Analysis of social welfare impact of crop pest and disease damages due to climate change: a case study of dried red peppers

ABSTRACT Climate change can affect agricultural production both directly and indirectly. The direct impact is through climate change itself while the indirect impact is through the outbreak

of pests and diseases (P&D) affected by climate change. We measured the difference in social welfare change of dried red peppers in monetary values between these two effects based on

constructed three models. In the P&D damage model, the effects of climatic factors on P&D damages were analyzed. In the yield model, the direct and indirect effects of climatic

factors on the dried red pepper yields were analyzed. Lastly, the effect of rising temperatures on the social welfare of dried red peppers was measured in monetary values using the

equilibrium displacement model (EDM). As a key result, although these rising temperatures increase the yields and social welfare, there are differences in social welfare change between with

and without P&D damages, and the difference increases over time. This implies that global climate change can affect agricultural production around the world, which can affect food

security around the world beyond changes in crop prices and social welfare. So rigorous pest control and damage predictions are needed. SIMILAR CONTENT BEING VIEWED BY OTHERS CROP PEST

RESPONSES TO GLOBAL CHANGES IN CLIMATE AND LAND MANAGEMENT Article 08 April 2025 OCCURRENCE OF CROP PESTS AND DISEASES HAS LARGELY INCREASED IN CHINA SINCE 1970 Article 09 December 2021 A

THEORETICAL FRAMEWORK TO IMPROVE THE ADOPTION OF GREEN INTEGRATED PEST MANAGEMENT TACTICS Article Open access 18 March 2024 INTRODUCTION Agricultural activities have been dominated by

natural and climatic conditions ever since humans began settled lives after leaving their hunting and gathering lives behind about 10,000 years ago. However, the recent rapid emergence of

abnormal climate is expected to have new impacts on crop productivity and supply and demand stability. According to the National Institute of Meteorological Sciences (Kwon et al., 2020), the

average global temperature is expected to rise by 3.6 °C (1.9–5.2 °C depending on the degree of greenhouse gas (GHG) emissions) by the end of the 21st century. This prediction is higher

than the previous prediction of 2.5 °C (1.3–3.7 °C) and shows that global warming is accelerating. In addition, by the end of the 21st century, the average annual precipitation in the world

is expected to increase by 5–10% compared to the present. In East Asia, the average summer precipitation will increase by up to 20% by the end of the 21st century. Particularly, Min et al.

(2020) said that the frequency and duration of high-temperature days are increasing while the frequency and duration of low-temperature days are decreasing due to increased anthropogenic GHG

emissions since the 1950s in East Asia, including the Korean Peninsula. This ongoing climate change can affect crop production both directly and indirectly. First, climate change can

directly affect crop productivity or production efficiency (Auffhammer et al., 2012; Cho et al., 2013; Challinor et al., 2014; Choi et al., 2018). Auffhammer et al. (2012) analyzed the

effect of drought and extreme rainfall on rice yields in India from 1966 to 2002. They stated that climate change had already had a negative effect on the rice producers and consumers of

India. Cho et al. (2013) conducted a spline regression analysis using meteorological data and rice yield panel data at 49 observation points from 1996 to 2011. They found that rice

productivity decreased when the average temperature for the rice growing season was higher than 20 °C and that precipitation also had a negative effect on productivity. Challinor et al.

(2014) predicted grain production would decrease significantly when the temperature increases by 2 °C or more. Choi et al. (2018) found that climatic conditions cause a statistically

significant decrease in vegetable yields. They said that drought and high-temperature damage in spring and heavy rain and typhoon damage in summer and autumn significantly affect the yield

of vegetables, so it is necessary to create a vegetable production infrastructure that can prepare for climate change. Second, climate change may indirectly affect crop yields by increasing

pests and diseases (P&D) and weed outbreaks (Lee et al., 2020). Several studies have proven or predicted that climate change increases the occurrence of P&D. For example, Jeong and

Kim (2014) studied the relationship between climatic factors (e.g., the number of days of precipitation, precipitation, daylight hours, and temperature) and the five different kinds of major

rice P&D damages using panel data from eight provinces in South Korea from 1991 to 2011. They found that specific climatic factors affect the occurrence area of particular rice

P&Ds. Pareek and Meena (2017) said that the number of pests will rise with every degree the global temperature rises and that this increase in temperature would affect crop pest insect

populations in several ways such as extending their geographical range, increasing their over-wintering, and changing their population growth rate. Kim et al. (2017) constructed a ginseng

P&D damage function to measure the effect of climatic factors on ginseng P&D outbreaks and demonstrated that climatic factors positively affect P&D damage. Kim and Kim (2017)

analyzed the effect of climatic factors such as temperature, precipitation, and humidity on rice P&D damage and found that the effect is valid. Skendzic et al. (2021) said that the main

drivers of climate change (increased atmospheric CO2 and temperature and decreased soil moisture) might significantly affect the population dynamics of insect pests and thus the percentage

of crop losses. They stated that temperature has an important role in pest metabolism, metamorphosis, mobility, and host availability and determines the possibility of changes in pest

population and dynamics. They also mentioned that P&Ds of different crops, such as wheat, rice, and maize, which are grown in different regions, are affected by different climatic

conditions. The outbreak of P&Ds is an exogenous shock that may lead to changes in social welfare due to changes in market prices and quantities. Several studies have calculated or

analyzed the effects of P&Ds on social welfare in monetary value. For example, Oliveira et al. (2013) estimated the production losses of major crops grown in Brazil and calculated the

economic losses caused by the purchase of insecticides to control insect pests and the medical expenses of humans damaged by these insecticides. Lee (2015) conducted an economic welfare

analysis reflecting the exogenous impact of the pork market caused by foot-and-mouth disease (FMD) from 2010 to 2011. Lee (2015) measured not only the damage caused by mass culls due to FMD

but also the additional welfare changes, with changes in the market equilibrium point for each production stage of pork and its substitutes (beef and chicken), caused by related government

policies and reduction of consumer confidence. Letourneau et al. (2015) estimated the welfare changes in markets for squashes and cucumbers caused by biodiversity changes associated with the

biological control of pests in Georgia and South Carolina. Using meta-analyses, they demonstrated a key framework that natural scientists could use for their studies and developed the

process in terms of economic theory. Daniels et al. (2017) measured the monetary value of natural predators for biological pest control in pear production. They constructed an ecological

simulation model with a production function considering the predator-prey dynamics between the pest insects and their natural enemies. They quantified the effect of reduced natural predator

numbers on the net farm income within an economic model. Hwang (2020) measured the impact of a decrease in supply caused by the mass cull of pigs due to African Swine Fever (ASF) in

