Temperature Rising,Growth Descending: Climate Change Impacts on Asian Economies

Abstract

This study investigates the impact of temperature and rainfall on economic growth in Asian countries using panel data regression analysis from 1991 to 2021. The results reveal a significant negative relationship between temperature and economic growth, with a 1°C rise leading to a 0.385% reduction in growth. High-temperature countries experience a more significant adverse effect compared to low-temperature countries. However, despite displaying a negative trend, rainfall does not significantly influence economic growth statistically. The study also finds differences in the impact of climate variables across different income groups. Additionally, temperature negatively affects the growth rate of agrarian countries, while rainfall does not significantly influence their growth dynamics. These findings underscore the importance of considering climate change in understanding economic growth and highlight the need for policymakers to prioritize climate risk assessment and adopt appropriate climate change mitigation and resilience strategies in Asian countries.

JEL Classification E23, O13, Q54

Keywords

Climate climate change risk economic growth temperature rainfall Asia

Introduction

Climate change is a global phenomenon with adverse effects on economic growth (Global Climate Risk Index, 2021; IPCC¹, 2007a, 2018²). It has been observed that extreme climate-induced events such as droughts and floods are expected to increase in frequency, intensity, and duration (IPCC, 2014). Consequently, climate change has gained significant attention in scholarly, social media, and political discussions (Tol, 2009). Numerous studies have focused on estimating the impact of climate change on agricultural growth, livelihoods, the environment, and various other economic sectors (Dell et al., 2012; Endalew & Sen, 2021; Khan et al., 2020; Kotz et al., 2021; Sharma, 2023; Sharma & Sen, 2021). While the impact of climate change is felt worldwide, it varies in intensity across economies (Global Climate Risk Index, 2021).³

Experts believe Asian countries are particularly vulnerable to climate change (Global Climate Risk Index, 2021;⁴ IPCC, 2018;⁵ IUCN, 2017; Mishra et al., 2020). Stern (2006) identifies three key reasons for this vulnerability: warmer climates in developing countries, dependence on agriculture for livelihoods, and limited adaptive capacity among marginalized populations. According to Stern’s projections, rising temperatures in the next 50 years will negatively affect water quality, agricultural productivity, human health, and consequently, economic growth and development. Given the vulnerability of Asian countries to climate change, it is essential to examine its impact comprehensively, especially considering the heterogeneity among various Asian countries. However, to the best of our knowledge, no study has estimated the comprehensive impact of climate change on Asian countries, considering their unique characteristics.

This study aims to fill this research gap by empirically analyzing the impact of climate change on the economic growth of selected Asian countries. The study also investigates the moderating effects of income groups and agricultural aspects on the relationship between climate change and economic growth. By focusing on temperature and rainfall as key climate variables, this study contributes to the existing literature by examining their direct impact on economic growth rather than studying individual transmission channels separately (Agovino et al., 2019; Mendelsohn et al., 2001; Olper et al., 2021).

Previous studies have predominantly focused on individual transmission channels such as agricultural productivity, social cohesion, human capital accumulation, income, migration flows, financial stability, monetary policy, and environmental degradation (Burke et al., 2015; Fishman et al., 2019; IPCC, 2019; Klusak et al., 2021; Mendelsohn, 2014; Priovashini & Mallick, 2021). Moreover, most of these studies have either focused on cross-country estimations, African countries, or a single country, thus highlighting the need for a comprehensive analysis specifically for Asian countries (Ali, 2012; Ali et al., 2019; Barrios et al., 2010; Kalkuhl & Wenz, 2020; Kotz et al., 2021; Moore & Diaz, 2015; Newell et al., 2021; Rahim & Puay, 2017; Sangkhaphan & Shu, 2019; Somanathan et al., 2021; Tol, 2009).

The present study makes a valuable contribution to the existing literature by adopting a comprehensive approach to estimating the economic effects of climate change in Asian economies. First, it examines the impact of temperature and rainfall on economic growth from 1991 to 2021, considering a significant time frame to ensure robust and reliable results. Second, the study explores the interaction between income groups and agricultural productivity with temperature and rainfall, providing insights into the differential impacts based on income levels and the nature of the economy. Third, it employs panel data regression analysis to account for heterogeneity across time and space, enhancing the validity of the findings. The results demonstrate that temperature has a significant adverse effect on the economic growth of Asian countries. Specifically, a 1°C increase in temperature is associated with a 0.385% reduction in economic growth. The impact of temperature is more pronounced in countries with higher average temperatures, followed by those with lower average temperatures. On the other hand, the effect of rainfall on economic growth is statistically non-significant. Additionally, the study reveals that the negative influence of temperature on GDP is significant for agrarian countries, while it does not have a statistically significant impact on non-agrarian countries. Fourth, the study provides strong statistical, empirical evidence considering robust methodology (panel data regression analysis) to quantify the impact of climate change on economic growth rates. These estimates may serve as a robust data point for policy implications by various governments and policymakers.

The study further contributes to the climate change literature by emphasizing its importance as a significant factor in explaining economic growth and the need for climate risk assessment in decision-making. Policymakers, economists, and analysts should pay close attention to climate change and its potential implications for economic growth. It is crucial to adopt appropriate climate change mitigation and resilience strategies at the country and regional levels, particularly in Asia, to ensure sustainable development. Furthermore, governments should allocate adequate financial support, including pre- and post-budgetary allocations to vulnerable sectors such as agriculture most affected by climate change. Though low-income and agriculture-based countries are more susceptible to climate change risk, however, it affects all kinds of economies. It is suggested that agriculture-based economies should invest more in climate-resilient agriculture practices, water conservation, crop insurance, and technology adaptation. International collaborations and global climate agreements may be instrumental to address the climate change-induced adverse effects.

The remaining article is organized in the following manner. The next section presents the vulnerability profile of Asian countries, followed by the exploration of relevant literature on climate change and economic growth, and the hypotheses for empirical estimation. The data collection sources and econometric methodology adopted in the present analysis are next. We then present the results and the discussion. Lastly, the robustness analysis and finally, the conclusions and policy implications are presented.

Vulnerability of Asian Countries: A Study Context

Asian countries are significantly vulnerable to climate change due to their heavy reliance on agriculture and natural resources, widespread poverty, and low average per capita income. The Intergovernmental Panel on Climate Change (IPCC, 2014) emphasizes that climate variability, characterized by increasing warm days and decreasing cold days, is rising in Asia. Additionally, rainfall patterns exhibit growing variability and extreme events, leading to water demand and supply imbalances across regions and countries. The detrimental impact of extreme precipitation is particularly pronounced in Bangladesh, India, Pakistan, and Sri Lanka, as highlighted by the IPCC (2014). Woetzel et al. (2020) project that Asia will account for more than two-thirds of the global gross domestic product (GDP) at risk due to climate change by 2050. Table A1 in the supplementary material (Appendix Tables A1 –A4 are accessible online) presents the vulnerability of sampled Asian countries based on the comprehensive Climate Risk Index (CRI) for the period 2000–2019 and the standalone risk score for 2019. It also includes the respective global rankings of these countries based on their vulnerability.

Table A1 in the supplementary material reveals that, for the period 2000–2019, 10 countries (out of 20) ranked within the top 50, indicating high vulnerability to climate change risk. In 2019, 11 countries ranked within the top 50. Notably, 12 of the 20 countries experienced a decline in their CRI scores and rankings in 2019 compared to the collective period of 2000–2019. Moreover, Asia is home to some of the fastest-growing economies of the twenty-first century, including China and India. As the most populous country, China ranks considerably high in the Global Climate Risk Index 2020 and 2021, securing 33rd and 32nd positions, respectively. India, on the other hand, ranked seventh in vulnerability for the period 2000–2019, indicating a high susceptibility to climate change. India witnessed 11 out of 15 warmest years in the last 18 years as reported by the Earth Observatory in 2019. Other countries such as Pakistan, Bangladesh, Myanmar, Cambodia, Vietnam, the Philippines, Indonesia, and China faced increased risks of rice production losses due to rising heat (Wassmann et al., 2009).

Literature Review

The literature concerning the climate-economic growth nexus in climate econometrics offers valuable insights regarding the potential impact of climate change on economic activity. Earlier studies highlight the detrimental impacts of climate change on economic growth, supported by theoretical frameworks and empirical evidence. This literature review aims to comprehensively review the theoretical framework and existing empirical studies examining the impacts of climate change on the economy.

Theoretical Framework

Previous studies have argued that factors such as erosion, floods, droughts, species extinction, and fatalities resulting from extreme weather events cause permanent damage to economic growth. Furthermore, the diversion of resources to mitigate the impacts of these extreme events is found to impede investments in economic and physical infrastructures, research and development (R&D), and human capital, thereby hindering overall economic growth (Ali, 2012).

