculture, forestry, animal husbandry and fishery; agricultural machinery input is calculated by the total power of agricultural machinery; fertilizer use is calculated by the actual amount of discounted pure quantity; irrigated area is calculated by the actual effective irrigated area each year; and agricultural water use input is calculated by the amount of water used by agriculture in each province.

(2)

Independent variables

Climate change is an important concept in climatology and a hot topic of international discussion in recent years; furthermore, its terminology in the field of climate and agricultural research is becoming more complex. Climate change is defined as a statistically significant change in the mean state of the climate or a change in weather that lasts for a longer period. The United Nations Framework Convention on Climate Change (UNFCCC) defines climate change as a change in the state of the climate over a period caused by human activities that directly or indirectly alter the composition of the atmosphere. The IPCC considers climate change as a change in climatic conditions over time, whether caused by the natural environment or human activities. Combined with existing academic research, climate change is a change in the long-term average trend of climate elements (e.g., precipitation, average temperature, sunshine hours, etc.); the greater the magnitude of climate change is, the greater the risk of unstable climate conditions and the greater the sensitivity of the environment to climate change. Drawing on the relevant research of Yin et al. (2016) [65], this paper selects the average annual temperature (AAT), annual precipitation (AP), and sunshine duration (SD) as the specific characteristics of climate change; the data of each province (urban area) are the average of the monitoring data of all the meteorological stations in that jurisdiction. The average annual temperature is the arithmetic mean of the average daily temperature at all meteorological stations in the province (urban area); sunshine duration refers to the length of time that the sun’s rays irradiate the ground in a year; and annual precipitation is the average of the total annual precipitation at all meteorological stations in the province (urban area).

(3)

Moderator variable

Addressing the constraints of factors such as land and labor on agricultural production is a basic feature of agricultural modernization and agricultural technological progress. Therefore, this paper uses labor productivity and land productivity indicators to construct a biased technological progress index to further elaborate on the impact of regional factor endowments on TFP in agriculture. $\mathrm{Y}$ denotes the agricultural output, $\mathrm{L}$ is the labor input, and $\mathrm{A}$ is the land (sown area) input; the biased technological progress index ($\mathrm{B}\mathrm{T}\mathrm{P}$) can be expressed as follows:

$${\mathrm{B}\mathrm{T}\mathrm{P}}_{\mathrm{i}}={\displaystyle \frac{\left({\mathrm{Y}}_{\mathrm{i}}/{\mathrm{L}}_{\mathrm{i}}\right)/\left({\mathrm{Y}}_{\mathrm{i}-1}/{\mathrm{L}}_{\mathrm{i}-1}\right)}{\left({\mathrm{Y}}_{\mathrm{i}}/{\mathrm{A}}_{\mathrm{i}}\right)/\left({\mathrm{Y}}_{\mathrm{i}-1}/{\mathrm{A}}_{\mathrm{i}-1}\right)}}$$

(6)

(4)

Control variables

In addition to climate change and agricultural technology, spatial changes in agricultural production areas due to markets, natural environments, and policies, as well as factors such as the substitution of agricultural products, infrastructure, and service systems, are likely to affect TFP in agriculture. Among them, public infrastructure plays an important role in mitigating the adverse effects of climate change and promoting new agricultural technologies. To avoid missing data, in this paper, we add socio-economic variables such as rural electric power facilities, agricultural structure, facility agriculture, per capita net income of rural residents, and rural road density as control variables in the empirical model.

Rural electricity facilities (REF). Rural electricity consumption is taken as a proxy variable for rural electric power facilities. With the advancement of agricultural production scales and digitalization, electric power facilities have become important guarantees for rural development and sources of power for improving agricultural TFP.

Agricultural structure (AS). The structure of agriculture is expressed as the percentage of area sown to grain over the total area sown to crops. Agricultural restructuring indicates the development of cropping structures in the direction of factor endowment advantages.

Facility agriculture (FA). This paper uses agricultural production equipment inputs to indicate the situation of facility agriculture. The development of facility agriculture is an important symbol of agricultural modernization. Greater levels of agricultural production equipment inputs support the progress and improve the content of agricultural technology.

Per capita net income of rural residents (IRR). The per capita net income of rural residents is the total income of rural residents for the year after deducting productive and unproductive expenditures and agricultural taxes; it reflects the level of economic development in each province (urban area). To a certain extent, the per capita net income of rural residents affects the choice of agricultural production methods.