September 2019 and a decrease in consumption due to a contraction in consumer confidence. As stated above, direct and indirect climatic factors, through P&D damages, affect crop

production. In addition, these influences affect social welfare by changing the price and quantity of crops. Therefore, this study intends to comprehensively analyze the sequential effects

of climate change, P&D damage, production change, and social welfare change for a specific crop. This sequential process also appears in the production and market of dried red peppers,

one of the staple seasoning vegetables of Korea. Dried red peppers have the largest vegetable cultivation area in Korea, with an area of 18.7% in 2015. However, its productivity changes

relatively easily compared to food crops. Since it is an open-field vegetable, it is more vulnerable to climatic factors and damage from P&Ds such as phytophthora blight, anthrax, soft

blight, and viral diseases.Footnote 1 For instance, Koung (2014) presents an analysis result that an increase in temperature can help red pepper growth, but its production decreases by 17%

when the temperature rises above 30 °C. Shin and Yun (2011) analyzed that the occurrence of anthrax in all regions of Korea would rise in the future using forecasted meteorological data from

2011 to 2100. Kim et al. (2015) analyzed the relationship between anthracnose disease incidence in red peppers and red pepper yield in fields and rain shelters. They concluded that the

yield decreased as anthracnose diseases occurred in both conditions. Dried red peppers have decreased yield due to heavy rains, typhoons, and more occurrences of anthrax in the summer of

2020. This enlarged the price instability causing the wholesale price of red pepper powder to rise by 67% compared to the 5-year normal price (Kim, 2020). Thus, the harvest of dried red

peppers, directly and indirectly, affected by climatic factors, has a substantial economic ripple effect in production and distribution and may further affect consumption. So, ongoing

climate change is expected to have a consequential economic impact on the dried red pepper market. Accordingly, this study aims to analyze the effects of climate change on the social welfare

of dried red peppers while considering P&D damages. The research process is divided into three steps with three models (See "Empirical models"). The key difference of this

study from previous studies is that it comprehensively analyzes the sequential effects of climate change, P&D damage, change in production, and change in social welfare for a specific

crop. Although other studies analyzed the direct impact of climate change on crop production or the indirect impact of P&D damage on production, studies considering both impacts

comprehensively are scarce. Furthermore, no studies are measuring such a sequential process of climate change to the social welfare of a crop, so the monetary impact of climate change on

agricultural social welfare currently cannot be measured. Therefore, this study defines itself by considering its sequential effect comprehensively and by presenting the impacts of climate

change through P&D damage on social welfare regarding monetary values. The flow of this study is as follows. “Introduction” summarizes the background of dried red pepper and its

P&Ds. “Methods” explains the data used for analysis with three models: P&D model, yield model, and equilibrium displacement model (EDM). In “Results”, the results of the three models

are presented. The final section presents “Conclusions”. METHODS STUDY MATERIALS DRIED RED PEPPERS IN KOREA With an annual red pepper consumption of 2.0–2.5 kg per person, Koreans are the

biggest consumers of red peppers in the world; other major countries with high red pepper consumption, Hungary, the U.S., and Japan, consume 200 g, 50 g, and 20 g of red peppers each year,

respectively (Lee et al., 2016). The two main red pepper consumption methods are making them into a powder, usually called dried red peppers, traditionally used for seasoning, and eating

them raw, usually called green peppers. Koreans consume more dried red peppers than green peppers. Dried red peppers are grown in open fields and are the most important source of income for

farmers as they account for the largest proportion of cultivation area at 22.2%. Furthermore, they produce the third highest income per 10a among the major open-field vegetables (Table 1).

P&DS OF DRIED RED PEPPERS Grown in open fields, dried red peppers are less durable in the wind than other crops. They are vulnerable to both dryness and humidity, making them very

difficult to grow due to infectious soil diseases such as phytophthora blight (Lee, 2011). In recent years, while the cultivation area of dried red peppers has remained at a constant level,

the production of dried red peppers has been relatively unstable (Fig. 1), which may result from increased P&D damages caused by current global warming. Therefore, we examined the

outbreak characteristics of red pepper P&Ds. There are 29 kinds of P&Ds that damage Korean red peppers, and the four most damaging P&Ds to dried red pepper cultivation are

_phytophthora blight (PB), anthrax (Ath), viral diseases (VD)_, and _tobacco moths (TM)_ (Kwon and Lee, 2002; Yang et al., 2020).Footnote 2 Since these P&Ds have different occurrence

conditions, it is necessary to review and examine the occurrence conditions of these four P&Ds through previous studies and the P&D damage trends by periods in recent years using

data provided by the National Crop Pest Management System (NCPMS) of the Rural Development Administration (RDA). The followings are the occurrence conditions of the four major P&Ds.

First, _PB_ is a disease caused by _Phytophthora capsici_ that inflicts substantial damage every year in continuous cropping lands. It occurs due to a high density of germs and low soil

fertility, and it is not easy to control solely with chemical solutions. It starts from the beginning of June after planting and flourishes during the rainy season from July to August. As

_Phytophthora capsici_ are hydrophilic and semiaquatic bacteria, rainfall and the number of days with precipitation are decisive factors in the occurrence of _PB_, which spreads more

severely at high temperatures (Jang, 2002; Yang et al., 2020). Second, _Ath_ is the most problematic disease along with _PB_. It starts as a small spot that is difficult to detect by eye in

mid-to-late June and increases rapidly during the rainy season when the temperature and humidity are high. It shows its peak outbreak in mid-August (Jang, 2002; Yang et al., 2020). Third,

_VD_ are much more damaging in open-field cultivation than in greenhouse cultivation, and 80% of the damage is due to transfer by aphids (Yang et al., 2020).Footnote 3 Bae (1999) stated that

the activities of aphids begin in dry mid-April, the early stage of red pepper growth, and poor growth of red peppers due to the lack of nutrients after their mid-stage growth causes severe

_VD_ damage as the activities of aphids increase. Fourth, the caterpillars of _TM_ dig up the leaves, fruits, and buds of dried red peppers or make holes in the fruits, which secondarily

causes bacterial soft rot. _TM_ begin their metamorphosis around June, and their damage occurs in August but most severely in early September (Yang et al., 2020). Next, we examined recent

trends of each P&D damage over the past eight years (2014–2021) using data from NCPMS.Footnote 4 The data were collected eight times at 15-day intervals between June 1st and September