Theoretically, the linkage between climate change and economic growth is explored from macroeconomic and microeconomic perspectives. From the macroeconomic perspective, the focus is on the output level such as agricultural yields and the overall growth potential of the economy (Chen & Yang, 2019; Schlenker & Roberts, 2009). The macrolevel perspective suggests that climate change can influence economic growth by impacting investments, capital, and institutions that play an important role in growth (Zhang et al., 2018). For example, when there is a steady savings rate, a decline in output caused by climate change results in a proportionate decrease in investment. This, in turn, lowers future production (capital accumulation effect) and, in nearly all instances, reduces future consumption per person (Fankhauser & Tol, 2005). In addition, financial resource allocation to mitigate climate change may lower the available funds for R&D, physical infrastructure, and human development (Ali, 2012). From the microeconomic perspective, factors such as physical and cognitive labor productivity, labor force dynamics, conflict, education, and health are some of the determinants of economic growth (Chang et al., 2023; Chen & Yang, 2019).

Further, the research within the climate econometrics domain can be categorized into aggregate macroeconomic output, top-down studies, and sectoral studies (Chang et al., 2023; Rising et al., 2022). Regarding macroeconomic output, previous studies suggest that climate change may impact the GDP growth rate, not just the GDP level (Chang et al., 2023; Newell et al., 2021). The level effect assumes that increase in temperature only temporarily affects the economic output level, and once the temperature reverts to its pre-warming state, the level effect disappears. In contrast, the growth effect suggests that a temperature rise permanently affects the growth of economic output, persisting even if the temperature returns to its pre-warming value, with the impact compounding over time. Top-down studies concentrate on the relationship between climate change and GDP at the macroeconomic level (Colacito et al., 2021; Newell et al., 2021). On the other hand, sectoral studies focus on specific areas such as agriculture, energy demand, mortality, crime, and labor productivity (Auffhammer, 2018; Hsiang et al., 2017). With respect to the adverse impact of climate change on agriculture, temperature may alter the length of the cropping (growth) season, whereas low and high rainfall may cause droughts and floods, respectively. Drought reduces soil moisture and leads to adverse impact on crop productivity. In turn, both these effects will adversely affect the food prices and its supply, affecting the growth dynamics (Akram, 2013).

Building on this theoretical foundation, the following section provides comprehensive reviews of existing studies that examine the effects of climate change on economic growth.

Empirical Studies

Climate Change and Economic Growth

Numerous studies examining the impact of climate change on economic growth consistently demonstrate adverse effects at the country level. Examining the influence of rainfall trends on economic growth in Africa, Barrios et al. (2010) utilized a cross-country panel climatic dataset. The findings indicate that a decline in rainfall negatively impacts the growth rate. Burke et al. (2015) conducted an analysis demonstrating that climate change, specifically rising temperatures, could lead to a staggering reduction of up to 20% in per capita income by the year 2100. Expanding on this, Kalkuhl and Wenz (2020) conducted a large-scale study across 1,500 regions in 77 countries, revealing that an average temperature increase of 3.5°C could lower economic output by 7–14% by the year 2100.

Climate change poses significant challenges for Asian countries, particularly regarding droughts and flooding. IPCC (2007b) highlighted the projected rise in temperatures, ranging from 0.5°C to 2°C in 2030 and 1°C to 7°C in 2070. Bangladesh, for instance, experiences a 15% annual drought on cultivable land (Ahmed et al., 2005), while India is also vulnerable to drought, with increasing incidences over time (Prabhakar & Shaw, 2008). Examining the impact of rainfall deficiency on economic growth, Sharma and Sen (2021) conducted an empirical analysis focusing on the districts of Madhya Pradesh, an Indian state. Their findings demonstrate that droughts, potentially induced by climate change, adversely affect agriculture and the overall economic growth rate. Several studies have investigated the impact of natural disasters on aggregate economic growth (Felbermayr & Gröschl, 2014; Karbownik & Wray, 2019; Loayza et al., 2012; Panwar & Sen, 2019; Sangkhaphan & Shu, 2019).

Furthermore, Rahim & Puay (2017) highlighted the adverse impacts of climate change on economic growth in Malaysia. Zhang et al. (2018) examined the impact of climate change on manufacturing firms in China, while Somanathan et al. (2021) estimated the effect of temperature on the manufacturing sector in India. Both studies found adverse impacts on the respective sectors. The Asian region is characterized by its high population density and heavy reliance on climate-sensitive sectors such as agriculture, fisheries, and tourism. By examining the relationship between climate change and economic growth in Asian economies, this study aims to provide empirical evidence and contribute to understanding the complex dynamics between climate change and economic development in the region. Therefore, we propose the following hypothesis:

H₁: Climate change has a significant negative impact on economic growth in Asian economies.

Moderating Role of Income in the Relationship between Climate Change and Economic Growth

Recent literature offers valuable insights into the relationship between climate change and economic growth, focusing on the varying effects experienced by countries based on their income. Raddatz (2009) conducted an estimation from 1975 to 2006, revealing that global climate change has a more pronounced impact on the real GDP per capita of small countries compared to larger ones. This finding aligns with similar observations made by Moore and Diaz (2015) and Tol (2009), whose empirical estimates suggest that the adverse impacts of rising temperatures are more significant in low-income countries as compared to high-income countries. This discrepancy is attributed to the higher adaptive capabilities of wealthier nations. In line with these findings, Kotz et al. (2021) utilized a panel data regression model covering 40 years and 1,537 regions worldwide to estimate the effects of higher annual temperature and its variability on macroeconomic growth rates. Their study demonstrates that a 1°C increase in temperature reduces the overall growth rate by 5%, and for low-income regions, the reduction reaches 12%. However, in contrast to these findings, Newell et al. (2021) show that temperature and rainfall have similar adverse effects on poor and rich countries. Building on this, we hypothesize that:

H_2a: In the context of Asian economies, the adverse impacts of climate change on economic growth will be more pronounced in low-income countries compared to high-income countries.

Moderating Role of Agrarian Economy in the Relationship between Climate Change and Economic Growth

Previous studies also emphasized that agrarian economies experience a stronger adverse impact of climate change on economic growth compared to non-agrarian economies. Ali (2012) conducted a cointegration analysis to explore the effects of climate change on economic growth in Ethiopia, a predominantly agrarian economy. The findings revealed an adverse impact, indicating that changes in rainfall magnitude and variability exert long-term drag impacts on output levels. In Pakistan, another agrarian economy, climate change-induced crop production losses have been estimated to have significant negative spill-over effects on economic sectors and overall economic growth. Khan et al. (2020) projected that these losses could reach a staggering $19.5 billion by 2050. Moreover, Ali et al. (2019) found that temperature had a higher adverse impact on Pakistan’s economic growth rate during the period 1980–2013. Sangkhaphan and Shu (2019) observed negative impacts of rainfall on aggregate, agriculture, and service sector growth rate of Thailand. Similarly, tea production has been significantly influenced by temperature variations in Sri Lanka. Gunathilaka et al. (2017) demonstrated the impact of temperature on tea production, highlighting the vulnerability of agrarian economies to climate change. Furthermore, Marambe et al. (2015) found that both temperature and rainfall played crucial roles in influencing food security in Sri Lanka.

The adverse impacts of climate change on the agriculture sector have been widely documented. Factors such as land degradation, reduced crop yield, decreased soil moisture, and evapotranspiration contribute to this negative impact. IPCC (2019) and Wheeler and Braun (2013) have highlighted the significant role of these factors in shaping the adverse effects of climate change on agrarian economies. Central and South Asian countries, for instance, have experienced a 30% decrease in crop yield due to increased rainfall during the summer monsoon (Mackay, 2008). This emphasizes the vulnerability of agrarian economies in these regions to climate change and its subsequent impact on economic growth. Accordingly, we hypothesize that:

H_2b: In the context of Asian economies, the adverse impact of climate change on economic growth will be more in agrarian economies compared to non-agrarian countries.

