Introduction

Climate change can occur once in a thousand years. However, recent abrupt and severe climate shifts have emerged as a significant concern within societies and a substantial environmental challenge. Escalating temperature, polar ice melting, global sea level rise, and shifting weather patterns, all stem from climate change. One of the pathways towards achieving sustainable development involves the advancement of the agricultural sector, a vital economic segment. Progress in almost all sectors of economy, even the industry sector is closely correlated to growth in agriculture. Looking at the experiences of leading countries in agricultural production, the utilization of capital equipment across various agricultural activities has proven to enhance the productivity of factors like land, labor, and management. This, in turn, results in decreased production costs, increased investment returns, surplus domestic supply, and expanded agricultural product exports. The world confronts a climate change crisis due to widespread and damaging human pursuits aimed at resource acquisition. The repercussions of climate change, including rising sea levels, global warming, floods, droughts, and landslides, pose substantial threats to human existence. Among economic sectors, agriculture holds a particularly critical role in ensuring the sustenance of human populations. Yet, climate change places the agricultural sector under a severe risk, jeopardizing its capacity to provide food for humanity. Hence, the principal objective of this current study is to explore the impact of two climatic variables i.e. climate change risk and climate change performance index, on the added value of the agricultural sector across 54 member countries of the climate change performance index. These countries are categorized into three groups: strong performance (16 countries), moderate performance (28 countries), and poor performance (10 countries). The study period spans from 2010 to 2020, and the quantile regression method is employed on panel data to conduct the analysis.

Methodology

The general definition of quantile regression states that if the linear regression model is assumed as the following equation, we have:

y_i=ˊx_iβ_τ+u_τi. 0<τ<1 (1)

Quant_τ(y_i|x_i)=x_iβ_τ (2) Equation (2) shows the τth conditional quantile function of the y distribution under the condition of random variables x in which the following condition holds:

Quant_τ(u_τi|x_i)=0 (3)

In the quantile regression structure, the effect of observable features on the conditional distribution is estimated through the process of minimizing the absolute value of the error element. To estimate the model coefficients, the absolute value of the errors with appropriate weighting is used:

Min Σ_yi≥ˊxiβ τy_i- ˊx_iβ+Σ_yi<ˊxiβ 1-τy_i- ˊx_iβ (4)

As mentioned, quantile regression is resistant to outliers. However, this method is not intended to recognize the heterogeneity of a country. In this research, the quantile panel regression method with fixed effects is used, which makes it possible to estimate the effects of conditional heterogeneous covariance of inflation rate stimuli, thus controlling invisible individual heterogeneities. A suitable method has been suggested by Koner (2004) for solving such problems. He considers invisible fixed effects as parameters that are jointly estimated with the effects of auxiliary variables for different quantiles. The unique feature of this method is that it introduces a penalty term in the minimization to address the computational problem of a set of parameters; The parameters are calculated as follows:

min_(α.β)Σ_k=1^KΣ_t=1^TΣ_i=1^N w_kρ_τky_it-α_i-x_it^Tβτ_k+λΣ_i^Nα_i (6)

In equation (6), i represents the number of countries (N), T represents the index for the number of observations of each country, K represents the quantile index, x is the matrix of explanatory variables, and ρ_τk is the quantile loss function. Also, W_k represents the relative weight for the kth quantile. λ is an adjustment parameter that reduces individual effects to zero to improve the performance of β estimates. If λ tends to zero, the penalty term is eliminated and a conventional fixed effects estimator is obtained. Whereas if λ tends to infinity, an estimate of the model is obtained without fixed effects. In this research, λ = 1 (Damette and Delacote, 2012).

Results and Discussion

The results of this research show that both climate change performance index variables and climate change risk have a positive and significant effect on the added value of the agricultural sector in all three functional groups and all deciles. This means that an increase in climate change performance and an increase in climate change risk, both of which represent the improvement of climate conditions in a country, which have a positive effect on the added value of the agricultural sector.

Conclusion

Nowadays, climate change crisis has become one of the most critical challenges facing humanity in the present century. Scientists and researchers attribute the main cause of climate change to destructive human activities aimed at obtaining more resources to meet their needs and desires. Global warming, rising sea and ocean levels, landslides, floods, and droughts are just some of the consequences related to the climate change crisis.

Within this context, agricultural sector, as one of the most important economic sectors for providing human food, is under the influence of climate change and could seriously endanger the future of humanity due to food resource scarcity. The main objective of this research is to examine the impact of two climate variables, namely climate change risk and climate change performance index, on the value added by the agricultural sector in 54 countries categorized into three groups: strong performance (16 countries), moderate performance (28 countries), and poor performance (10 countries) during the period from 2010 to 2020, using a multiple regression method on tabular data.

