Unveiling the interplay between climate variability and economic growth in India: an auto regressive distributed lag (ARDL) framework

Abstract

This study offers a comprehensive analysis of the impact of extreme weather events, intensified by climate change, on India’s real Gross National Product. Focusing on hydro-geological factors crucial for India’s Gross National Product, the Auto Regressive Distributed Lag model reveals significant insights. Notably, the study reveals a significant positive correlation between the lagged real GNP (GNP(−1)) and current real GNP, emphasizing the persistence of economic growth trends. Gross Fixed Capital Formation emerges as a key determinant, with a 0.50% increase in economic growth corresponding to a 1% rise in GFCF. Forest land and rainfall exhibited substantial positive associations, contributing to a noteworthy 1.674 and 0.099% with economic growth. Conversely, CO₂ emissions, rising temperatures (both Maximum Temperature and Minimum Temperature) exerted a significant negative influence on real GNP, emphasizing the need for sustainable emission reduction strategies. ARDL Bounds Test, revealed existence of a long-run relationship between real GNP and the selected variables. An ECM-Long Run Test underscores the lasting impact, with a 32.4% adjustment towards long-run equilibrium. The study establishes a bi-directional causality between real Gross National Product and carbon dioxide emissions, emphasizing the interconnectedness of economic growth and emissions. This underscores the urgency for addressing climate change alongside sustainable economic development. The findings serve as a crucial guide for evidence-based policymaking, urging proactive strategies to navigate climate challenges and ensure a prosperous, sustainable future for India. The findings contribute to the existing literature, emphasizing the multifaceted nature of factors shaping India’s sustained economic development.

Keywords:

• Climate change
• carbon dioxide emissions
• gross fixed capital formation
• gross national product
• auto regressive distributed lag

JEL classification:

• O1
• Q5

REVIEWING EDITOR:

• Geng Yuqing, Shanghai Dianji University, Shanghai, China

SUBJECTS:

• Development Policy
• Rural Development
• Economics and Development

1. Introduction

The escalating incidence and repercussions of extreme weather events in India, intensified by the looming climate change crisis, pose an ominous threat to the nation’s socio-economic fabric. These events forecast a disconcerting surge of 3.5% in poverty levels by 2040 (Mishra et al., 2020), potentially unravelling the strides made in augmenting incomes and living standards over the past three decades. India, a forefront contender in experiencing substantial economic losses and casualties from 1998 to 2017 (Lena et al., 2019), confronts a precarious scenario where the compounding effects of climate change, akin to the recent Covid-19 pandemic’s global impact, imperil both overall economic growth and the well-being of its grassroots populace. Extensive studies (Stern et al., 2006; Nordhaus, 1991; Tol, 2008; Yohe & Schlesinger, 2002) underline the intricate relationship between economic growth, Gross National Product (GNP), and climate variability. Developing countries like India, dependent on climate-sensitive sectors, face a heightened vulnerability to climate change. The sluggishness in developing and implementing climate-resilient strategies compounds the predicament. As India pursues industrialization for economic growth, it inadvertently contributes to greenhouse gas (GHG) emissions, exacerbating the climate change conundrum. Consequently, the imperative for India lies in effectively addressing these challenges to avert severe implications on economic growth and the welfare of its population, particularly at the grassroots level.

Anthropogenic activities, notably the combustion of fossil fuels, catapult the atmosphere into a heightened state of emissions, releasing carbon dioxide (CO₂) and methane (CH₄) and instigating transformative alterations in Earth’s climate system. The unequivocal warming of the climate system, as accentuated by the Intergovernmental Panel on Climate Change (IPCC), serves as a harbinger of a precarious future (Solomon et al., 2007). These adverse effects compound the existing challenges posed by rapid industrialization, urbanization, and economic development, thereby augmenting livelihood challenges in the country (Balasubramanian & Dhulasi, 2012). Consequently, India is compelled to prioritize climate change mitigation due to its direct impact on economic growth, particularly the GNP. While the comprehension of climatic factors and their repercussions is an evolving endeavour, this study directs its focus toward hydro-geological factors wielding substantial influence on India’s GNP. The nation’s geographical expanse and climatic diversity render it exceedingly susceptible to the adverse effects of climate change. Droughts, floods, cyclones, and storms disrupt key sectors like agriculture, manufacturing, and services, precipitating economic losses. The hydrological intricacies, encompassing rainfall patterns, water availability, and temperature fluctuations, reverberate through these sectors, constituting the backbone of India’s economy and influencing agricultural output, water availability, and industrial productivity.