16th every year. _PB_ and _VD_ are investigated through damaged heads rate (DHR),Footnote 5 and _Ath_ and _TM_ are investigated through damaged fruits rate (DFR).Footnote 6 The following

four figures are graphs showing the damage rate of each P&D at the investigation dates from 2014 to 2021 (Fig. 2). EMPIRICAL MODELS The purpose of this study is to comprehensively

analyze the sequential process of climate change, P&D damage, change in production, and change in social welfare for dried red peppers. In order to achieve this, the research process is

divided into three models sequentially. First, a P&D damage model is established to analyze the impact of climate change on P&D damages. The four major P&D damages analyzed in

this model are _PB_, _Ath_, _VD_, and _TM_. Each kind of P&D damage is affected by different climatic factors, so different climatic factor variables are used as independent variables

for each kind of P&D damage (Fig. 2). Second, a yield model is established to analyze the direct and indirect effects of climate change on the yield of dried red peppers. Particularly,

the indirect effect is analyzed by using the predicted P&D damage values as independent variables. Lastly, we analyzed the effect of changed yields and production, which are directly and

indirectly affected by climate change, on the market price and quantity of dried red peppers and measured the resulting social welfare changes using the EDM. However, climatic conditions

mostly stay the same but gradually, and EDM analysis needs a temporary shock from outside the market, such as a crucial disease outbreak or a government policy implementation. In order to

measure the impact of continuous climate change, it is appropriate to set a specific scenario and analyze based on it. For this reason, RCP 6.0 temperature change scenarios provided by the

Korea Meteorological Administration (KMA) are used for the EDM analysis.Footnote 7 P&D DAMAGE MODEL The purpose of the P&D damage model is to analyze the impact of climate change on

P&D damages, so the dependent variables are P&D damage rates and the independent variables are climatic factors and the locations of cities. The P&D damage rates of _PB_, _Ath_,

_VD_, and _TM_ provided by NCPMS are used as dependent variables. The P&D damage rate data consist of observation values from eight sessions conducted from June 1st, 2014, to September

16th, 2021. Each observation was made on the first day of each 15-day session. Spatially, these data are collected from 59 cities nationwide, where the data was averaged from the 1657

observation sites. We averaged the data of the 59 cities to construct a panel data set since each observation site varies slightly every session and year. As independent variables, the

longitude and latitude of each city and climatic factors, such as temperature (°C), rainfall (mm), humidity (%), and sunshine hours (hrs), are considered. The climatic factor data provided

by the Korea Meteorological Administration (KMA) for each city and the P&D damage observation site locations were spatially interpolated using QGIS. Summary statistics and descriptions

of all of these variables are presented in Table 2. Since P&D damage does not occur in every city, many observations with a P&D damage rate of 0 occurred. Therefore, it is

appropriate to use the left-censored panel Tobit model as the empirical P&D damage model, as shown in the following Eq. (1): $$\begin{array}{ll}Damage_{it}^ \ast = \beta _1 + \beta

_2\,Location_{it}\\ \qquad\qquad\qquad+ \,\beta _3\,Climate_{it} + \beta _4\,Avgdev_{climate_{it}}\\ \qquad\qquad\qquad+ \,u_{it}\quad if\,Damage_{it}\, > \,0\end{array}$$ $$Damage_{it}^

\ast = 0\quad if\,Damage_{it} \le 0$$ (1) where the 59 different cities nationwide are represented by _I_ = 1,2,...,59 and the 64 different observation sessions from 2014 to 2021 are _t_ =

1,2,3,...,64. _Damage__it_ denotes the latent P&D damage of each city _i_ at session _t_, \(Damage_{it}^ \ast\) is the P&D damage rate observed, _Location__it_ is the latitude and

longitude, _Climate__it_ represents the climatic factors such as temperature, rainfall, humidity, and sunshine hours (i.e., Avgt, Avgr, Avgh, and Sun), and _Avgdev___Climate__it_ represents

deviations of average temperature and humidity from 1970 to 2000 (i.e., Avgt dev and Avgh dev). The error term _u__it_ is $$u_{it} = \alpha _i + v_{it}$$ (2) where the individual city effect

_α__i_ is distributed \(N\left( {0,\,\sigma _\alpha ^2} \right)\), and the stochastic disturbance term _v__it_ is distributed \(N\left( {0,\,\sigma _\nu ^2} \right)\). We assume that _α__i_

is independent of _Location__it_, _Climate__it_, _Avgdev___climate__it_, and _v__it_, where _E_(_α__i__α__j_) = 0, _E_(_α__i__v__it_) = 0, and _E_(_v__it__v__ij_) = 0 and in all cases _i_ ≠

_j_. In the panel Tobit model, the random effect estimation method is preferred because there are consistency issues, i.e., incidental parameter problems, for fixed effect estimates unless

the timeframe of the panel data is long enough (Wooldridge, 2010; Kim and Langpap, 2016). In this case, the timeframe is not long enough, so a panel Tobit analysis based on the random effect

model is performed. However, to overcome the limitation of not considering fixed effects, we applied the Chamberlain random effects (CRE) Tobit model (Wooldridge, 2010), which applies

Chamberlain’s conditional estimation method by including the group average as additional regressors in the model, providing a consistent way to estimate fixed effects models. We use

_Avgdev___Climate__it_ to control group fixed effect by employing average climate factors across the cities from 1970 to 2000 and to control abnormality of climatic conditions by using

deviation from the average. YIELD MODEL For the dried red pepper yield model, the direct and indirect effects of climate change on the yield are analyzed. The dependent variable of this

model is the dried red pepper yield, and the independent variables consist of climatic factors as direct effects and P&D damage variables as indirect effects. The yield panel data

provided by Statistics Korea (KOSTAT) are used as a dependent variable. The yield data are the average data by the eight provinces from 2014 to 2021. As key control variables, the predicted

values of the four P&D damage rates are used as independent variables for indirect effects. The predicted values of _Ath_ and _TM_ are combined to prevent multicollinearity problems

because their damage rate measurement methods are not only the same as DFR but also have a high correlation.Footnote 8 As the remaining independent variables for direct effects, average

temperature and average rainfall data provided by the KMA are used. These variables are divided into three growth periods, the planting period (April), the growing period (May–July), and the

harvesting period (August–October), since the climatic factors of each growth period may affect the yield differently. We refer to Lee and Yang (2017) and Lee et al. (2020) for setting up

the yield model and selecting climate variables. We also checked for multicollinearity among the seasonal climate variables and found that the correlation coefficients between the variables

ranged from 0.05 to 0.58 in absolute value, and the average VIF (Variance Inflation Factor) was 4.83 (less than 10), indicating that multicollinearity is not a concern (Vittinghoff, 2005).