Data and Empirical Methodology

Instrumentation of Variables

In this study, we focused on 20 Asian countries for which comprehensive data on independent variables (indicators) were available. The sample includes major economies in Asia, including China, India, Japan, and Bangladesh, among others. The study period is 31 years (1991–2021) as per the data availability for the selected variables. GDP growth rate at market prices based on constant local currency is considered a substitute for economic growth, and temperature (TEMP) and rainfall (RAIN) are proxies for climatic conditions in Asia. The control variables include gross capital formation as a percentage of GDP (CAPITAL), education as a percentage of gross senior secondary school enrollment (EDUCATION), energy use as a kilogram of oil equivalent per capita (ENERGY), and exports of goods, services, and primary income (EXPORT). In addition, inflation in consumer prices (INFL), population density defined as population per square kilometer of land area (POP), and trade openness measured as percentage trade of GDP (TRADE) were also considered. These control variables were included following the standard literature, key studies (Loayza et al., 2013; Panwar & Sen, 2019; Sharma & Sen, 2021, among others), and considering their importance in climate change and economics theory and literature. The interaction effect is separately examined with income group and agriculture dummies. The study analyzes 20 countries, and selected indicators and income classification are shown in Table A2 in the supplementary material. Temperature and rainfall statistics for Asian countries were collected from Climate Change Knowledge Portal. While, data pertaining to GDP, CAPITAL, EDUCATION, TRADE, ENERGY, EXPORT, INFL, POP, and agriculture were obtained from the World Health Organization (WHO) portal. Table 1 presents the descriptive statistics of all the variables used in the present analysis.

Table 1.

Descriptive Statistics of Temperature and Rainfall (1991–2021).

Country Name	Maximum Temperature	Minimum Temperature	Maximum Rainfall	Minimum Rainfall	Maximum–Minimum Temperature	Maximum–Minimum Rainfall	Mean Temperature	Mean Rainfall
Armenia	21.5355	7.235	55.2894	32.2306	14.3005	23.0588	9.9494	45.0614
Bahrain	42.979	26.015	9.5563	2.1903	16.964	7.366	29.6963	5.9061
Bangladesh	32.0878	24.6898	241.7033	148.9607	7.398	92.7426	26.0851	182.9076
Cambodia	35.8436	26.9879	222.6043	129.2202	8.8557	93.3841	28.7532	161.0064
China	17.2343	6.368	53.6026	43.1611	10.8664	10.4415	8.2549	47.8405
India	30.2282	23.9767	96.6811	69.6588	6.2516	27.0224	25.305	85.4099
Indonesia	34.1098	25.8705	300.4484	182.9922	8.2392	117.4562	27.1643	243.2727
Israel	30.8532	18.5243	33.155	10.6281	12.3288	22.5269	21.6657	20.2188
Japan	19.1835	10.6002	158.5982	112.7448	8.5833	45.8534	12.496	136.6969
Jordan	30.2257	17.4246	12.7896	5.8419	12.8011	6.9476	20.6646	8.3032
Kuwait	40.9173	25.3212	15.1141	4.2369	15.5961	10.8771	28.3705	9.3315
Malaysia	36.7565	25.4738	316.2797	202.233	11.2827	114.0467	27.1679	263.5468
Mongolia	15.5406	–0.7299	24.0765	14.5376	16.2705	9.5389	2.6056	18.4578
Pakistan	26.9907	19.5464	35.6005	16.0181	7.4444	19.5824	21.3631	26.0066
Philippines	35.5956	25.6141	271.1043	157.9076	9.9815	113.1967	27.1001	216.1305
Saudi Arabia	38.2352	23.9194	9.9568	4.4409	14.3158	5.516	27.0826	5.8536
Singapore	39.1993	27.5515	253.5167	112.2163	11.6478	141.3004	29.3743	216.8486
Sri Lanka	35.2812	26.8244	174.8161	116.4442	8.4568	58.3719	28.2487	142.1613
Thailand	36.5606	26.0668	154.9374	110.5379	10.4939	44.3995	27.9551	129.8218
Vietnam	33.3207	23.8887	186.9283	133.4373	9.4321	53.491	25.929	159.4211

Source: Climate Change Knowledge Portal.

Model Specification and Methodology

Panel regression is used to examine the relationship between climate and economic growth, allowing the researcher to study cross-section effects, that is, time and space elements. Moreover, panel regression takes care of heterogeneity as it considers subject-specific variables. Random and fixed effects are two models in panel regression. When an intercept drawn from a large population is time-invariant, then it is a random effect model; when each entity has its own intercept to represent special characteristics, then it is called a fixed effect model. The suitability of the model for analysis of panel data is identified using the Hausman test. In this test, the null hypothesis is that the fixed effect model is applicable, while the alternative hypothesis states that the random effect model is applicable. The result pertaining to the Hausman test is duly reported in Tables 2 and 3. As the p value of the Hausman test is less than 5% hence, the decision criterion is to accept the alternative hypothesis. Therefore, the study uses a fixed effect model (both country and year fixed effects). To control the time-invariant country-specific characteristics, we include country-fixed effects. Time-fixed effects control for the time trends common to temperature and rainfall activities. Panel fixed effects models allow to control the unobserved time-invariant heterogeneity and time-variant shocks that are common for all cross-sectional units.

Table 2.

Panel Regression Results—Part A (Effect of Temperature and Rainfall).

Dependent Variable is Economic Growth	(1)	(2)	(3)	(4)	(5)
Effect of temperature and rainfall
TEMP	–0.385^a
TEMP	(0.149)
RAIN	0.000
RAIN	(0.007)
Effect on high-temperature countries
TEMP		–0.440^b
TEMP		(0.219)
Effect on low-temperature countries
TEMP			–0.223
TEMP			(0.198)
Effect on heavy-rainfall countries
RAIN				0.000
RAIN				(0.008)
Effect on light-rainfall countries
RAIN					–0.009
RAIN					(0.055)
L_CAPITAL	3.433^b	4.621^a	6.238^a	4.215^a	3.096^a
	(0.790)	(1.148)	(1.191)	(0.864)	(1.067)
L_EDUCATION	0.700^a	–4.716^a	1.799^b	–2.268^b	2.351^b
	(0.563)	(1.656)	(0.738)	(1.021)	(0.940)
L_ENERGY	–3.749^a	2.700	–7.268^a	–1.182	–5.538^b
L_ENERGY	(1.428)	(3.648)	(2.141)	(1.253)	(2.356)
L_EXPORT	1.061	0.935	2.005^b	0.215	2.525^b
L_EXPORT	(0.551)	(1.008)	(1.062)	(0.754)	(1.260)
INFL	–0.001	–0.294^a	–0.002^b	–0.188^a	–0.001
INFL	(0.001)	(0.078)	(0.001)	(0.036)	(0.001)
L_POP	–3.801^b	–3.358	–1.523	–1.902	–3.727
L_POP	(1.650)	(2.500)	(2.039)	(2.967)	(2.541)
L_TRADE	–0.759	2.586^b	0.490	0.955	–0.690
	(1.035)	(1.183)	(1.041)	(0.919)	(1.386)
C	23.306	–13.734	–12.668	10.918	–13.896
C	(14.874)	(17.731)	(19.111)	(18.330)	(31.061)
Adjusted R²	0.534	0.394	0.603	0.691	0.524
F-statistic	10.633	10.883	7.994	11.775	5.950
Hausman Test	47.564^a	23.575^a	24.398^a	28.945^a	37.921^a

Notes: Standard errors are in parentheses. ^aindicates significance at 1% and ^bindicates significance at 5%. While running regression models, the observations of CAPITAL, EDUCATION, ENERGY, EXPORT, POP, and TRADE are transformed into natural logarithmic values.

The Effect of Temperature and Rainfall on Economic Growth

The following regression model is applied to determine the effect of climate on the economic growth of Asian countries and to test the first hypothesis of the study.

y_{i} = β_{0} + β_{1} x_{i} + β_{2} z_{i} + ε_{i},

(1)

where $ε_{i} \begin{matrix} i i d \\ ~ \end{matrix} N (0, σ_{ε}^{2})$

In Equation (1), y_i signifies the GDP growth rate of country i over the period 1991–2021. x_i signifies the temperature and rainfall variables of country i over the same year period, and z_i signifies the vector of control variables of country i over the same year period.

The study of Gornall et al. (2010) and Barrios et al. (2010) suggest the importance of temperature and rainfall intensity in determining the influence on the economic growth of the country. Hence, it is worthwhile to study the effect of climate on economic growth when countries are classified based on temperature and rainfall intensity. The author segregated the countries into high-temperature ( $t e m p_{i}^{h i g h}$ ) and low-temperature ( $t e m p_{i}^{l o w})$ countries, and heavy-rainfall ( $r a i n_{i}^{h e a v y})$ and light-rainfall ( $r a i n_{i}^{l i g h t}$ ) countries. High-temperature countries are those whose mean temperature is above the sample median of the mean temperature of all the countries considered in the study, whereas countries having a mean temperature below the sample median of the mean temperature of all the countries are assumed to be low-temperature countries. Similarly, a country that is a “heavy-rainfall region” is defined as having above-median mean rainfall of all the countries, and the remaining countries are treated as “light-rainfall region” countries.