The ramifications of climate change on India’s GNP extend across multiple dimensions. The agricultural sector, a significant employer and economic contributor, emerges as highly susceptible to climate variability. The erratic dance of rainfall patterns, prolonged droughts, or excessive rainfall contributes to crop failures, reduced yields, and augmented agricultural input costs, thereby impinging on farm incomes and overall economic growth. The ripples extend to other sectors, with rising temperatures and shifting precipitation patterns disrupting industrial production, particularly energy-intensive industries like manufacturing and mining. These sectors, reliant on stable energy supply, water availability, and favorable climatic conditions, grapple with reduced output, heightened operational costs, and diminished competitiveness. This cascading effect translates into job losses, income disparities, and elevated poverty levels, further exacerbating social and economic inequalities. The repercussions not only stall human development but also undermine the nation’s overall economic growth and stability.

This study’s significance resonates in the profound influence hydro-geological factors wield over India’s intricate economic tapestry. It emerges as an imperative exploration, particularly within the nexus of climate change and economic dynamics. Positioned as a guiding beacon at this intersection, the study delves into unravelling the specific factors that propel economic fluctuations, offering a nuanced understanding of their complexities. Beyond mere elucidation, this research aspires to be a harbinger of actionable insights, seeking potential strategies capable of mitigating the adverse impacts of climate change intricately etched into India’s economic fabric. In the crucible of a temporal context marked by surging awareness and mounting scientific evidence, the study assumes a pivotal role. The resounding call for climate-resilient and sustainable development pathways in India becomes a driving force, echoing through the corridors of policymaking. In this climate of urgency, the research functions as a compass, meticulously guiding the examination of the intricate relationship between the dependent variable, real GNP, and a constellation of independent variables. These variables span the spectrum from CO₂ emissions, Forest Land (FL), Mean Annual Rainfall (RF), Mean Annual Maximum Temperature (MaxT), Mean Annual Minimum Temperature (MinT) to Gross Fixed Capital Formation (GFCF). Their exploration extends to the all-India level, amplifying the study’s ambition to contribute not just to research but to the forefront of cutting-edge exploration. As the threads of this study weave through the fabric of economic and climatic complexities, they contribute significantly to the advancement of scientific knowledge. It stands as a sentinel, transcending academic confines and poised at the vanguard of empirical understanding. Its expansion unfolds as a narrative of discovery, where each variable becomes a chapter, contributing to the saga of India’s resilience in the face of climate challenges. The unique contribution lies not just in unravelling these complexities but in offering a roadmap for sustainable development. This research, in essence, is a commitment to environmental stewardship, a testament to the imperative of addressing climate change impact, and a stride towards sustainability. It ventures beyond the realms of identifying problems to presenting solutions. By delving into the intricate dance between economic variables and climatic determinants, it offers a blueprint for informed decision-making. Its unique contribution lies in providing actionable insights that bridge the gap between environmental conservation and economic prosperity. In view of these, this study aims at comprehensive examination of the relationship between real GNP and diverse factors such as CO₂ emissions, FL, RF, MaxT, MinT and GFCF. It further seeks to contribute to evidence-based policymaking by assisting policymakers in crafting climate-resilient policies that foster sustainable development.

Noteworthy is the absence of prior research on this specific intersection in India, enhancing the study’s significance in dissecting the determinants of GNP while incorporating the complexities of climate change. This research assumes a crucial role in deciphering the intricate relationship between climate variability and economic outcomes. Policymakers armed with this understanding can make informed decisions, devise resilient strategies, and foster sustainable and inclusive growth. As a cornerstone in advancing scientific knowledge, enabling long-term projections, and mobilizing stakeholders towards climate-resilient development pathways, this research transcends the realm of academic exploration. It emerges as a clarion call to address climate change challenges with urgency, safeguarding economic growth and steering India towards a future characterized by prosperity, sustainability, and resilience.