Table 3 shows a summary of the statistics and descriptions of all these variables. The empirical yield model is shown in the following Eq. (3): $$Yield_{it} = \beta _1 + \beta _2\widehat

{Damage_{it}} + \beta _3Climate_{it} + \beta _4Avgdev\_climate_{it} + u_{it}$$ (3) where _i_ = 1,2,...,8 represents the eight different provinces, _t_ = 1,...,8 is the eight years from 2014

to 2021, _Yield__it_ is the dried red pepper yield, \(\widehat {Damage_{it}}\) is the predicted P&D damages (i.e., _PBVD_ and _ATM_)_, Climate__it_ is averages of temperature, rainfall,

and sunshine hours for each period, and _Avgdev___climate__it_ is average deviations of climate factors (see Table 3). We used a fixed effect panel model in this yield model, unlike in the

P&D damage model. In short panel data,Footnote 9 the estimates obtained from fixed and random effect models may be significantly different. The yield model of this study rejects the null

hypothesis at a 5% significance level in the fixed effect model (_p_-value: 0.0179) while the null hypothesis cannot be rejected at a 5% significance level as a result of a Breusch and

Pagan multiplier test for random effects. Therefore, the fixed effect model is used for the yield model. EQUILIBRIUM DISPLACEMENT MODEL The equilibrium displacement model (EDM) is one of the

comparative static analysis methods used to analyze the effect of exogenous factors on endogenous variables. In other words, EDM simulates change rates of price and quantity, which are

endogenous variables, when the market equilibrium of an item moves to a new point due to changes in external conditions. For example, Lusk and Anderson (2004) analyzed the effect of

country-of-origin labeling (COOL) on the welfare of participants in the livestock sector by constructing an EDM and conducted a sensitivity analysis to examine how the costs of COOL affect

the welfare of the market participants. Okrent and Alston (2012) attempted to encourage the consumption of healthy food and discourage the consumption of unhealthy food. They used EDM to

estimate the impact of hypothetical farm commodity and retail food policies on the economic welfare of other alternatives to reduce obesity. In this study, the change in the social welfare

of dried red peppers is measured by analyzing the effect of climate change and P&D damages on the price and quantity of dried red peppers. For EDM analysis, it is necessary to make

assumptions about the price elasticities of supply and demand and the initial values of price and quantity. It is because EDM analysis measures the change rates of endogenous variables

through the total derivative of functions. For this reason, the initial price is assumed to be 9140 KRW/kg (OASIS of Korean Rural Economic Research Institute (KREI), 2014–2021)Footnote 10

and the initial quantity is assumed to be 74,672 M/T (Statistics Korea (KOSTAT), 2014–2021). The price elasticity of supply is assumed to be 0 ~ 0.2 with the review of Lee et al. (2013), Min

et al. (2022), and Lee et al. (2011)Footnote 11, and the price elasticity of demand is assumed to be −0.16 ~ −0.24 by referring to Choi (2017), and Kim et al. (2000) (Table 4). Since the

price elasticity of demand presented in previous studies is somewhat different, we conducted a sensitivity analysis in EDM analysis. In general, EDM analyzes the effects of large changes

outside the market. In order to measure the impact of continuous climate change, it is appropriate to set a specific scenario and analyze based on it. In this study, the RCP 6.0 temperature

change scenario by three growth periods of dried red peppers over the next three decades is used for EDM (Table 5). The following three functions are empirical system equations for EDM

analysis: $$Q^S = f\left( {P|D\left( T \right),T} \right)$$ (4) $$Q^D = g\left( {P|Y} \right)$$ (5) $$Q^S = Q^D$$ (6) where _Q__D_, _Q__S_ and _P_ represent the demand, the supply, and the

wholesale price of dried red peppers, respectively. Equation (6) represents the equilibrium condition of the market. _D_ and _T_ represent P&D damage and temperature change, which are

exogenous variables that affect the supply of dried red peppers, and _D_ is a function of _T_. _Y_ represents an exogenous variable that affects demand. The above three equations can be

transformed into total derivative forms, which are the linear combination of the change rates and elasticities of each variable. The total derivative of the function (4) is in the following

Eq. (7):Footnote 12 $$\begin{array}{ll}{EQ^S = \varepsilon _P\cdot EP + \left( {\varepsilon _D\cdot \beta _T\cdot ET + \varepsilon _T\cdot ET} \right)}\\ \qquad\,\,{ = \varepsilon _P\cdot EP

+ K}\end{array}$$ (7) where _EQ__S_ = Δ_Q__S_/_Q__S_, _EP_ = Δ_P_/_P_, _ET_ = Δ_T_/_T_, $$\varepsilon _P = \left( {\Delta Q^S/Q^S} \right)/\left( {\Delta P/P} \right),$$ $$\varepsilon _D =

\left( {\Delta Q^S/Q^S} \right)/\left( {\Delta D/D} \right),$$ $$\beta _T = \left( {\Delta D/D} \right)/\left( {\Delta T/T} \right),$$ $$\varepsilon _T = (\Delta Q^S/Q^S)/(\Delta T/T)$$ _K_

is the exogenous shock that shifts the supply curve. _K_’s first term _ε__D_ · _β__T_ · _ET_ is the indirect effect of temperature change on the yield of dried red peppers, and _K_’s second

term _ε__T_ · _ET_ is the direct effect of temperature change on the yield. Equation (5) also can be transformed to a total derivative form as Eq. (8):Footnote 13 $$EQ^D = \eta _P\cdot EP +

U$$ (8) where _η__P_ = (Δ_Q__D_/_Q__D_)/(Δ_P_/_P_), $$U = external\,shock\,of\,demand$$ The total derivative form of function (6) is as follows:Footnote 14 $$EQ^D = EQ^S = EQ$$ (9) Based on

Eq. (9), Eqs. (7) and (8) can be expressed by a system of two equations as follows: $$\left\{ {\begin{array}{*{20}{c}} {EQ = \varepsilon _P\cdot EP + K} \\ {EQ = \eta _P\cdot EP + U}

\end{array}} \right.$$ (10) In order to obtain the change in equilibrium price and quantity through EDM, the unknowns _EQ_ and _EP_ must be solved. The solution values of _EQ_ and _EP_ can

be expressed as functions of the exogenous variables _K_ and _U_ and the given parameter _η__P_ as follows: $$EQ = \varepsilon _P\cdot EP + K,\,EP = \frac{{K - U}}{{\eta _P - \varepsilon

_P}}$$ (11) where −1 < _η__P_ < 0 and 0 < _ε__P_ < 1. Dried red peppers are one of the agricultural products generally recognized as essential goods, so it is assumed that the

price elasticity of demand _η__P_ is more than −1 and less than 0, and the price elasticity of supply _ε__P_ is more than 0 and less than 1. Thus, when temperature change exerts an exogenous

impact on the production of dried red peppers, _EQ_ and _EP_ change as follows: $$dEQ/dET = (\varepsilon _D\cdot \beta _T + \varepsilon _T)\left( {\frac{{\eta _P}}{{\eta _P - \varepsilon

_P}}} \right),\,dEP/dET = (\varepsilon _D\cdot \beta _T + \varepsilon _T)/(\eta _P - \varepsilon _P).$$ (12) Temperature rise increases P&D damage (Jeong and Kim, 2014; Pareek and Meena,

2017; Kim et al., 2017; Kim and Kim, 2017) and dried red pepper production decreases as P&D damage rises (Kim et al., 2015). That is, _β__T_ > 0, _ε__D_ < 0. However, since the

direct effect of temperature rise may increase or decrease dried red pepper production, the change direction of _EQ_ and _EP_ is unknown before empirical analysis.Footnote 15 This is shown

in Fig. 3. Given the direction and the amount of change in the supply curve _S_0, the difference between the changed social welfare (□Q'E'E0Q0 or □Q0E0E''Q'')

and the initial social welfare (□_OP_0_E_0_Q_0) can be calculated based on the temperature change scenarios in Table 5. The direction and amount of change in _S_0 is determined based on the

analysis results of the P&D damage model and the yield model, which will be shown in the next section. RESULTS The estimation results for the three empirical models are presented below.