The regression models used are defined below:

y_{i} = β_{01} + β_{11} t e m p_{i}^{h i g h} + β_{21} z_{i} + ε_{i}

(2)

y_{i} = β_{02} + β_{12} t e m p_{i}^{l o w} + β_{22} z_{i} + ε_{i}

(3)

y_{i} = β_{03} + β_{13} r a i n_{i}^{h e a v y} + β_{23} z_{i} + ε_{i}

(4)

y_{i} = β_{04} + β_{14} r a i n_{i}^{l i g h t} + β_{24} z_{i} + ε_{i}

(5)

The Interaction Effect of Income Group with Temperature and Rainfall on Economic Growth

Given the importance of the level of the country’s income and agriculture output and to test the second hypothesis, this study runs a regression model having an interaction effect of income group with temperature and rainfall. According to the World Bank country classification, the countries are classified into four income groups: low-income, lower-middle income, upper-middle income, and high-income countries. However, due to the unavailability of data for low-income countries, the author ignored these countries. The three-income group category factor can be exemplified in the regression model by introducing two dummy explanatory variables by employing the following coding scheme (Table A3 in the supplementary material):

First, we regress Equation (6) for the temperature and rainfall separately and sequentially to analyze the interaction effect of temperature and rainfall with the income categories.

y_{i} = α_{0} + α_{1} x_{i} + α_{2} d x_{i} + α_{3} z_{i} + ε_{i},

(6)

where $ε_{i} \begin{matrix} i i d \\ ~ \end{matrix} N (0, σ_{ε}^{2})$

y_i, x_i, and z_i are the same as explained for Equation (1), while d represents income group dummy. D₁ and D₂ signify lower- and upper-middle income countries, respectively (Table A3 in the supplementary material). The regression equation (6) is a simple model with no lags of temperature and rainfall. For a better understanding of the dynamics of climate effects on economic growth, the author also considers one period lag of temperature and rain. The regression model is defined as below:

y_{i} = α_{01} + α_{11} x {(- 1)}_{i} + α_{21} d x {(- 1)}_{i} + α_{31} z_{i} + ε_{i},

(7)

The Interaction Effect of Agriculture with Temperature and Rainfall on Economic Growth

According to the International Standard Industrial Classification of All Economic Activities (ISIC) division 1–5, agriculture includes forestry, hunting, and fishing, along with crop cultivation and livestock production. This variable enables the author to concentrate on the value added to agriculture, which is calculated by subtracting the value of peripheral inputs from the agricultural sector output. To test the third hypothesis of the study, the following regression analysis was performed:

y_{i} = α_{02} + α_{12} x_{i} + α_{22} d x_{i} + α_{32} z_{i} + ε_{i},

(8)

Countries with agriculture value added above the sample median were treated as agrarian countries while countries with agriculture value added below the sample median were considered as non-agrarian countries. D₃ is the dummy for the non-agricultural countries (Table A4 in the supplementary material).

Equation (8) is further modified to analyze the moderation effect of temperature and rainfall (lags) with the agricultural and non-agricultural countries.

y_{i} = α_{03} + α_{13} x {(- 1)}_{i} + α_{23} d x {(- 1)}_{i} + α_{33} z_{i} + ε_{i},

(9)

The results obtained are presented in the next section.

Empirical Results

The Effect of Climate Variables (Temperature and Rainfall) and the Interaction Effect of Income Group with Climate Variables (Temperature and Rainfall) on Economic Growth

Table 2 presents the findings of regression analyses pertaining to the relationship between climate and economic growth in Asian countries. In Column (1), the analysis is conducted without considering any interaction effect of income and agriculture. The results indicate a significant negative effect of temperature on the economic growth of Asian countries. A 1°C increase in temperature leads to a reduction in economic growth by 0.385% at 1% significance level. Columns (2)–(5) examine the impact of climate change on economic growth in countries classified based on temperature and rainfall patterns. In high-temperature countries (Column 2), a 1°C rise in temperature results in a relatively higher and significant decrease in the economic growth rate (–0.440%) in comparison to the low-temperature countries (Column 3), which observed a decline of 0.223%. These findings are similar as theoretically expected that high temperatures are detrimental to the growth dynamics. Notably, rainfall failed to influence the economic growth rate significantly (Column 1). However, when countries are classified based on rainfall intensity, as expected, an adverse (–0.009) but no statistically significant effect on economic growth is observed for light-rainfall countries (Column 5). Moreover, the study shows positive (statistically non-significant) rain coefficients in Columns (4), which represent heavy rainfall countries, which is no surprise. Rainfall may be instrumental in economic growth and is expected to positively relate to the country’s growth if the contribution of agriculture to the economy is substantial (Sharma & Sen, 2021). Considering these results, it can be concluded that higher temperatures have detrimental impacts on economic growth, while the rainfall intensity does not show any significant impact on the economic growth rate.

The study further explores the interaction effect of income group with temperature and rainfall on economic growth. In Table 3, Columns (6) and (7) show the results of moderation analysis for temperature and rainfall respectively, where dummy D₁ is for lower-middle income countries and D₂ represents the upper-middle income countries. As a priori expected, the findings reveal a negative influence of an interaction between temperature and the various income group countries. The lower-middle income and upper-middle income countries’ growth rate is lowered by 0.001 and 0.04, respectively. This indicates that those countries with lower to upper incomes are relatively more affected than high-income countries. These findings also hold for the lagged temperature effect (Column 8). These results are supported by Dell et al. (2009) that poorer countries are relatively more impacted than rich countries due to lower production output and consumption reduction. Broadly, it may be concluded that the results obtained corroborate the theoretical expectations. Regarding rainfall, the results (Column 7) indicate that the interaction effects are statistically non-significant. The lower-middle and upper-middle income countries observed a 0.027 and –0.007% effect on the growth rates. Furthermore, the study considers the lagged effect of rainfall with the interaction effect of the income group, as presented in Column (9), showing consistent outcomes even with lagged rainfall.

Table 3.

Panel Regression Results—Part B (Interaction Effect of Temperature and Rainfall with Income and Agriculture).

Dependent Variable is Economic Growth	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)
Interaction effect of income group with temperature
TEMP	–0.373^a
TEMP	(0.144)
TEMP * D₁	–0.001
TEMP * D₁	(0.145)
TEMP * D₂	–0.041
TEMP * D₂	(0.133)
Interaction effect of income group with rain
RAIN		–0.014
RAIN		(0.018)
RAIN * D₁		0.027
RAIN * D₁		(0.021)
RAIN * D₂		0.007
RAIN * D₂		(0.023)
Interaction effect of income group with one period lag of temperature
TEMP (–1)			–0.546^a
TEMP (–1)			(0.174)
TEMP (–1) * D₁			–0.208
TEMP (–1) * D₁			(0.121)
TEMP (–1) * D₂			–0.160
TEMP (–1) * D₂			(0.132)
Interaction effect of income group with one period lag of rain
RAIN (–1)				0.019
RAIN (–1)				(0.017)
RAIN (–1) * D₁				0.004
RAIN (–1) * D₁				(0.020)
RAIN (–1) * D₂				0.025
RAIN (–1) * D₂				(0.022)
Interaction effect of agriculture with temperature
TEMP					–0.075
TEMP					(0.343)
TEMP * D₃					–0.143
TEMP * D₃					(0.182)
Dependent Variable is Economic Growth	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)
Interaction effect of agriculture with one period lag of temperature
TEMP (–1)						0.387
						(0.432)
TEMP (–1) * D₃						–0.042
						(0.236)
Interaction effect of agriculture with rain
RAIN							–0.009
RAIN							(0.013)
RAIN * D₃							0.023
RAIN * D₃							(0.016)
Interaction effect of agriculture with one period lag of rain
RAIN (–1)								0.024
								(0.025)
RAIN (–1) * D₃								0.016
								(0.023)
L_CAPITAL	3.432^a	3.538^a	2.717^a	3.134^a	3.558^a	2.996^a	3.515^a	3.114^a
	(0.696)	(0.684)	(0.679)	(0.661)	(0.749)	(0.709)	(0.805)	(0.761)
L_EDUCATION	0.749	1.054^b	1.264^b	1.413^b	0.814	1.231^b	1.032^b	1.400^a
	(0.665)	(0.633)	(0.646)	(0.618)	(0.554)	(0.505)	(0.496)	(0.489)
L_ENERGY	–3.791^a	–3.994^a	–1.157	–1.175	–3.583^b	–1.026	–3.893^a	–1.108
L_ENERGY	(1.232)	(1.231)	(1.239)	(1.244)	(1.432)	(0.890)	(1.402)	(1.021)
L_EXPORT	1.120^b	1.110^b	0.481	0.267	1.227^b	0.415	1.098^b	0.255
L_EXPORT	(0.626)	(0.603)	(0.615)	(0.599)	(0.555)	(0.566)	(0.553)	(0.541)
INFL	–0.001	–0.001	–0.032^a	–0.029^a	–0.001	–0.030^b	–0.001	–0.029
INFL	(0.001)	(0.001)	(0.009)	(0.009)	(0.001)	(0.013)	(0.001)	(0.013)
L_POP	–3.820^a	–4.883^a	–1.418	–2.191	–4.000^b	–1.640	–4.795^a	–2.132
L_POP	(1.472)	(1.426)	(1.488)	(1.444)	(1.669)	(1.346)	(1.656)	(1.311)
L_TRADE	–0.799	–0.866	–0.799	–0.626	–0.961	–0.843	–0.888	–0.642
	(0.791)	(0.779)	(0.784)	(0.776)	(1.048)	(0.869)	(1.033)	(0.839)
C	22.800	18.959	14.323	1.682	26.541	22.780	18.366	1.455
C	(14.508)	(14.656)	(14.436)	(14.519)	(15.887)	(14.811)	(14.479)	(10.845)
Adjusted R²	0.534	0.529	0.572	0.552	0.537	0.576	0.529	0.569
F-statistic	10.437	10.239	11.961	11.834	10.799	12.372	10.417	12.048
Hausman test	38.924^a	49.353^a	45.348^a	60.301^a	43.269^a	46.008^a	45.767^a	58.732^a