2. Review of literature

In the realm of environmental economics and finance, recent research has yielded insightful findings across diverse geographical and thematic dimensions. Amar et al. (2023) undertook a comprehensive investigation spanning from 2015 to 2022, examining the influence of environmental, social, and governance (ESG) practices on the financial performance of Nifty 50 companies in India. Their analysis unearthed a consistent and adverse impact of the environmental and governance pillar scores on return on equity (ROE), emphasizing the imperative for companies to address and improve these aspects for enhanced financial profitability. Christian et al. (2023) delved into the short-term dynamic integration between oil price shocks and interest rates across the U.S.A, Euro area, and twelve Asian economies from August 1999 to January 2018. Notably, their findings revealed compelling evidence of time variation in integration levels, with nuanced distinctions during crises, shedding light on the intricate interplay between oil prices and global financial dynamics. Daniyal et al. (2023) contributed to the discourse on CO2 emissions and Gross Domestic Product (GDP) in Pakistan, unravelling substantial correlations between the surge in CO2 emissions, electricity production sources, and the amplification of GDP and population growth. The study advocates for robust policy discourse and nationwide initiatives to curtail CO2 emissions and promote sustainable environmental practices. Ghosh et al. (2023) addressed climate-induced uncertainties in Bangladesh’s agricultural sector, employing ARDL bounds testing to unveil long-term relationships between climate variables and agricultural outcomes. Their findings illuminated positive impacts on agricultural value-added from past occurrences of agricultural value-added, carbon emissions, and average rainfall, highlighting the complex interplay between environmental factors and agricultural productivity. Leila et al. (2023) provided a poignant assessment of energy poverty in Lebanon, revealing alarming prevalence and proposing targeted policy actions amidst ongoing crises. Additionally, Leila and Fakhri (2023) employed Structural Vector Autoregression to investigate the relationship between oil market shocks and financial instability in Asian countries, emphasizing the critical role of the oil price shock’s source in influencing financial markets. Hasan et al. (2022) explored the sustainability of Bangladesh’s economic progress, emphasizing the intricate relationship between renewable energy consumption, gross fixed capital formation, and environmental factors in shaping GDP growth. Adejumo (2021) focused on Nigeria, analyzing the influence of climate change on economic growth using time-series data and uncovering significant impacts of annual average rainfall, carbon emissions, foreign direct investment, and gross fixed capital formation on real GNP. Fateh et al. (2021) scrutinized the relationship between oil revenues and economic growth in the MENA region, challenging the notion of a resource curse and highlighting the role of political institutions. Layal et al. (2020a, b) introduced a Financial Stress Index for Lebanon, offering a proactive tool for macroprudential regulators to monitor and manage financial conditions. Their work underscores the practical implications of employing such an index in maintaining financial stability. In the same context, Leila et al. (2020) concluded that the sharp fall in oil prices between 2014 and 2016 had adverse effects on economic growth in the MENA region, particularly for oil-exporting nations. Dumrul and Kilicarslan (2017) investigated the economic impact of climate change on the agricultural sector in Turkey, emphasizing the sector’s sensitivity and recommending multifaceted strategies to combat climate change. Collectively, these studies contribute valuable insights to the ongoing discourse on environmental economics and finance, addressing issues ranging from ESG practices and financial performance to the complex interactions between climate variables and economic indicators, providing a comprehensive understanding of the challenges and opportunities in the field.

The above research findings (Figure 1) offer a multifaceted understanding of the complex interplay between economic activities, environmental factors, and policy implications across diverse regions. The study on Pakistan reveals a concerning correlation between CO₂ emissions, electricity production, and economic growth, urging comprehensive strategies for energy conservation. In Bangladesh, the intricate relationships between climate change indicators and agricultural outcomes underscore the need for adaptive policies to ensure the resilience of the vital agricultural sector. Bangladesh’s economic progress is intricately linked to addressing environmental challenges, emphasizing the role of renewable energy and sustainable practices. Nigeria’s analysis highlights the significant influence of climate-related factors on economic growth, with implications for forest conservation and population management. In Turkey, the agricultural sector emerges as the most sensitive to climate change, emphasizing the need for holistic strategies to combat its impact. The investigation into the financial performance of Nifty 50 companies in India underscores the differentiated impact of environmental, social, and governance practices on return on equity, necessitating tailored sustainability measures. The examination of oil market shocks in Asian countries emphasizes the critical role of the source of oil price fluctuations in influencing financial stability. The MENA region’s analysis challenges the prevailing notion of a resource curse, highlighting the resource blessing and emphasizing the role of political institutions. The short-term dynamic integration between oil price shocks and interest rates across economies reveals varying levels of co-movement, influenced by global financial crises. The introduction of a Financial Stress Index for Lebanon stands as a proactive measure for macroprudential regulators, offering a valuable tool to gauge and manage financial conditions. The comprehensive assessment of energy poverty in Lebanon underscores the urgent need for targeted measures to alleviate the prevalent issue in the face of ongoing crises. Lastly, the adverse effects of the oil price shock on the MENA region’s economic growth highlight the importance of resilient economic strategies in the context of volatile oil markets. Collectively, these studies contribute valuable insights to the broader discourse on sustainable development, economic resilience, and climate change adaptation, urging policymakers, businesses, and communities to adopt proactive measures for a more sustainable and resilient future.