In the estimation results of the P&D damage model and the yield model, we focus more on the sign and significance of the climatic variables rather than describing all the coefficient

values since this study aims to measure changes in the social welfare of dried red peppers caused by climate change. RESULT OF P&D DAMAGE MODEL The estimation results of the P&D

damage model are shown in Table 5, which shows the marginal effects of each climatic variable on each of the four P&Ds. First, the _PB_ damage rate is significantly affected by sunshine

hours (_Sun_), temperature deviation (_Avgt dev_), and humidity deviation (_Avgh dev_). More specifically, the rate increases as temperature compared to the average year (1971–2000)

increases and decreases as humidity compared to the average year and sunshine hours increase. For the _Ath_ damage rate, average temperature (_Avgt_) and its square term (_Avgt_2), average

humidity (_Avgh_), temperature deviation (_Avgt dev_), and _Avgh dev_ affect the rate significantly. Since _Avgt_2 has not only a positive impact but both _Avgt_2 and _Avgt_ are also

significant, the Ath damage rate has a minimum value at 30.3 °C and then increases again. Lastly, for _VD_ and _TM_ damage rates, _Avgt,_ _Avgt_2, _Avgh_, sunshine hours (_Sun_), _Avgt dev_,

and _Avgh dev_ have significant effects on the rates. Like _Ath_ damage rate, _VD_ and _TM_ damage rates also have minimum values at 37.5 °C and 28.6 °C, respectively, since their _Avgt_2

variables are not only positive but both _Avgt_2 and _Avgt_ are significant. They increase as temperature compared to the average year (_Avgt dev_) and average humidity (_Avgh_) increase

(Table 6). RESULT OF YIELD MODEL The estimation results of the yield model, which takes into account climatic factors as the direct effects of climate change and the predicted P&D damage

values as the indirect effects of climate change, are shown in Table 7. As shown in the yield model analysis results, the temperature of the planting period and the harvesting period have

positive effects on the yield, and the temperature of the growing period has a negative effect on the yield. Rainfall of all periods has significant effects on the yield. In detail, when the

average temperature of the planting and harvesting periods increases by 1 °C, the yield increases by 25.3 kg and 47.7 kg per 10 a, respectively. When the rainfall increases by 10 mm, the

yield of the planting period increases by 2.5 kg per 10 a while the yields of the growing period and the harvesting period decrease by 2.5 kg and 2.8 kg per 10 a, respectively. Although the

signs of _Avgt_ coefficients are not significant, the directions are reasonable because, in Korea, the growing season is in summer, when the temperature peaks throughout the year, and the

temperature is low in the planting and harvesting seasons, spring and autumn, respectively. Especially, experiments by Koung (2014) showed that red pepper growth varies at a specific

temperature. Considering the Korean seasonality and the experimental results of Koung (2014), the temperature sign direction of this model is reasonable. A notable result is that most of the

temperature variables have positive effects on the yield. In contrast, the indirect effects, the predicted values of P&D damages, negatively affect the yield. Since these two kinds of

effects are offsetting each other, the sign of the total climate change effect is determined by the greater one. The EDM analysis results examine the net effect of the direct and indirect

effects. RESULT OF EDM The amount of yield change based on the temperature change scenarios is measured and EDM analysis using these values is conducted to derive the change rates of price

and quantity. After that, social welfare changes are measured in monetary values using the assumed price elasticities of supply and demand and initial price and quantity. YIELD CHANGE BASED

ON THE RCP TEMPERATURE CHANGE SCENARIOS Table 8 measures the amount of yield change based on the three RCP 6.0 temperature change scenarios over the next three decades using the analysis

results of the yield model and compares the difference of yield change between with and without P&D damages. In 2030, the yield was 1 kg less with P&D damage compared to without

P&D damage. In 2040 and 2050, yields were 18.1 kg and 23.8 kg less with P&D damage than without P&D damage, respectively (see also Fig. 4). Temperature rise has a positive effect

and the predicted P&D damage values have a negative effect on the yield model results, but the yield decreases in all RCP scenarios. Thus, P&D damage offsets the positive effect of

the climatic factors and causes more damage. Therefore, the reason for the decrease in the yield is not the direct effect of climate change but the indirect effect caused by P&D damage.

SOCIAL WELFARE CHANGE BASED ON RCP TEMPERATURE CHANGE SCENARIOS The EDM analysis process is shown in Fig. 5. Using the assumed parameters for elasticities of supply (0 ~ 0.2) and demand

(−0.16 ~ −0.24) and initial price and quantity, the inverse demand curve can be derived as _P__d_ = _K_ − _b_ · _Q_. The supply curve shifts to the right without P&D damage rather than

with P&D damage. As a result, the price with P&D damages (_P__B_) is higher than the price without P&D damages (_P__A_), and accordingly, social welfare loss occurs more with

P&D damages than without P&D damages (_□OKE__A_ _Q__A__-□OKE__B_ _Q__B_) (“□” means the area of a rectangle). Table 9 shows the sensitivity analysis of social welfare change with the

price elasticities of demand ranging from −0.16 to −0.24, holding the price elasticity of supply at 0.1 with the changed equilibrium prices and quantities based on the RCP 6.0 temperature

change scenario over the next three decades. The results show that when the price elasticity of demand is −0.16, the social welfare loss is 9.3 billion KRW in 2030, 161.5 billion KRW in

2040, and 239.2 billion KRW more in 2050 when it is with damages rather than without P&D damages. When the price elasticity of demand is −0.20, the difference in social welfare is 7.1

billion KRW in 2030, 125.8 billion KRW in 2040, and 183 billion KRW in 2050, respectively. Lastly, when the elasticity is −0.24, the difference is 9.8 billion KRW in 2030, 174.7 billion KRW

in 2040, and 251.2 billion KRW in 2050, respectively.Footnote 16 In summary, as time passes, the difference in social welfare with P&D damages and without P&D damage increases, and

the difference decreases as the price elasticity of demand increases. Table 10 shows the sensitivity analysis of the price elasticities of supply ranging from 0 to 0.2, holding the price

elasticity of demand at −0.2. When the supply elasticity is 0, the social welfare loss is 6.8 billion KRW more in 2030, 116.1 billion KRW more in 2040, and 176.4 billion KRW more in 2050

with P&D damages than without the damages. When the supply elasticity is 0.1, the difference in social welfare is 7.3 billion KRW in 2030, 130.4 billion KRW in 2040, and 186.1 billion