The Interaction Effect of Agriculture with Temperature and Rainfall on Economic Growth

Furthermore, the study analyzes the interaction effect of agriculture value added (above the sample mean) with temperature (Table 3, Columns 10 and 11) and rainfall (Columns 12 and 13) on economic growth rate. The coefficient for temperature in agrarian countries is negative 0.143 (and significant) as compared to non-agrarian countries, indicating that higher temperature has an adverse effect on the economic growth of these countries. The study also considers the lagged effect of temperature with the interaction effect of agriculture group as seen in Column 11. The lagged effect of temperature is negative (–0.042) and significant for agrarian Asian countries. These results are aligned with the results of previous studies.

On the other hand, current period rainfall (Column 12) does not have any significant influence (–0.023) on the economic growth rate of agrarian Asian countries. These results replicate for the lagged rainfall effect (Column 13) as the coefficient obtained is –0.016 and is also statistically not significant. Largely, the impact of rainfall interaction with agrarian countries is in line with a prior expectation that it may yield somewhat positive or neutral impacts.

Discussion

Temperature and Rainfall

The results of the present study (Table 2) provide evidence of a negative and significant impact of temperature on Asia’s economic growth. These findings align with previous research conducted by Dell et al. (2012), Burke et al. (2015), Acevedo et al. (2018), Kalkuhl and Wenz (2020), and Kotz et al. (2021) supporting the notion that higher temperatures have a detrimental impact on economic growth. Higher temperatures may lower total factor productivity and demand, ultimately reducing the economic growth rate (Moore & Diaz, 2015). Further, the present study reveals that the coefficient of rain is negative, indicating a potential adverse impact. However, it is not statistically significant. This finding is supported by previous studies by Ali (2012), Barrios et al. (2010), Lanzafame (2014), and Sangkhaphan and Shu (2019). However, in climate change and disasters literature, there is a diverse view about the aggregate economic impacts of rainfall. For example, some suggest that rainfall may not necessarily have an adverse impact on the economic growth rate (Lanzafame, 2014). Other studies advocate that it may adversely affect certain sectors of the economy, such as agriculture and manufacturing (Sharma & Sen, 2021). However, their limited linkages with the aggregate economy may neutralize such a sectorial adverse effect (Pelling et al., 2002; Sharma & Sen, 2021). Rain may have a positive impact too as highlighted by Panwar and Sen (2019). The authors argue that previous year’s deficiency of rainfall may be compensated by the current year’s rain, and it may be beneficial for the agriculture sector exerting spillover effects on the other sectors of the economy. Therefore, the present results with respect to the neutral (non-significant) effect of rainfall on economic growth in Asian economies may be justified.

Income Group

The coefficient for the main effect of temperature was –0.373, suggesting that an increase in temperature is associated with a decrease in economic growth in Asian countries (Table 3, Column 1). This finding aligns with the expectation and broader literature that higher temperatures have a detrimental impact on economic growth. The interaction effect between temperature and income group countries are adverse though plausible (Table 3). It indicates that the relationship between temperature and economic growth varies across different income groups. Poor countries are at high risk of experiencing reduced growth due to temperature adversity, which can manifest through adverse effects on agricultural, industrial, or political stability (Abidoye & Odusola, 2015; Dell et al., 2012). Also, export growth of agriculture products and manufacturing may be adversely affected lowering the economic growth rates in poor countries (Jones & Olken, 2010). The findings of the present study also get support from previous empirical studies conducted by Kotz et al. (2021), Raddatz (2009), and Tol (2009), which also estimate that poorer countries are more vulnerable and reactive to climate change. It may be due to limited resources for adaptation. In addition, Acevedo et al. (2020) also estimate that low-income countries are more affected relative to high-income countries, as investments, labor productivity, and outputs (both agricultural and industrial) decline. Furthermore, the study also examined the interaction effect of income groups with a one period lag of temperature on economic growth. The results reveal that the effect of temperature is negative, however, non-significant statistically.

With respect to rainfall, the results indicate that the interaction effects are statistically non-significant, suggesting that the influence of rainfall on economic growth does not significantly differ across various income groups. The study also extends the analysis and investigates the interaction effect of income groups with a one period lag of rainfall on economic growth. The results are similar to some studies such as Lanzafame (2012), which indicates that in Africa (a low-income region), rainfall is non-significant to influence economic growth rates. Berlemann and Wenzel (2018) show that rainfall in low-income economies is detrimental in comparison to high-income economies.

Agrarian Economy

The results show that agrarian economies exhibit a negative (though non-significant) relationship between temperature (and its lag) and economic growth compared to non-agrarian economies (Table 3). These results are well supported by Ali (2012), Ali et al. (2019), Casey et al. (2023), Gunathilaka et al. (2017), and Kalkuhl and Wenz (2020). The adverse impacts of temperature on the agriculture sector are due to land degradation, reduced crop yield, decreased soil moisture, and evapotranspiration (IPCC, 2019; Wheeler & Braun, 2013). Also, climate change may lower the agriculture output by reducing moisture, affecting the quality and growth of some crops (Mahato, 2014; Rasul et al., 2011). Further, the interaction effect of agricultural value-added and rainfall has a non-significant impact on economic growth. Panwar and Sen (2019) and Sharma and Sen (2021) argue that such results are plausible as higher rainfall in a region/country may compensate for the lower rainfall of previous farming seasons/years, leaving no adverse effects or even positive effects (Levine & Yang, 2014) on agricultural yield. Though the impact of high rainfall on agriculture may be detrimental, at the aggregate level, it may be non-significant (Damania et al., 2020), especially if the agriculture sector is not contributing much to the overall economic output.

Robustness Analysis

The study conducts a robust sensitivity analysis of the results obtained in two ways. First, it uses the simple panel least square analysis and reports the findings for climate variables’ effect on growth rates and their interaction effect with income and agriculture economies in Tables 4 and 5, respectively. Panel least square is the simplest analysis but has the potential to inform the direction and strength of the baseline results (panel fixed effects, Tables 2 and 3).

Table 4.

Panel Regression Results—Part A (Effect of Temperature and Rainfall—Panel Least Square).

Dependent Variable is Economic Growth	(1)	(2)	(3)	(4)	(5)
Effect of temperature and rainfall
TEMP	–0.002^a
TEMP	(0.002)
RAIN	–0.090
RAIN	(0.023)
Effect on high-temperature countries
TEMP		–0.445^a
TEMP		(0.068)
Effect on low-temperature countries
TEMP			–0.152
TEMP			(0.044)
Effect on heavy-rainfall countries
RAIN				0.010^b
RAIN				(0.004)
Effect on light-rainfall countries
RAIN					0.032
RAIN					(0.017)
L_CAPITAL	3.749	2.991^a	6.610^a	5.268^a	3.935^a
	(0.630)	(0.833)	(1.012)	(0.826)	(0.934)
L_EDUCATION	–1.816	–2.025^a	–1.686^a	–2.277^a	–0.748^a
	(0.428)	(0.914)	(0.551)	(0.704)	(0.707)
L_ENERGY	–0.585	–0.372	–0.950^b	–0.537	–0.112
L_ENERGY	(0.238)	(0.237)	(0.410)	(0.358)	(0.434)
L_EXPORT	–0.001	–0.315	–0.139	–0.566	–0.018
L_EXPORT	(0.121)	(0.197)	(0.176)	(0.240)	(0.187)
INFL	–0.001	–0.198^a	–0.001	–0.170	–0.001
INFL	(0.001)	(0.047)	(0.001)	(0.037)	(0.001)
L_POP	0.304	0.266	0.907^a	0.104	0.119
L_POP	(0.106)	(0.183)	(0.241)	(0.143)	(0.153)
L_TRADE	1.500	0.947	1.590^a	1.373^a	0.781
	(0.312)	(0.555)	(0.528)	(0.289)	(0.893)
C	–1.199	21.852	–8.052	7.338	–8.102
C	(2.691)	(4.410)	(3.193)	(4.083)	(4.643)
Adjusted R²	0.169	0.228	0.304	0.308	0.135
F-statistic	14.467	12.090	17.023	17.357	6.889

Table 5.