Rural road density (RRD). Rural road density can reflect the infrastructure stock of rural roads and the degree of rural road access. Considering the differences in the geographical area of each province (urban area), this paper uses the ratio of the mileage of rural roads in each province (urban area) to the land area of the corresponding region to express the density of rural roads.

4.3. Data

This paper is based on China, so the data used are all variables related to the Chinese region. The data for the measurement of agricultural TFP were taken from the China Rural Statistical Yearbook of past years. Meteorological data were obtained from the annual value dataset of China’s ground-based climate data in the China Meteorological Science Data Sharing Service Network, which covers the climate data of 752 basic and benchmark ground-based meteorological observation stations and automatic stations in China for the years 2000–2021. Notably, in this paper, the 752 baseline points are grouped according to the province (urban area) in which they are located, and the climatic variables in the region are expressed as the average value of the province (urban area). The data for the indicators selected for the control variables are taken from the China Statistical Yearbook and the China Rural Statistical Yearbook of the past years.

Table 1. Descriptive statistical analysis of key variables.

Variable Category	Variable Name	Sample Size	Average Value	Standard Deviation	Minimum Value	Maximum Value
dependent variable	Agr_TFP	682	1.4913	0.7815	1	2.3611
independent variables	AAT (10 °C)	682	1.3646	0.5445	0.2999	2.4875
	SD (1000 h)	682	2.0777	0.4979	1.0807	2.9345
	AP (10 dm)	682	0.7894	0.3821	0.1858	1.6890
control variables	REF (10 billion kWh)	682	2.8153	1.6289	0.0300	5.5900
	AS (%)	682	64.4030	9.4620	36.8490	83.7350
	FA (100 billion)	682	1.8518	3.2638	0.0100	2.0506
	IRR (100 billion)	682	13.7041	40.4499	0.1098	674.0167
	RRD (%)	682	51.1634	32.7520	9.5367	189.9258
moderator variable	BTP	682	1.072	0.2536	0.7261	1.4385

shows the descriptive statistics of the relevant variables. With the agricultural TFP in 2000 as the base period, i.e., a minimum value of 1, the agricultural TFP in 2000–2021 shows an upward trend, and the maximum value is 2.8611, indicating a large gap in agricultural TFP in different periods and between different provinces (municipalities). The descriptive statistics of climate variables have obvious regional variability, and the specific representations of climate factors, annual average temperature, and sunshine hours have more significant time-series evolution characteristics. The average annual temperature and sunshine hours increase annually, and the annual precipitation exhibits spatial characteristics of increasing in the southern region and decreasing in the northern region. Biased technological progress also has more obvious regional characteristics; it gradually moves from an initially imbalanced structure to a relatively balanced one. Although studies have shown that climate change has a significant impact on agricultural TFP, it is unknown whether biased technological progress will mitigate the impact of climate change on agricultural TFP. For this reason, this paper tests whether biased technological progress mitigates the impact of climate change on agricultural TFP based on existing theories.

Before the baseline regression, the Pearson Correlation Coefficients between the explanatory variables, the explained variables, the moderating variables, and the control variables were analyzed, as shown in

Table 2. VIF test and Pearson Correlation analysis of the main variables.

	VIF	lnAgr_TFP	AAT	AP	SD	REF	AS	FA	IRR	RRD
lnAgr_TFP	-	1
AAT	0.3154	−0.5717 ***	1
AP	0.5627	0.4153 ***	0.2475 ***	1
SD	0.3621	0.6204 ***	0.1654 ***	−0.1582 ***	1
REF	0.2732	0.2853 ***	0.3017 ***	0.1135 ***	0.1252 ***	1
AS	0.1254	0.1572 ***	0.3185 ***	0.0753 ***	0.0236 ***	−0.5253 ***	1
FA	0.8639	0.2741 ***	0.1148 ***	−0.2572 ***	0.0152 ***	0.1218 ***	−0.0417 ***	1
IRR	0.4825	0.1347 ***	0.1297 ***	0.3372 ***	0.2417 ***	0.4245 ***	0.2935 ***	0.0926 ***	1
RRD	0.3627	0.3752 ***	0.2683 ***	0.4626 ***	0.3782 ***	0.2538 ***	0.3125 ***	0.4735 ***	0.2619 ***	1

Notes: *** represent significant at the 1% statistical levels.