Figure 1. Smart Art Chart for reviews regarding determinants for economic growth of an economy.

3. Methodology

3.1. Research design

ARDL model was employed to examine the relationship between the dependent variable, real GNP (base year 1970–1971), and independent variables including CO₂ emissions, FL, RF, MaxT, MinT and GFCF (Dumrul & Kilicarslan, 2017; Rahim & Puay, 2017) The study utilized annual time series data from 1971 to 2020. The secondary data for the aforementioned variables during the reference period are collected from www.fao.org and www.indiastat.com.

Examining the connection between above climate change variables, GFCF, CO₂ emissions with real GNP through ARDL model holds significant importance. It offers insights into the intricate interplay between environmental factors and economic performance. This research aids policymakers in crafting climate-resilient policies, fostering sustainable development, and formulating adaptation and mitigation strategies. It guides resource allocation, facilitates compliance with global climate commitments, and promotes economic stability by assessing climate-related risks.

This study holds significant importance for India due to its vulnerability to climate change and the need for sustainable economic development. By employing the ARDL model, India can gain crucial insights into the potential economic impacts of climate change. Given India’s large population, rapid development, and environmental challenges, this research helps policymakers make informed decisions regarding climate-resilient economic strategies and assess progress towards international climate commitments. Additionally, the ARDL model’s flexibility in handling variables with different integration orders enhances its suitability for analyzing India’s complex climate and economic data, making it a valuable tool for shaping the country’s sustainable future (Imran et al., 2023).

3.2. Model specification

The ARDL model, which combines the features of autoregressive and distributed lag models into a general dynamic regression model, was introduced by Pesaran et al. (2001). It offers several advantages compared to traditional cointegration methods. Firstly, it allows for variables to be integrated at different orders, accommodating variables that are integrated of order one, order zero, or even fractionally integrated, with the exception of 1(2). Secondly, this model is efficient for small and finite sample sizes, making it suitable for data analysis in such cases. Thirdly, the model provides unbiased estimates in long run, as demonstrated by Harris and Sollis (2003). Lastly, this model integrates short-run adjustments with long-run equilibrium by deriving the Error Correction Term (ECT) through a simple linear transformation, thereby capturing both short-term dynamics and long-run relationships (Ali et al., 2017). Expressing all variables of this study in logarithms, the following ARDL model was considered: (1) $$\begin{array}{c}{\text{DLOG realGNP}}_{\mathrm{t}}={\alpha }_{01}+\sum _{i=1}^p{b}_{1i}D\mathit{\text{LOGrealGN}}{P}_{t-i}+\sum _{j=1}^{q_1}{b}_{2j}\mathit{\text{DLOGCO}}{2}_{t-i}+\\ \sum _{j=1}^{q_2}{b}_{3j}{\mathit{\text{DLOGFL}}}_{t-i}+\sum _{j=1}^{q_3}{b}_{4j}{\mathit{\text{DLOGGFCF}}}_{t-i}+\sum _{j=1}^{q_4}{b}_{5j}{\mathit{\text{DLOGMAXT}}}_{t-i}+\\ \sum _{j=1}^{q_5}{b}_{6j}{\mathit{\text{DLOGMINT}}}_{t-i}+\sum _{j=1}^{q_6}{b}_{7j}{\mathit{\text{DLOGRF}}}_{t-i}+{\beta }_1{\text{LOGreal GNP}}_{\mathrm{t}-1}+\\ {\beta }_2\hspace{0.17em}\mathrm{\text{log}}\hspace{0.17em}{\text{CO}}_2{}_{t-1}+{\beta }_3{\mathit{\text{LOGFL}}}_{t-1}+{\beta }_4{\mathit{\text{LOGGFCF}}}_{t-1}+{\beta }_{5}L\mathit{\text{OGMax}}{T}_{t-1}+\\ {\beta }_6{\text{LOGMinT}}_{\mathrm{t}-1}+{\beta }_7{\text{LOGRF}}_{\mathrm{t}-1}+{\epsilon }_{0t}\end{array}$$(1)