Panel Regression Results—Part B (Interaction Effect of Temperature and Rainfall with Income and Agriculture—Panel Least Square).

Dependent Variable is Economic Growth	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)
Interaction effect of income group with temperature
TEMP	–0.096^a
TEMP	(0.032)
TEMP * D₁	0.010
TEMP * D₁	(0.037)
TEMP * D₂	–0.004
TEMP * D₂	(0.029)
Interaction effect of income group with rain
RAIN		–0.008
RAIN		(0.004)
RAIN * D₁		0.010
RAIN * D₁		(0.004)
RAIN * D₂		0.007
RAIN * D₂		(0.004)
Interaction effect of income group with one period lag of temperature
TEMP (–1)			–0.546^a
TEMP (–1)			(0.174)
TEMP (–1) * D₁			–0.208^b
TEMP (–1) * D₁			(0.121)
TEMP (–1) * D₂			–0.160
TEMP (–1) * D₂			(0.132)
Interaction effect of income group with one period lag of rain
RAIN (–1)				–0.006
RAIN (–1)				(0.004)
RAIN (–1) * D₁				0.008
RAIN (–1) * D₁				(0.005)
RAIN (–1) * D₂				0.005
RAIN (–1) * D₂				(0.004)
Interaction effect of agriculture with temperature
TEMP					–0.442
TEMP					(0.069)
TEMP * D₃					–0.010
TEMP * D₃					(0.036)
Interaction effect of agriculture with one period lag of temperature
TEMP (–1)						–0.478
						(0.084)
Dependent Variable is Economic Growth	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)
TEMP (–1) * D₃						–0.006
						(0.038)
Interaction effect of agriculture with rain
RAIN							0.010
RAIN							(0.046)
RAIN * D₃							0.012
RAIN * D₃							(0.067)
Interaction effect of agriculture with one period lag of rain
RAIN (–1)								0.012
								(0.045)
RAIN (–1) * D₃								0.015
								(0.066)
L_CAPITAL	3.801^a	4.697^a	3.805^a	4.849^a	3.759^a	3.778^a	4.651^a	4.810^a
	(0.624)	(0.598)	(0.625)	(0.599)	(0.625)	(0.627)	(0.598)	(0.599)
L_EDUCATION	–1.891	–1.917^a	–1.761^a	–1.838^a	–1.833^a	–1.709^a	–1.909^a	–1.827^a
	(0.420)	(0.441)	(0.422)	(0.445)	(0.422)	(0.423)	(0.439)	(0.443)
L_ENERGY	–0.089	–0.315	–0.060	–0.317	–0.199	–0.202	–0.404	–0.389
L_ENERGY	(0.402)	(0.263)	(0.406)	(0.263)	(0.283)	(0.284)	(0.271)	(0.272)
L_EXPORT	–0.046	–0.081	–0.114	–0.172	–0.084	–0.155	–0.096	–0.187
L_EXPORT	(0.114)	(0.121)	(0.114)	(0.123)	(0.111)	(0.111)	(0.121)	(0.122)
INFL	–0.001	–0.001	–0.031	–0.021^a	–0.001	–0.030^a	–0.001	–0.022^a
INFL	(0.001)	(0.001)	(0.011)	(0.011)	(0.001)	(0.011)	(0.001)	(0.011)
L_POP	0.299^a	0.240^a	0.284^b	0.178	0.314^a	0.297^a	0.153	0.113
L_POP	(0.116)	(0.113)	(0.117)	(0.114)	(0.106)	(0.107)	(0.100)	(0.101)
L_TRADE	1.546^a	1.233^b	1.543^a	1.095	1.452^a	1.447^a	1.202^a	1.062^b
	(0.296)	(0.312)	(0.298)	(0.314)	(0.282)	(0.283)	(0.307)	(0.308)
C	–3.589	–4.294	–2.302	–2.031	–1.743	–0.176	–2.641	–0.615
C	(3.073)	(3.037)	(3.134)	(3.132)	(2.674)	(2.728)	(2.817)	(2.899)
Adjusted R²	0.186	0.166	0.202	0.174	0.1701	0.186	0.148	0.158
F-statistic	13.455	11.691	14.378	11.979	14.646	15.639	12.558	13.067

The results closely resemble the baseline findings for temperature and rainfall variables. Secondly, the study also employs the fixed effect panel regression model but without the control variables, as shown in Tables 6 and 7. Notably, both the climate change variables (temperature and rainfall, Table 6) and their interaction with the agricultural economies (Table 7) found similar results. These robustness checks strengthen our main analysis and validate the key findings.

Table 6.

Panel Regression Results—Part A (Without Control Variables).

Dependent Variable is Economic Growth	(1)	(2)	(3)	(4)	(5)
Effect of temperature and rainfall
TEMP	–0.518^a
TEMP	(0.147)
RAIN	–0.005
RAIN	(0.009)
Effect on high-temperature countries
TEMP		–0.362^a
TEMP		(0.247)
Effect on low-temperature countries
TEMP			–0.588^a
TEMP			(0.193)
Effect on heavy-rainfall countries
RAIN				0.006
RAIN				(0.004)
Effect on light-rainfall countries
RAIN					0.029
RAIN					(0.054)
C	17.213^b	–0.005	–0.005^a	–0.005	–0.005
C	(3.577)	(0.009)	(0.009)	(0.009)	(0.009)
Adjusted R²	0.449	0.483	0.440	0.553	0.408
F-statistic	10.554	8.033	6.779	10.101	6.196
Hausman test	19.835^a	25.976^a	25.593^a	22.592^a	23.467^a

Table 7.

Panel Regression Results—Part B (Without Control Variables).

Dependent Variable is Economic Growth	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)
Interaction effect of income group with temperature
TEMP	–0.491^a
TEMP	(0.155)
TEMP * D₁	–0.025
TEMP * D₁	(0.133)
TEMP * D₂	–0.124
TEMP * D₂	(0.103)
Interaction effect of income group with rain
RAIN		–0.016
RAIN		(0.018)
RAIN * D₁		0.028
RAIN * D₁		(0.022)
RAIN * D₂		0.000
RAIN * D₂		(0.024)
Interaction effect of income group with one period lag of temperature
TEMP (–1)			–0.627^a
TEMP (–1)			(0.196)
TEMP (–1) * D₁			–0.183
TEMP (–1) * D₁			(0.140)
TEMP (–1) * D₂			–0.201
TEMP (–1) * D₂			(0.113)
Interaction effect of income group with one period lag of rain
RAIN (–1)				0.019
RAIN (–1)				(0.017)
RAIN (–1) * D₁				0.002
RAIN (–1) * D₁				(0.021)
RAIN (–1) * D₂				0.021
RAIN (–1) * D₂				(0.023)
Interaction effect of agriculture with temperature
TEMP					–0.473
TEMP					(0.349)
TEMP * D₃					–0.085
TEMP * D₃					(0.196)
Interaction effect of agriculture with one period lag of temperature
TEMP (–1)						–0.152
						(0.570)
Dependent Variable is Economic Growth	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)
TEMP (–1) * D₃						0.006
						(0.286)
Interaction effect of agriculture with rain
RAIN							–0.013
RAIN							(0.014)
RAIN * D₃							0.031
RAIN * D₃							(0.018)
Interaction effect of agriculture with one period lag of rain
RAIN (–1)								0.022
								(0.024)
RAIN (–1) * D₃								0.026
								(0.016)
C	16.957	5.126^a	21.849^a	1.872^b	21.198^b	27.896^a	4.935	2.005^b
C	(3.946)	(0.963)	(4.976)	(0.914)	(4.949)	(5.377)	(0.952)	(0.904)
Adjusted R²	0.497	0.438	0.491	0.483	0.451	0.492	0.438	0.483
F-statistic	10.377	9.972	11.896	11.588	10.606	12.217	10.142	11.801
Hausman Test	40.708^a	49.041^a	41.568^a	49.754^a	45.961^a	40.891^a	45.066^a	50.390^a

Conclusion

The study investigates the impact of climate change (temperature and rainfall) on the economic growth of Asian countries from 1991 to 2021 using the panel data regression analysis. The findings indicate a significant negative effect of rising temperatures on economic growth, while rainfall does not show a statistically significant effect. The countries were classified as high- and low-temperature and heavy- and light-rainfall countries to explore the impacts further. Both the high- and low-temperature countries are found to be vulnerable, experiencing a predicted 0.440 and 0.223% lower economic growth for every 1°C increase in temperature, respectively. However, the classification based on rainfall intensity did not show a statistically significant relationship with economic growth in Asian countries. The study contributes to the existing body of knowledge by estimating the impact of climate change, precisely temperature and rainfall, on economic growth in Asian economies. This contrasts with previous studies that mainly focused on individual transmission channels and predominantly examined Western contexts.