The results of this study indicate that both climate change performance index and climate change risk have a positive and significant impact on the value added by the agricultural sector in all three performance groups and throughout all decades. This means that increasing climate change performance and climate change risk, both of which signify improved climate conditions in a country, positively affect the value added by the agricultural sector.Introduction

Climate change can occur once in a thousand years. However, recent abrupt and severe climate shifts have emerged as a significant concern within societies and a substantial environmental challenge. Escalating temperature, polar ice melting, global sea level rise, and shifting weather patterns, all stem from climate change. One of the pathways towards achieving sustainable development involves the advancement of the agricultural sector, a vital economic segment. Progress in almost all sectors of economy, even the industry sector is closely correlated to growth in agriculture. Looking at the experiences of leading countries in agricultural production, the utilization of capital equipment across various agricultural activities has proven to enhance the productivity of factors like land, labor, and management. This, in turn, results in decreased production costs, increased investment returns, surplus domestic supply, and expanded agricultural product exports. The world confronts a climate change crisis due to widespread and damaging human pursuits aimed at resource acquisition. The repercussions of climate change, including rising sea levels, global warming, floods, droughts, and landslides, pose substantial threats to human existence. Among economic sectors, agriculture holds a particularly critical role in ensuring the sustenance of human populations. Yet, climate change places the agricultural sector under a severe risk, jeopardizing its capacity to provide food for humanity. Hence, the principal objective of this current study is to explore the impact of two climatic variables i.e. climate change risk and climate change performance index, on the added value of the agricultural sector across 54 member countries of the climate change performance index. These countries are categorized into three groups: strong performance (16 countries), moderate performance (28 countries), and poor performance (10 countries). The study period spans from 2010 to 2020, and the quantile regression method is employed on panel data to conduct the analysis.

Methodology

The general definition of quantile regression states that if the linear regression model is assumed as the following equation, we have:

y_i=ˊx_iβ_τ+u_τi. 0<τ<1 (1)

Quant_τ(y_i|x_i)=x_iβ_τ (2) Equation (2) shows the τth conditional quantile function of the y distribution under the condition of random variables x in which the following condition holds:

Quant_τ(u_τi|x_i)=0 (3)

In the quantile regression structure, the effect of observable features on the conditional distribution is estimated through the process of minimizing the absolute value of the error element. To estimate the model coefficients, the absolute value of the errors with appropriate weighting is used:

Min Σ_yi≥ˊxiβ τy_i- ˊx_iβ+Σ_yi<ˊxiβ 1-τy_i- ˊx_iβ (4)

As mentioned, quantile regression is resistant to outliers. However, this method is not intended to recognize the heterogeneity of a country. In this research, the quantile panel regression method with fixed effects is used, which makes it possible to estimate the effects of conditional heterogeneous covariance of inflation rate stimuli, thus controlling invisible individual heterogeneities. A suitable method has been suggested by Koner (2004) for solving such problems. He considers invisible fixed effects as parameters that are jointly estimated with the effects of auxiliary variables for different quantiles. The unique feature of this method is that it introduces a penalty term in the minimization to address the computational problem of a set of parameters; The parameters are calculated as follows:

min_(α.β)Σ_k=1^KΣ_t=1^TΣ_i=1^N w_kρ_τky_it-α_i-x_it^Tβτ_k+λΣ_i^Nα_i (6)

In equation (6), i represents the number of countries (N), T represents the index for the number of observations of each country, K represents the quantile index, x is the matrix of explanatory variables, and ρ_τk is the quantile loss function. Also, W_k represents the relative weight for the kth quantile. λ is an adjustment parameter that reduces individual effects to zero to improve the performance of β estimates. If λ tends to zero, the penalty term is eliminated and a conventional fixed effects estimator is obtained. Whereas if λ tends to infinity, an estimate of the model is obtained without fixed effects. In this research, λ = 1 (Damette and Delacote, 2012).

Results and Discussion

The results of this research show that both climate change performance index variables and climate change risk have a positive and significant effect on the added value of the agricultural sector in all three functional groups and all deciles. This means that an increase in climate change performance and an increase in climate change risk, both of which represent the improvement of climate conditions in a country, which have a positive effect on the added value of the agricultural sector.

Conclusion

Nowadays, climate change crisis has become one of the most critical challenges facing humanity in the present century. Scientists and researchers attribute the main cause of climate change to destructive human activities aimed at obtaining more resources to meet their needs and desires. Global warming, rising sea and ocean levels, landslides, floods, and droughts are just some of the consequences related to the climate change crisis.

Within this context, agricultural sector, as one of the most important economic sectors for providing human food, is under the influence of climate change and could seriously endanger the future of humanity due to food resource scarcity. The main objective of this research is to examine the impact of two climate variables, namely climate change risk and climate change performance index, on the value added by the agricultural sector in 54 countries categorized into three groups: strong performance (16 countries), moderate performance (28 countries), and poor performance (10 countries) during the period from 2010 to 2020, using a multiple regression method on tabular data.

The results of this study indicate that both climate change performance index and climate change risk have a positive and significant impact on the value added by the agricultural sector in all three performance groups and throughout all decades. This means that increasing climate change performance and climate change risk, both of which signify improved climate conditions in a country, positively affect the value added by the agricultural sector.