KRW in 2050, respectively. When the elasticity is 0.2, the difference is 7.6 billion KRW, 138.2 KRW in 2040, and 191.4 billion KRW in 2050, respectively. Based on the results of all the

climate change scenarios and price elasticity combinations presented above, the loss of social benefits due to direct and indirect damages from climate change and pests ranged from 6.8

billion KRW to 251.2 billion KRW. Comparing this to the total production value of dried red pepper in 2021, which is 694.2 billion KRW at real prices (2015 = 100), the loss of social

benefits is estimated at around 1% to 36.2%. DISCUSSION AND CONCLUSIONS Recent rapid climate change may affect the productivity of crops and have a new impact on supply stability. Climate

change itself can directly affect the production of crops or indirectly affect them by increasing the occurrence of P&Ds. Furthermore, changes in crop production can affect supply and

social welfare. Therefore, this study examined the effect of climate change on social welfare by considering the indirect effects of P&D damages. The analysis target in this study is

dried red peppers. The productivity of open-field vegetables fluctuates more easily than food crops due to climatic conditions, and dried red peppers account for the largest cultivation area

among the open-field vegetables. In addition, dried red peppers account for a high proportion of farm income and are also major seasoning vegetables for Koreans as they are an ingredient in

kimchi. Therefore, changes in dried red pepper production may affect social welfare significantly. This study goes through three steps. First, a P&D damage model is constructed to

measure the effect of climatic factors on P&D damage rates. Second, a yield model is constructed to measure the direct and indirect impact of climate change on dried red pepper

production. Lastly, EDM analysis of RCP temperature change scenarios examines social welfare changes in monetary value. In the P&D damage model, the effect of climatic factors on the

damage rates of four major P&Ds, phytophthora blight (_PB_), anthrax (_Ath_), viral diseases (_VD_), and tobacco moths (_TM_), was analyzed. Since the occurrence conditions differ for

each P&D, they were reviewed in advance while considering climatic factors. As the analysis results show, the P&D damages of Ath, VD, and TM have minimum values at 30.3 °C, 37.5 °C,

and 28.6 °C, respectively, because their average temperature square terms (_Avgt_2) are not only positive but both _Avgt_ and _Avgt_2 are also significant. Additionally, the abnormal climate

that will further become severe may increase the P&D damages since all the temperature deviation variables from its normal trends have a significant positive effect. In the yield model,

a notable result is that P&D damage inhibits the positive effect of temperature rise. In other words, despite the positive effect of temperature itself on the yield of dried red

peppers, the future yield will not increase significantly because the P&D damages inhibit the positive effect. Furthermore, the difference in yields between with and without P&D

damages increases over time, affecting social welfare change. The results of EDM analysis show that the difference in yields can affect in difference in social welfare between with and

without P&D damages. EDM analysis result shows the social welfare loss with P&D damage tends to increase over time. Based on the results of all the climate change scenarios and price

elasticity combinations presented above, the loss of social benefits due to direct and indirect damages from climate change and pests ranged from 6.8 billion KRW to 251.2 billion KRW, which

is 1% ~ 36.2% of the total production value of dried red pepper in 2021 (694.2 billion KRW). The key differences between this study and previous studies are that it analyzes the sequential

effects of climate change, P&D damage, change in production, and change of social welfare for dried red peppers and measures the degree of actual damages in monetary values using RCP

temperature change scenarios. Previous studies analyzed only the trends of climate change and the marginal effects of climate change on dried red pepper production or focused on only one

specific P&D damage, so they are limited in that they do not consider the overall P&D damage of a certain crop. In addition, it was difficult to examine comprehensive damage because

no studies considered both the effects of climate change and P&D damage on crop production. In this study, a P&D damage model is constructed, and the predicted values of the P&D

damages are used in the yield model as independent variables to consider the indirect impact of climate change. In summary, although rising temperatures increase the dried red pepper yields

and lead to an increase in social welfare, P&D damages inhibit the increase in yield and social welfare, so it is necessary to manage the P&Ds. This study analyzed dried red peppers

in Korea, but the sequential process of climate change, P&D damage, production change, and social welfare change may not be just a problem for dried red peppers in Korea. Rising global

temperatures and more intense global precipitation affect insect diapause. They can change pest populations and dynamics to have more impact on crop yields, so more efforts are needed to

predict and control P&D damages. Moreover, if this process causes economic losses to major crops worldwide, such as rice in Asia and corn in Western countries, it can have a substantial

impact on food security for countries beyond the issue of crop prices and social welfare. This study only considered a supply-side shock. However, if the decline in demand is greater than

the P&D damage, prices may eventually fall or change due to government policies. In other words, the change in the left-shifted demand curve may be larger than the change in the

left-shifted supply curve due to the impact of rising temperatures shown in the analysis results of this study. Thus, prices can decrease or be controlled through government price

stabilization policies. Since this study did not consider the demand side shock or government policies, future studies are needed to develop these factors further. DATA AVAILABILITY Data are

available from the corresponding author upon reasonable request. Supplementary material is available at https://dataverse.harvard.edu/api/access/datafile/7167013. NOTES * When pest damage

occurs in red peppers, their yield may decrease by at least 15%, at most 60% (Yang et al., 2020). * In the terms and conditions of crop insurance operated by Korean agricultural cooperative,

_PB_, _Ath_, and _VD_ are also compensated for P&D damage as a high priority, with the first grade and the second grade for _TM_. * There are 16 types of _VD_ that occur in red peppers,

and 7 of them mainly occur in domestic red peppers (Yang et al., 2020). * NCPMS has provided P&D damage rate data since 2014. * DHR (%) = {(the number of damaged heads) / (the number of

investigated heads)} * 100 * DFR (%) = {(the number of damaged fruits) / (the number of investigated fruits)} * 100 * Since RCP 6.0 scenario is the case where the greenhouse gas reduction

policy is properly realized (Korea Environment Corporation), this study determines that the RCP 6.0 scenario is the most realistic than the other three scenarios, RCP 2.6, RCP 4.5, RCP 8.5.