The study also introduces interaction terms to capture the potential impact of income groups and agricultural aspects on the relationship between climate and economic growth. The results reveal that the negative impact of temperature is more pronounced in agrarian economies than non-agrarian economies. However, there is no significant relationship between rainfall trends and economic growth in Asian countries, even considering the income group and agricultural aspects. It confirms that the level of development and nature of the economy matters for resilience toward climate change adversities.

Given the critical role of Asian economies in driving global growth, the empirical findings of the study offer significant policy implications. First, governments and policymakers across Asian nations must recognize the adverse impact of temperature on economic growth. Thus, there is an immediate need for policy amendments prioritizing climate mitigation and resilience mechanisms. Furthermore, the findings suggest that agrarian economies are particularly vulnerable to negative impacts from temperature changes compared to non-agrarian economies. Inadequate climate change management may exacerbate water scarcity, diminish food production, and cause poverty. Given Asia’s substantial population, these challenges pose a significant threat in the future. Therefore, policymakers must integrate climate considerations into economic growth estimation frameworks, particularly in low-income and agriculture-based economies. To mitigate these challenges, agriculture-based economies should prioritize investments in climate-resilient agricultural practices, water conservation, crop insurance, and technology adaptation. Second, a decisive response to reducing greenhouse gas emissions is needed across all ASEAN countries. International collaborations and adherence to global climate agreements, such as the COP26 agreement, are essential in mitigating the adverse effects of climate change. Adhering to COP26 would significantly reduce economic impacts.

Third, environmental policies vary among countries, with developed countries typically enforcing stringent regulations compared to others, including Asian countries. Thus, there is a need for government institutions in Asia to enact new reforms in environmental policy to limit polluting activities, reduce carbon emissions, and adopt sustainable practices, such as green supply chains and technologies. Finally, a proactive approach is needed to integrate climate change adaptation into national development plans, with budget allocations ensuring adequate financial support for adaptation measures.

While the study has significant merits and important findings, it also has limitations. The study could not incorporate certain potential and essential control variables into the analysis due to the non-availability of data while also considering the relevance of model specification. Future research should address this limitation by including additional factors such as carbon emissions, rising sea levels, droughts, flood damage, and labor productivity to provide a more accurate understanding of the causal mechanisms linking climate and economic growth in Asia. Additionally, future researchers may undertake similar analyses considering the availability of long-term data for climate change variables to compare the results with the present study for deeper and updated insights.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Notes

ORCID iDs

Nikhil Kaushik

Ashish Sharma

Sunil Kumar

Supplemental material

References

Abidoye

B. O.

, & Odusola

A. F.

(2015). Climate change and economic growth in Africa: An econometric analysis. Journal of African Economies , 24(2), 277–301.

Acevedo Mejia

, Mrkaic

, Novta

, Pugacheva

, & Topalova

P. B.

(2018). The effects of weather shocks on economic activity: What are the channels of impact? Journal of Macroeconomics , 65, 103207.

Agovino

, Casaccia

, Ciommi

, Ferrara

, & Marchesano

(2019). Agriculture, climate change and sustainability: The case of EU-28. Ecological Indicators , 105, 525–543.

Ahmed

A. U.

, & Iqbal

(2005). Agricultural drought in Bangladesh. In Boken

V. K.

, Cracknell

A. P.

, & Heathcote

R. L.

(Eds.), Monitoring and predicting agricultural drought: A global study (pp. 312–322). Oxford University Press.

Akram

(2013). Is climate change hindering economic growth of Asian economies? Asia-Pacific Development Journal , 19(2), 1–18.

Ali

S. N.

(2012). Climate change and economic growth in a rain-fed economy: How much does rainfall variability cost Ethiopia? (EEA Working Paper Series). http://ssrn.com/abstract=2018233. https://dx-doi-org.web.bisu.edu.cn/10.2139/ssrn.2018233.

Ali

, Ying

, Abdullah, Nazir

, Ishaq

, Shah

, Ilyas

, Ud Din

, & Tariq

(2019). The effect of climate change on economic growth: Evidence from Pakistan. Pacific International Journal , 2(2), 70–76.

Auffhammer

(2018). Quantifying economic damages from climate change. Journal of Economic Perspectives , 32(4), 33–52.

Barrios

, Bertinelli

, & Strobl

(2010). Trends in rainfall and economic growth in Africa: A neglected cause of the African growth tragedy. The Review of Economics and Statistics , 92(2), 287–298.

10.

Berlemann

, & Wenzel

(2018). Precipitation and economic growth (CESifo Working Paper No. 7258). Center for Economic Studies and ifo Institute (CESifo).

11.

Burke

, Hsiang

S. M.

, & Miguel

(2015). Global non-linear effect of temperature on economic production. Nature , 527(7577), 235–239.

12.

Casey

, Fried

, & Goode

(2023). How long do rising temperatures affect economic growth? FRBSF Economic Letter , 2023(15), 1–6.

13.

Chang

J. J.

, Mi

, & Wei

Y. M.

(2023). Temperature and GDP: A review of climate econometrics analysis. Structural Change and Economic Dynamics , 66, 383–392.

14.

Chen

, & Yang

(2019). Temperature and industrial output: Firm-level evidence from China. Journal of Environmental Economics and Management , 95(May), 257–274.

15.

Colacito

, Hoffmann

, & Phan

(2021). The impact of rising temperature on U.S. economic growth. In J. W. Dash (Ed.), World scientific encyclopedia of climate change: Case studies of climate risk, action, and opportunity (Vol. 3, pp. 33–138).

16.

Damania

, Desbureaux

, & Zaveri

(2020). Does rainfall matter for economic growth? Evidence from global sub-national data (1990–2014). Journal of Environmental Economics and Management , 102, 102335.

17.

Dell

, Jones

B. F.

, & Olken

B. A.

(2009). Temperature and income: Reconciling new cross-sectional and panel estimates. American Economic Review , 99(2), 198–204.

18.

Dell

, Jones

B.F.

, & Olken

B.A.

(2012). Temperature shocks and economic growth: Evidence from the last half century. American Economic Journal: Macroeconomics , 4(3), 66–95.

19.

Endalew

H. A.

, & Sen

(2021). Effects of climate shocks on Ethiopian rural households: An integrated livelihood vulnerability approach. Journal of Environmental Planning and Management , 64(3), 399–431.

20.

Fankhauser

, & Tol

R. S.

(2005). On climate change and economic growth. Resource and Energy Economics , 27(1), 1–17.

21.

Felbermayr

, & Gröschl

(2014). Naturally negative: The growth effects of natural disasters. Journal of Development Economics , 111, 92–106.

22.

Fishman

, Carrillo

, & Russ

(2019). Long-term impacts of exposure to high temperatures on human capital and economic productivity. Journal of Environmental Economics and Management , 93, 221–238.

23.

Global Climate Risk Index. (2021). Who suffers most from extreme weather events? Weather-related loss events in 2019 and 2000 to 2019 . German Watch e.V. Berlin. https://www.germanwatch.org/sites/default/files/Global%20Climate%20Risk%20Index%202021_2.pdf

24.

Gornall

, Betts

, Burke

, Clark

, Camp

, Willett

, & Wiltshire

(2010). Implications of climate change for agricultural productivity in the early twenty-first century. Philosophical Transactions of the Royal Society of Biological Sciences , 365(1554), 2973–2989.

25.

Gunathilaka

R. P.

, Smart

J. C.

, & Fleming

C. M.

(2017). The impact of changing climate on perennial crops: The case of tea production in Sri Lanka. Climatic Change , 140(3), 577–592.

26.

Hsiang

, Kopp

, Jina

, Rising

, Delgado

, Mohan

, Rasmussen

D. J.

, Muir-Wood

, Wilson

, Oppenheimer

, Larsen

, & Houser

(2017). Estimating economic damage from climate change in the United States. Science , 356(6345), 1362–1369.

27.