. The correlation analysis shows that there is a significant negative correlation between climate change and agricultural TFP, indicating that climate change is detrimental to agricultural TFP, which is basically in line with the expectation and is to be further analyzed in the empirical test. In addition, in order to avoid the problem of multicollinearity between the variables, this paper carried out the analysis of variance inflation factor VIF, as can be seen in Table 2, the VIF is significantly less than 10, indicating that there is no problem of multicollinearity.

5. Results and Discussion

5.1. Analysis of Baseline Regression Results

In this paper, a two-way fixed-effects model was used to verify the effect of annual average temperature on agricultural TFP (Model 1), the effect of sunshine hours on agricultural TFP (Model 2), the effect of annual precipitation on agricultural TFP (Model 3), and the effect of the changes in all climatic factors on agricultural TFP (Model 4); the actual results are shown in

Table 3. Baseline model regression results.

	Dependent Variable lnAgr_TFP
Independent Variables	(1)	(2)	(3)	(4)
AAT	−0.0312 ***			−0.0271 ***
	(0.0072)			(0.0078)
AD		0.0351 ***		0.0284 ***
		(0.0056)		(0.0103)
AP			−0.0958 ***	−0.0869 ***
			(0.0245)	(0.0267)
REF	0.0210 ***	0.0152 **	0.0422 ***	0.0273 ***
	(0.0062)	(0.0074)	(0.0113)	(0.0102)
AS	0.0181 ***	0.0137 **	0.0222 **	0.0165 **
	(0.0043)	(0.0056)	(0.0091)	(0.0072)
FA	0.0163 ***	0.0173 **	0.0135 **	0.0158 **
	(0.0047)	(0.083)	(0.067)	(0.0073)
IRR	0.0638 **	0.0377 *	0.0529 **	0.0423 *
	(0.0268)	(0.0196)	(0.0245)	(0.0241)
RRD	0.0301 ***	0.0374 ***	0.0254 **	0.0216 ***
	(0.0095)	(0.0075)	(0.0106)	(0.0820)
constant term	0.4177 ***	0.2336 **	0.6776 ***	0.6229 ***
	(0.0982)	(0.1070)	(0.2404)	(0.1133)
time effect	yes	yes	yes	yes
individual effect	yes	yes	yes	yes
N	682	682	682	682
R2	0.3192	0.2873	0.3561	0.1718

Notes: ***, ** and * represent significant at the 1%, 5% and 10% statistical levels respectively.

.

A comprehensive analysis of the chi-square statistics of models (1), (2), (3), and (4) indicates that the results of the fixed-effects model are significant at the 1% level; therefore, the choice of the regression model is valid. Moreover, the empirical results show that the significance levels of the variables in the model are the same, indicating that the estimation results of the model are robust. According to the above analyses of the empirical results, the average annual temperature and annual precipitation have significant negative effects on agricultural TFP. When the average annual temperature increases by 1 °C, the TFP of agriculture decreases by 0.31%; when the annual precipitation increases by 1000 mm, the TFP of agriculture decreases by 9.58%. In other words, the average annual temperature and annual precipitation reduced agricultural TFP by affecting the crop planting system, planting cost, and output, which is consistent with the results of the theoretical mechanism analysis. In addition, the number of sunshine hours has a positive effect on agricultural TFP at the statistical level of 1%; an increase in the number of sunshine hours increases agricultural TFP. An increase in the number of sunshine hours by 1000 h increases agricultural TFP by 3.51%; this finding implies that some climate factors also have a positive effect on agricultural TFP. All the climate factors are added to the regression of Model 4; compared with the regression results of the first three columns, the regression coefficients of the climate variables decrease, but their direction and significance do not change. Compared with the results of agricultural TFP in the USA [66], the estimated coefficients in China are basically on the same order of magnitude. At the absolute level, the impact of climate change on agricultural TFP in China is equivalent to approximately 20% of that in the United States, which is mainly attributed to the fact that China’s light and heat resources and precipitation conditions are better than the climate endowment of the United States. In terms of control variables, the impacts of rural electric power facilities, agricultural structure, facility-based agriculture, the per capita net income of rural residents, and rural road density on agricultural TFP are all significantly positive.