* Variables with a medium (r > 0.5) or even high (r > 0.7) correlation need to be reduced in the model to avoid multicollinearity problems (Donath et al., 2012). The correlation

between _Ath_ and _TM_ is 0.6827. * Short panel: _i_ > _t_, long panel: _i_ < _t_ (Gujarati, 2004). * https://oasis.krei.re.kr/index.do * Since there are no values of supply elasticity

for Korean dried red peppers, we reviewed the supply elasticities of Lee et al. (2013), Min et al. (2022), and Lee et al. (2011), where they analyzed subtropical vegetables, rice, and bean

and tofu, respectively. * Please refer to the equation (A1) in the Appendix for the mathematical induction. * Please refer to equation (A2) in the Appendix for the mathematical induction. *

Please refer to the equation (A3) in an Appendix for the mathematical induction. * The change in production directly and indirectly affected by climatic factors is discussed in the analysis

results of the yield model in the “Results” section. * While we project changes in social welfare due to the direct and indirect impacts of climate change through 2050, we acknowledge that

forecast errors will increase as we move further into the future due to limitations in the temporal scope of our data. Therefore, while we believe that the trends over time are reliable, the

accuracy of the projected values should be interpreted with caution. REFERENCES * Auffhammer M, Ramanathan V, Vincent JR (2012) Climate change, the monsoon, and rice yield in India. Clim

Change 111:411–424 Article  ADS  Google Scholar  * Bae D (1999) How to manage the red pepper and control its P&Ds. Agrochem News 20(3):48–51 Google Scholar  * Challinor AJ, Watson J,

Lobell DB, Howden SM, Smith DR, Chhetri N (2014) A meta-analysis of crop yield under climate change and adaptation. Nat Clim Change 4:287–291 Article  ADS  Google Scholar  * Cho H, Cho E,

Kwon O, Roh J (2013) Climate variables and rice productivity: a semi-parametric analysis using panel regional data. Korean J Agric Econ 54(3):71–94 Google Scholar  * Choi B, Kim W, Lym H

(2018) Changes in supply and demand environment of major vegetables and countermeasures. Korea Rural Economic Institute, pp 1–202.

https://www.krei.re.kr/eng/researchReportView.do?key=355&pageType=010101&biblioId=518885&pageUnit=10&searchCnd=all&searchKrwd=&pageIndex=16&engView=Y * Choi S

(2017) An estimation of vegetable demand system utilizing. Seoul National University, Korea * Daniels S, Witters N, Belien T, Vrancken K, Vangronsveld J, Passe SV (2017) Monetary valuation

of natural predators for biological pest control in pear production. Ecol Econ 134:160–173 Article  Google Scholar  * Donath C, Grabel E, Baier D, Pfeiffer C, Bleich S, Hillemacher T (2012)

Predictors of binge drinking in adolescents: ultimate and distal factors—a representative study. BMC Public Health 12:263 Article  PubMed  PubMed Central  Google Scholar  * Gujarati DN

(2004) Basic econometrics, 4th edn. McGraw-Hill Education, New York * Hwang J (2020) An analysis of the influence of African swine fever on pork demand and supply. Gyeongsang National

University. http://www.riss.or.kr/search/detail/DetailView.do?p_mat_type=be54d9b8bc7cdb09&control_no=7703e18f1d7a43b4ffe0bdc3ef48d419&keyword= * Jang G (2002) How to manage the red

pepper P&Ds. Agrochem News 23(6):10–13 MathSciNet  Google Scholar  * Jeong H, Kim C (2014) An analysis of impacts of climate change on rice damage occurrence by insect pests and disease.

Korean J Environ Agric 33(1):52–56 Article  MathSciNet  Google Scholar  * Kim M, Park B, Park J, Seo D, Heo J (2000) Estimating the supply and demand function of major vegetables and

fruits. Korea Rural Economics Institute, pp 1–78.

https://repository.krei.re.kr/bitstream/2018.oak/13850/1/%EC%A3%BC%EC%9A%94%20%EC%B1%84%EC%86%8C%C2%B7%EA%B3%BC%EC%9D%BC%EC%9D%98%20%EC%88%98%EA%B8%89%ED%95%A8%EC%88%98%20%EC%B6%94%EC%A0%95.pdf

* Kim G, Kim T (2017) An analysis of the effect of climate change on the occurrence of rice P&Ds considering spatial dependence. Korean Assoc Agric Food Policy 2017(2):891–914 Google

Scholar  * Kim H (2020) Seasoning vegetable monthly report November 2020. Korea Rural Economics Institute Aglook, pp 1–12.

https://aglook.krei.re.kr/main/uObserveMonth/OVR0000000036?queryType=2020&queryType2=7241 * Kim J, Cheong S, Lee K, Yim J, Choi S, Lee W (2015) Yield loss assessment and determination of

control thresholds for anthracnose on red pepper. Res Plant Dis 21(1):6–11 Article  Google Scholar  * Kim T, Langpap C (2016) Agricultural landowners’ response to incentives for

afforestation. Resour Energy Econ 43:93–111 Article  Google Scholar  * Kim Y, Jung J, Ahn D (2017) Estimating the damage function of disease and insect pest of ginseng considering spatial

autocorrelation. J Rural Dev 40(4):75–95 CAS  Google Scholar  * Koung C (2014) The influence of abnormally temperatures on growth and yield of hot pepper (_Capsicum annum_ L.). Pusan

National University.

http://www.riss.kr/search/detail/DetailView.do?p_mat_type=be54d9b8bc7cdb09&control_no=9d3924a5eb18ea52ffe0bdc3ef48d419&keyword=%EA%B3%B5%EC%B2%A0%ED%91%9C%20%EB%B6%80%EC%82%B0%EB%8C%80%ED

* Kwon C, Lee S (2002) Occurrence and ecological characteristics of red pepper anthracnose. Res Plant Dis 8(2):120–123 Article  Google Scholar  * Kwon S, Kim J, Byeon Y, Bu K, Seo J, Seon

M, Seong H, Sim S, Lee J, Lim Y (2020) Global climate change forecast report. National Institute of Meteorological Sciences (NIMS), pp 1–40. http://www.nims.go.kr/?sub_num=1126 * Lee B, Kim

Y, Kim D, Kim G (2020). Agriculture. In: Bae, YJ, Lee, HJ, Jeong, BW (eds) Korean climate change assessment report 2020. Ministry of Environment, pp 149–185.

http://www.me.go.kr/home/web/policy_data/read.do?menuId=10262&seq=7563 * Lee C, Yang S (2017) Development of yield forecast models for vegetables using artificial neural networks: the

case of chilli pepper. Korea J Org Agric 25(3):555–567. https://doi.org/10.11625/KJOA.2017.25.3.555 * Lee G, Joe S, Jeon S, Kim S, Song Y (2011) The economic effect and utilization of

country-of-origin labelling in Korea. Korea Rural Economic Institute, pp 1–170.

https://repository.krei.re.kr/bitstream/2018.oak/19801/1/%EB%86%8D%EC%8B%9D%ED%92%88%20%EC%9B%90%EC%82%B0%EC%A7%80%ED%91%9C%EC%8B%9C%EC%9D%98%20%ED%9A%A8%EA%B3%BC%20%EB%B6%84%EC%84%9D%EA%B3%BC%20%ED%99%9C%EC%9A%A9%EB%8F%84%20%EC%A0%9C%EA%B3%A0%20%EB%B0%A9%EC%95%88.pdf