IPCC. (2007a). Climate change 2007: Impacts, adaptation and vulnerability. In Parry

M. L.

, Canziani

O. F.

, Palutikof

J. P.

, van der Linden

P. J.

, & Hanson

C. E.

(Eds.), Contribution of Working Group II to the fourth assessment report of the Intergovernmental Panel on Climate Change (p. 976). Cambridge University Press.

28.

IPCC. (2007b). Climate change 2007: Synthesis report. In Core Writing Team

R. K. Pachauri

, & Reisinger

(Eds.), Contribution of Working Groups I, II and III to the fourth assessment report of the Intergovernmental Panel on Climate Change (p. 104). IPCC.

29.

IPCC. (2014). Climate change 2014: Synthesis report. In Core Writing Team

R. K. Pachauri

, & Meyer

L. A.

(Eds.), Contribution of Working Groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change (p. 151). IPCC.

30.

IPCC. (2018). IPCC Chapter 3: Impacts of 1.5ºC global warming on natural and human systems In. Global warming of 1.5°C. Special report of the Intergovernmental Panel on Climate Change . IPCC Secretariat. https://helda.helsinki.fi/server/api/core/bitstreams/fbbe87a5-5098-4554-a8fb-b5ecb1a001c6/content

31.

IPCC. (2019). Climate change and land: An IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems . https://www.ipcc.ch/site/assets/uploads/2019/08/3.-Summary-of-Headline-Statements.pdf

32.

IUCN. (2017). International Union for Conservation of Nature. Annual Report 2017 . https://portals.iucn.org/library/sites/library/files/documents/2018-007-En.pdf

33.

Jones

B. F.

, & Olken

B. A.

(2010). Climate shocks and exports (NBER Working Paper No. 15711). National Bureau of Economic Research. https://www.nber.org/system/files/working_papers/w15711/w15711.pdf

34.

Kalkuhl

, & Wenz

(2020). The impact of climate conditions on economic production: Evidence from a global panel of regions. Journal of Environmental Economics and Management , 103, 102360.

35.

Karbownik

, & Wray

(2019). Long-run consequences of exposure to natural disasters. Journal of Labor Economics , 37(3), 949–1007.

36.

Khan

M. A.

, Tahir

, Khurshid

, Husnain

M. I. U.

, Ahmed

, & Boughanmi

(2020). Economic effects of climate change-induced loss of agricultural production by 2050: A case study of Pakistan. Sustainability , 12(3), 1216.

37.

Klusak

, Agarwala

, Burke

, Kraemer

, & Mohaddes

(2021). Rising temperatures, falling ratings: The effect of climate change on sovereign creditworthiness. Management Science , 69(12), 7468–7491.

38.

Kotz

, Wenz

, Stechemesser

, Kalkuhl

, & Levermann

(2021). Day-to-day temperature variability reduces economic growth. Nature Climate Change , 11(4), 319–325.

39.

Lanzafame

(2014). Temperature, rainfall and economic growth in Africa. Empirical Economics , 46(1), 1–18.

40.

Levine

D. I.

, & Yang

(2014). The impact of rainfall on rice output in Indonesia (No. w20302). National Bureau of Economic Research. http://www.nber.org/papers/w20302

41.

Loayza

N. V.

, Olaberría

, Rigolini

, & Christiaensen

(2012). Natural disasters and growth: Going beyond the averages. World Development , 40(7), 1317–1336.

42.

Mackay

(2008). Climate change. 2007: Impacts, adaptation and vulnerability. Contribution of Working Group II to the fourth assessment report of the Intergovernmental Panel on Climate Change. Journal of Environmental Quality , 37(6), 2407.

43.

Mahato

(2014). Climate change and its impact on agriculture. International Journal of Scientific and Research Publications , 4(4), 1–6.

44.

Marambe

, Punyawardena

, Silva

, Premalal

, Rathnabharathie

, Kekulandala

, Nidumolu

, & Howden

(2015). Climate, climate risk, and food security in Sri Lanka: The need for strengthening adaptation strategies. In Handbook of climate change adaptation (pp. 1759–1789). Springer-Verlag.

45.

Mendelsohn

(2014). The impact of climate change on agriculture in Asia. Journal of Integrative Agriculture , 13(4), 660–665.

46.

Mendelsohn

, Dinar

, & Sanghi

(2001). The effect of development on the climate sensitivity of agriculture. Environment and Development Economics , 6(1), 85–101.

47.

Mishra

, Bhatia

, & Tiwari

A. D.

(2020). Bias-corrected climate projections for South Asia from coupled model intercomparison project-6. Scientific Data , 7(1), 1–13.

48.

Moore

F. C.

, & Diaz

D. B.

(2015). Temperature impacts on economic growth warrant stringent mitigation policy. Nature Climate Change , 5(2), 127–131.

49.

Newell

R. G.

, Prest

B. C.

, & Sexton

S. E.

(2021). The GDP-temperature relationship: Implications for climate change damages. Journal of Environmental Economics and Management , 108, 102445.

50.

Olper

, Maugeri

, Manara

, & Raimondi

(2021). Weather, climate and economic outcomes: Evidence from Italy. Ecological Economics , 189, 107156.

51.

Panwar

, & Sen

(2019). Economic impact of natural disasters: An empirical re-examination. Margin: The Journal of Applied Economic Research , 13(1), 109–139.

52.

Pelling

, Ozerdem

, & Barakat

(2002). The macro-economic impact of disasters. Progress in Development Studies , 2(4), 283–305.

53.

Prabhakar

S. V. R. K.

, & Shaw

(2008). Climate change adaptation implications for drought risk mitigation: A perspective for India. Climatic Change , 88(2), 113–130.

54.

Priovashini

, & Mallick

(2021). A bibliometric review on the drivers of environmental migration. Ambio: A Journal of Human Environment , 15(1), 241–252.

55.

Raddatz

(2009). The wrath of God: Macroeconomic consequences of natural disasters (World Bank Policy Research Working Paper No. 5039). World Bank.

56.

Rahim

, & Puay

T. G.

(2017). The impact of climate on economic growth in Malaysia. Journal of Advanced Research in Business and Management Studies , 6(2), 108–119.

57.

Rasul

, Chaudhry

Q. Z.

, Mahmood

, & Hyder

K. W.

(2011). Effect of temperature rise on crop growth and productivity. Pakistan Journal of Meteorology , 8(15), 53–62.

58.

Rising

J. A.

, Taylor

, Ives

M. C.

, & Ward

R. E.

(2022). Challenges and innovations in the economic evaluation of the risks of climate change. Ecological Economics , 197, 107437.

59.

Sangkhaphan

, & Shu

(2019). The effect of rainfall on economic growth in Thailand: A blessing for poor provinces. Economies , 8(1), 1.

60.

Schlenker

, & Roberts

M. J.

(2009). Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. Proceedings of the National Academy of Sciences , 106(37), 15594–15598.

61.

Sharma

(2023). Rainfall deficiency, drought and economic growth in the Bundelkhand region of India. Sustainable Water Resources Management , 9, 72. https://doi.org/10.1007/s40899-023-00851-0

62.

Sharma

, & Sen

(2021). Impact of drought on economy: A district level analysis of Madhya Pradesh, India. Journal of Environmental Planning and Management , 64(6), 1021–1043.

63.

Somanathan

, Somanathan

, Sudarshan

, & Tewari

(2021). The impact of temperature on productivity and labor supply: Evidence from Indian manufacturing. Journal of Political Economy , 129(6), 1797–1827.

64.

Stern

(2006). Stern review: The economics of climate change . Cambridge University Press.

65.

Tol

R. S.

(2009). The economic effects of climate change. Journal of Economic Perspectives , 23(2), 29–51.

66.

Wassmann

, Jagadish

S. V. K.

, Sumfleth

, Pathak

, Howell

, Ismail

, Serraj

, Redona

, Singh

R. K.

, & Heuer

(2009). Regional vulnerability of climate change impacts on Asian rice production and scope for adaptation. Advances in Agronomy , 102, 91–133.

67.

Wheeler

, & Von Braun

(2013). Climate change impacts on global food security. Science , 341(6145), 508–513.

68.

Woetzel

, Tonby

, Krishnan

, Yamada

, Sengupta

, Pinner

, Fakhrutdinov

, & Watanabe

(2020). Climate risk and response in Asia . McKinsey Global Institute. https://www.mckinsey.com/business-functions/sustainability/our-insights/climate-risk-and-response-in-asia#:~:text=In%20many%20ways%2C%20Asia%20stands,probability%20of%20lethal%20heat%20waves

69.

Zhang

, Deschenes

, Meng

, & Zhang

(2018). Temperature effects on productivity and factor reallocation: Evidence from a half million Chinese manufacturing plants. Journal of Environmental Economics and Management , 88, 1–17.

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