5.2. Analysis of Moderating Effects Based on Biased Technological Progress

According to a previous theoretical analysis, biased technological progress has a moderating effect on the impact of climate change on agricultural TFP, and biased technological progress can weaken the negative impact of climate change on agricultural TFP. Therefore, this paper adopts an interaction term to verify whether biased technological progress has a moderating effect on the impact of climate change on agricultural TFP.

As shown in

Table 4. Moderating effect test results.

	Dependent Variable lnAgr_TFP
Independent Variables	(5)	(6)	(7)	(8)
AAT	−0.0267 ***			−0.0223 ***
	(0.0093)			(0.0054)
SD		0.0383 ***		0.0335 ***
		(0.0091)		(0.0093)
AP			−0.0647 ***	−0.0524 ***
			(0.0243)	(0.0186)
BTP	0.0596 ***	0.0158 ***	0.0436 ***	0.0352 ***
	(0.0178)	(0.0061)	(0.0137)	(0.0106)
AAT × BTP	0.0183 ***			0.0134 ***
	(0.0064)			(0.0042)
SD × BTP		0.0072 **		0.0098 **
		(0.0035)		(0.0047)
AP × BTP			0.0282 ***	0.0251 **
			(0.0075)	(0.0063)
constant term	0.3251 ***	0.1628 *	0.3571 ***	0.2784 ***
	(0.0714)	(0.0973)	(0.0893)	(0.0845)
control variable	yes	yes	yes	yes
time effect	yes	yes	yes	yes
individual effect	yes	yes	yes	yes
N	682	682	682	682
R2	0.2736	0.4163	0.3518	0.4675

Notes: ***, ** and * represent significant at the 1%, 5% and 10% statistical levels respectively.

, the interaction term between climate factors and biased technological progress is added to the baseline regression model to test the moderating role of biased technological progress in the relationship between climate change and agricultural production. Considering the possible problem of multicollinearity in the model, this paper centers on the variables involved in the interaction term. According to the moderating effect model, the interaction terms of average annual temperature, number of hours of sunshine, and annual precipitation with biased technological progress have positive and significant effects on agricultural TFP. This implies, first, that biased technological progress significantly moderates the impact of climate factors on agricultural TFP. The regression results reveal that the interaction terms of biased technological progress with both average annual temperature and annual precipitation are significant at the 1% level, and the interaction term with the number of sunshine hours is significant at the 5% level. Second, the mutual coefficients are positive; they are in the opposite direction to the main effects (average annual temperature and annual precipitation on agricultural TFP), which suggests that biased technological progress can weaken the negative impact of climate change on agricultural TFP. Furthermore, they are in the same direction as that of sunshine duration on agricultural TFP, which means that biased technological progress has a facilitating effect on the impact of sunshine duration on agricultural TFP [58].

5.3. Regional Differences in the Impact of Climate Change on Agricultural TFP

As shown in

Table 5. Regional differences in the impact of climate change on agricultural TFP.

	Dependent Variable lnAgr_TFP
Indenpendent Variables	Northern China	Northeastern China	Eastern China	Central China	Southern China	Southwestern China	Northwestern China
AAT	−0.0218 **	0.0037	−0.0164 ***	0.0318 ***	−0.0362 ***	−0.0235 ***	0.0089 **
	(0.0099)	(0.0028)	(0.0051)	(0.0065)	(0.0124)	(0.0078)	(0.0045)
SD	0.0263 ***	0.0358 ***	0.0225 **	0.0247 ***	0.0231 ***	0.0349 ***	0.0216
	(0.0067)	(0.0114)	(0.0094)	(0.083)	(0.0075)	(0.0131)	(0.0084)
AP	0.0973 **	0.1245 ***	−0.0782 **	−0.0859 **	−0.1146 ***	−0.1354 ***	0.1576 ***
	(0.0462)	(0.0276)	(0.0315)	(0.0338)	(0.0287)	(0.0322)	(0.0248)
control variable	yes	yes	yes	yes	yes	yes	yes
time effect	yes	yes	yes	yes	yes	yes	yes
individual effect	yes	yes	yes	yes	yes	yes	yes
N	682	682	682	682	682	682	682
R2	0.2831	0.3057	0.2671	0.3596	0.2473	0.1855	0.2684

Notes: *** and ** represent significant at the 1% and 5% statistical levels respectively.