* Lee G, Song M, Kim S, Nam S, Heo J, Yoon J, Kim D (2016) Effects of rain-shelter types on growth and fruit quality of red pepper (_Capsicum annuum_ L. var. ‘Keummaru’) cultivation in

paddy. Korean J Hortic Sci Technol 34(3):355–362 CAS  Google Scholar  * Lee H (2015) Estimating social costs of foot-and-mouth disease outbreak in Korea: an equilibrium displacement model

approach. Seoul National University.

http://www.riss.kr/search/detail/DetailView.do?p_mat_type=be54d9b8bc7cdb09&control_no=521068606333026dffe0bdc3ef48d419&keyword=%EA%B5%AC%EC%A0%9C%EC%97%AD%20%EC%84%9C%EC%9A%B8%EB%8C%80%ED%95%99%EA%B5%90%20%EA%B7%A0%ED%98%95%EB%8C%80%EC%B2%B4%EB%AA%A8%ED%98%95

* Lee H, Kim S, Kim Y, Jeon S (2013) Outlook for consumption of subtropical vegetables and required cultivation area. Korean J Agric Sci 40(4):425–434 Google Scholar  * Lee J (2011) A

fact-finding survey on the farming status of red pepper cultivation in Cheongyang District. Kongju National University.

http://www.riss.kr/search/detail/DetailView.do?p_mat_type=be54d9b8bc7cdb09&control_no=c20aa63c89d44dc0ffe0bdc3ef48d419&keyword= * Lee S, Cho Y, Lee H (2020) Research on factors

affecting red pepper yields in Korea using Big Data: focused on the panel regression analysis and the structural equation model. Glob Bus Adm Rev 17(3):110–130.

https://doi.org/10.38115/asgba.2020.17.3.110 * Letourneau DK, Ando AW, Jedlicka JA, Narwani A, Barbier E (2015) Simple-but-sound methods for estimating the value of changes in biodiversity

for biological pest control in agriculture. Ecol Econ 120:215–225 Article  Google Scholar  * Lusk JL, Anderson JD (2004) Effects of country-of-origin labeling on meat producers and

consumers. J Agric Resour Econ 29(2):185–205 Google Scholar  * Min S, Lee M, Guk J, Kim Y, Lee J, Cha D, Jeong J, Son S, Ahn S (2020) Detection of climate change and the variation of

meteorological disasters on the Korean Peninsula. In: Kim, NW, Lee, JH, Joe, GH, Kim, SH (eds) Korean climate change assessment report 2020. Korea Meteorological Administration (KMA), pp

261–305. https://kaccc.kei.re.kr/home/kaccc/notice_view.do?bseq=9717# * Min S, Yi H, Kim K (2022) The subsurface drainage facility project: performance evaluation and economic feasibility. J

Rural Dev 45(3):51–74 Google Scholar  * Okrent A, Alston JM (2012) The effects of farm commodity and retail food policies on obesity and economic welfare in the United States. Am J Agric

Econ 94(3):611–646 Article  Google Scholar  * Oliveira CM, Auad AM, Mendes SM, Frizzas MR (2013) Crop losses and the economic impact of insect pests on Brazilian agriculture. Crop Prot

56:50–54 Article  Google Scholar  * Pareek A, Meena BL (2017) Impact of climate change on insect pests and their management strategies. In: Kumar S, Kanwat M, Meena PD (eds) Climate change

and sustainable agriculture (2017). New India Publishing Agency, New Delhi, pp 253–286 * Shin J, Yun S (2011) Impact of climate change on fungicide spraying for anthracnose on hot pepper in

Korea during 2011–2100. Korean J Agric Forest Meteorol 13(1):10–19 Article  Google Scholar  * Skendzic S, Zovko M, Zivkovic IP, Lesic V, Lemic D (2021) The impact of climate change on

agricultural insect pests. Insects 12(5):1–31 Article  Google Scholar  * Vittinghoff E (2005) Regression methods in biostatistics: linear, logistic, survival, and repeated measures models.

Springer, Heidelberg * Wooldridge JM (2010) Econometric analysis of cross section and panel data, 2nd edn. The MIT Press, Cambridge * Yang E, Cho M, Lee S, Park D, Jang Y, Shin Y, Jang G,

Jang B, Choi B, Jin Y, Kwon S, Choi G (2020) Agricultural technology guide: peppers. Rural Development Administration (RDA), pp 1–323.

https://www.nongsaro.go.kr/portal/ps/psb/psbx/cropEbookLst.ps?menuId=PS65290&stdPrdlstCode=VC&sStdPrdlstCode=VC011205# Download references ACKNOWLEDGEMENTS This work was supported by

the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2019S1A5A2A03053394). AUTHOR INFORMATION AUTHORS AND AFFILIATIONS * Department of

Agricultural Economics, Oklahoma State University, Stillwater, OK, USA Donggeun Han * Department of Management Information Systems (Bus & Econ Res Inst.), Gyeongsang National University,

Jinju, Republic of Korea Donghee Yoo * Department of Food and Resource Economics (Inst. of Agri & Life Sci.), Gyeongsang National University, Jinju, Republic of Korea Taeyoung Kim

Authors * Donggeun Han View author publications You can also search for this author inPubMed Google Scholar * Donghee Yoo View author publications You can also search for this author

inPubMed Google Scholar * Taeyoung Kim View author publications You can also search for this author inPubMed Google Scholar CONTRIBUTIONS DH contributed to data analysis and interpretation

and manuscript writing and revision; DY contributed to manuscript editing and proofreading; and TK contributed to the overall conceptual design of the manuscript, data interpretation,

manuscript writing, and revision. All authors approved the version to be published and agreed to take responsibility for all aspects of the work. CORRESPONDING AUTHOR Correspondence to

Taeyoung Kim. ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ETHICAL APPROVAL This article does not contain any studies with human participants performed

by any of the authors. INFORMED CONSENT This article does not contain any studies with human participants performed by any of the authors. ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer

Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. SUPPLEMENTARY INFORMATION INDUCTION PROCESS FOR EDM RIGHTS AND PERMISSIONS OPEN

ACCESS This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format,

as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third

party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the

article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright

holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. Reprints and permissions ABOUT THIS ARTICLE CITE THIS ARTICLE Han, D., Yoo, D. & Kim, T.

Analysis of social welfare impact of crop pest and disease damages due to climate change: a case study of dried red peppers. _Humanit Soc Sci Commun_ 10, 378 (2023).

https://doi.org/10.1057/s41599-023-01873-x Download citation * Received: 23 March 2023 * Accepted: 14 June 2023 * Published: 05 July 2023 * DOI: https://doi.org/10.1057/s41599-023-01873-x

SHARE THIS ARTICLE Anyone you share the following link with will be able to read this content: Get shareable link Sorry, a shareable link is not currently available for this article. Copy to

clipboard Provided by the Springer Nature SharedIt content-sharing initiative