, the impact of climate change on agricultural TFP has obvious regional heterogeneity [67]. Specifically, except for the nonsignificant effect of average annual temperature on agricultural TFP in Northeast China and the significant positive effect in Central and Northwest China, the average annual temperature has a significant negative effect on agricultural TFP. Except for the nonsignificant regression coefficient of sunshine duration in Northwest China, sunshine duration has a significant positive effect on agricultural TFP. In terms of the magnitude of the coefficient, for every 1 °C increase in average annual temperature in South China, agricultural TFP decreases by 3.62%, while in East China, it decreases by 1.64%, which is much smaller than the effect on South China. However, for every 1 °C increase in the mean annual temperature, the agricultural TFP in Central China increases by 3.18%. In terms of sunshine duration, the regression coefficients of sunshine duration on agricultural TFP are significantly positive in all regions except for those in Northwest China, where the regression coefficients of sunshine duration are not significant. In terms of the magnitude of the coefficient, for every 1000-h increase in the number of sunshine hours in Northeast China, agricultural TFP increases by 3.58%. When the number of sunshine hours increases by 1000 h in East China, agricultural TFP increases by 2.25%, with smaller differences from those in other regions. The impact of the average annual temperature on agricultural TFP shows significant differences in different regions, which leads to a smaller overall impact, as measured in the previous section. In recent years, global warming has increased regional heat resources, which has led to a shift in the boundaries of crop cultivation to the north and west and contributed to the expansion of cultivated areas, an increase in the replanting index and [68], to a certain extent, an increase in crop yields. However, for regions with relatively abundant heat, higher temperatures shorten the growth and development period of crops, leading to lower crop yields and lower quality [69].

Annual precipitation in northern China, northeastern China, and northwestern China has a significant positive effect on agricultural TFP, while annual precipitation in eastern China, central China, southern China, and southwestern China has a significant negative effect on agricultural TFP. In terms of the magnitude of the coefficient, when the annual precipitation in northern China, northeastern China, and northwestern China increases by 1000 mm, the agricultural TFP increases by 9.73%, 12.45%, and 15.76%, respectively, which indicates that the magnitude of the coefficient has obvious regional differences. When annual precipitation increases by 1000 mm in eastern China, central China, southern China, and southwestern China, agricultural TFP decreases by 7.82%, 8.59%, 11.46%, and 13.54%, respectively. In recent years, drought conditions in northern China, northeastern China, and northwestern China have become increasingly severe, and water resource pressures are increasing; for example, the annual precipitation in northern China is less than the water required during the growth and development of wheat crops, and the frequency of extreme events, such as drought, has increased in northern China. Therefore, an increase in annual precipitation can improve water scarcity in arid regions and, thus, increase agricultural productivity [70]. However, water resources are abundant in eastern, central, southern, and southwestern China, and an increase in annual precipitation is prone to flooding, which is not conducive to agricultural production or agricultural TFP [71].

5.4. Robustness Tests

5.4.1. Robustness Test of the Baseline Regression

(1) Variable replacement. Variable substitution is an important method for testing the robustness of the baseline regression model. To further avoid the errors that may result from the selection of indicators and to better reflect the impact of climate change on agricultural TFP, this paper takes the cumulative temperature as a substitute variable for sunshine hours; the replacement results are shown in Model (9). The impact of the replacement variable of sunshine hours on agricultural TFP is consistent with the benchmark results, which verifies the robustness of the benchmark model setup.

(2) Restricted sample. Considering the adverse effects of extreme temperatures on agricultural production and the food supply, the robustness of the regression results should be validated. In this paper, the samples with average annual temperature and annual precipitation within 5% and 95%, respectively, of the samples are excluded and then regressed using the fixed-effects model; the test results are shown in Model (10). After limiting the samples, the impact of climate factors on TFP in agriculture corresponds to the regression results before sample exclusion and the direction of the effect does not change, indicating that the benchmark regression model has robustness.

5.4.2. Robustness Test of the Moderating Effect

To further avoid the errors that may result from the selection of indicators and to comprehensively reflect the level of technological progress, in this paper, the stock of science and technology as a proxy variable for biased technological progress, and estimates the stock of agricultural science and technology in the base period via the simple mean deflator of the consumer price index and the fixed-asset investment price index; thus, robustness tests are conducted on the moderating effect of biased technological progress, as shown in

Table 6. Robustness test.

	Dependent Variable lnAgr_TFP
Independent Variables	(9)	(10)	(11)
AAT	−0.0314 ***	−0.0286 ***	−0.0237 ***
	(0.0075)	(0.0058)	(0.0035)
SD	0.0352 ***	0.0272 ***	0.0381 ***
	(0.0106)	(0.0081)	(0.0116)
AP	−0.0747 ***	−0.0793 ***	−0.0565 ***
	(0.0286)	(0.0304)	(0.0184)
BTP			0.0327 ***
			(0.0089)
AAT × BTP			0.0125 ***
			(0.0039)
SD × BTP			0.104 **
			(0.0046)
AP × BTP			0.0283 ***
			(0.0079)
constant term	0.1953 ***	0.1589 *	0.2245 ***
	(0.0625)	(0.0910)	(0.081)
control variable	yes	yes	yes
time effect	yes	yes	yes
individual effect	yes	yes	yes
R2	0.0351	0.0462	0.0376

Notes: ***, ** and * represent significant at the 1%, 5% and 10% statistical levels respectively.

. The regression results of Model (11) show that the interaction term between climate factors and biased technological progress is significantly positive and significant at the 1% level. This finding indicates that biased technological progress weakens the negative effect of average annual temperature and annual precipitation on agricultural TFP and strengthens the positive effect of sunshine hours on agricultural TFP. These findings are consistent with those of previous papers, which in turn validates the accuracy of these previous results.

6. Conclusions and Policy Implications

However, whether climate change has an impact on agricultural TFP has not yet reached a unified consensus in the academic community. This paper explores the impact of climate change on agricultural TFP and the moderating effect of biased technological progress through theoretical analyses and empirical tests. Through theoretical analysis, this paper explains the internal logic that climate change affects the growth of agricultural TFP through changes in cropping systems, changes in production input factor costs, and uncertainty in outputs. Furthermore, biased technological progress is used as a moderating variable to explore its role in climate change affecting agricultural TFP. In terms of empirical testing, this paper empirically tests the above theoretical analyses based on data from 31 provinces (urban areas) in China from 2000 to 2021 by using fixed effects and moderating effects models. Furthermore, it examines the mechanism of climate change on agricultural TFP and regional heterogeneity and the moderating effect of biased technological progress on the relationship between climate change and TFP. The model is used to examine the relationship between climate change and TFP. In addition, the empirical results are tested for robustness via variable substitution and restricted samples, and the conclusions still hold. The empirical results reveal that average annual temperature and annual precipitation have a significant negative effect on agricultural TFP, sunshine hours have a significant positive effect on agricultural TFP, and there is obvious regional heterogeneity in the effect of climate change on agricultural TFP. Moreover, biased technological progress has a positive moderating effect on agricultural TFP, mitigating the negative impact of climate change on agricultural TFP by reducing the risk of uncertainty in agricultural production.

Based on the above conclusions, this paper draws the following conclusions: first, the climate early warning mechanism should be improved and the ability of agriculture to cope with climate change risks should be strengthened. The construction of an accurate agricultural climate early warning mechanism can enable farmers to take preparatory measures in advance to cope with sudden climate risks and enhance their ability to adapt to climate change and resist climate risks. The new “Internet+” mode can be used to construct an agricultural meteorological information platform to promptly provide farmers with climate change adaptation programs to reduce the losses caused by climate change to agricultural production and improve agricultural production capacity and potential. Second, the development of appropriate technologies should be promoted to achieve the dynamic optimization of agricultural technologies and factor combinations. The country should promote the transformation of agricultural production methods; establish long-term mechanisms and feedback channels for the research and development of agricultural technology; shift from relying mainly on traditional agricultural factor inputs to relying mainly on technological progress and improving the efficiency of factor allocation; strengthen the intensity of the research and development and promotion of agricultural technology; promote the progress of agricultural technology guided by the real needs of agricultural production; develop agricultural technology that is suitable for the local endowment of resources; and increase the contribution of agricultural technology to the TFP of agriculture. China’s quality requirements for improving food security necessitate the development of ecological agriculture, organic agriculture, and fine agriculture through technological innovation.