In the above equation, ${\alpha }_{01}$ is the drift component, $b_{1i}$ to $b_{7j}$ represent short run dynamics of the model; the coefficients β₁ to β₇ indicate long run relationship, ‘D’ is the first difference and ${\epsilon }_{0t}$ indicates the error term. A bound test, is commonly used to determine whether a long-run or short-run relationship exists between variables. If the variables are non-cointegrated, only the short-run ARDL model is specified. However, if they are cointegrated, both the short-run ARDL model and Error Correction Model (ECM)-long-run test need to be specified. By employing linear transformation, ECT is derived from the ARDL bounds test. The negative and statistically significant ECT indicate speed of adjustment and how quickly the variables return to long-run equilibrium. The long-run test enables the establishment of the ECT as follows: (2) $$\begin{array}{c}{\text{DLOG realGNP}}_{\mathrm{t}}={\alpha }_{01}+\sum _{i=1}^p{b}_{1i}D\mathrm{\text{LOG realGN}}{\mathrm{P}}_{t-i}+\sum _{j=1}^{q_1}{b}_{2j}\mathit{\text{DLOGCO}}{2}_{t-i}+\\ \sum _{j=1}^{q_2}{b}_{3j}{\mathit{\text{DLOGFL}}}_{t-i}+\sum _{j=1}^{q_3}{b}_{4j}{\mathit{\text{DLOGGFCF}}}_{t-i}+\sum _{j=1}^{q_4}{b}_{5j}{\mathit{\text{DLOGMAXT}}}_{t-i}+\\ \sum _{j=1}^{q_5}{b}_{6j}{\mathit{\text{DLOGMINT}}}_{t-i}+\sum _{j=1}^{q_6}{b}_{7j}{\mathit{\text{DLOGRF}}}_{t-i}+\lambda {\text{ECT}}_{\mathrm{t}-1}+{\gamma }_{0t}\end{array}$$(2)

In Eq. (2)(2) $$\begin{array}{c}{\text{DLOG realGNP}}_{\mathrm{t}}={\alpha }_{01}+\sum _{i=1}^p{b}_{1i}D\mathrm{\text{LOG realGN}}{\mathrm{P}}_{t-i}+\sum _{j=1}^{q_1}{b}_{2j}\mathit{\text{DLOGCO}}{2}_{t-i}+\\ \sum _{j=1}^{q_2}{b}_{3j}{\mathit{\text{DLOGFL}}}_{t-i}+\sum _{j=1}^{q_3}{b}_{4j}{\mathit{\text{DLOGGFCF}}}_{t-i}+\sum _{j=1}^{q_4}{b}_{5j}{\mathit{\text{DLOGMAXT}}}_{t-i}+\\ \sum _{j=1}^{q_5}{b}_{6j}{\mathit{\text{DLOGMINT}}}_{t-i}+\sum _{j=1}^{q_6}{b}_{7j}{\mathit{\text{DLOGRF}}}_{t-i}+\lambda {\text{ECT}}_{\mathrm{t}-1}+{\gamma }_{0t}\end{array}$$(2) , ECT represents the long-run adjustment process and accordingly, ‘λ’ represents speed of adjustment parameter, $b_{1i}$ to $b_{7j}$ represent the short-run dynamics of the model. In the last step, after the determination of long-run relationship between variables, direction of causality using Granger causality is analysed (Figure 2).

Figure 2. Graphical abstract of ARDL methodology.

This research, utilizing the ARDL model to analyze the nexus between environmental variables and economic growth in India, holds significant societal benefits. It empowers policymakers to formulate climate-resilient policies, fostering sustainable development and aiding in the design of effective adaptation and mitigation strategies. The study guides resource allocation, directing investments toward sectors crucial for sustainable development and vulnerable to climate change. Moreover, by providing insights that facilitate compliance with international climate agreements, the research positions India to contribute to global climate commitments. Finally, the assessment of climate-related risks enhances economic stability, enabling stakeholders, including businesses and investors, to make informed decisions and reduce vulnerability to environmental shocks. In essence, this research not only advances knowledge but also offers practical tools for shaping resilient, sustainable, and globally aligned policies in the face of climate challenges.

4. Results and discussion

4.1. Descriptive statistics

The provided decade-wise data from 1971 to 2020 (Table 1) offers insights into the trends in climate variability based on different climate variables. When considering CO₂ emissions, there is noticeable fluctuation in mean values over the years. The highest mean was observed during the 1991–2000 period, followed by a decrease in subsequent years, as the Government is focusing on hydrogen-based production (environment-friendly alternative and to achieve net-zero emissions by 2070 (Kowtham et al., 2022). However, the Standard Deviation (SD) and Coefficient of Variation (CV) show varying degrees of variability around the mean, with the highest values recorded during 2001–2010 and 2011–2020 periods. The minimum and maximum values reflect range of CO₂ emissions, with the highest emissions occurring during the 2011–2020 period. These fluctuations in CO₂ emissions can be attributed to changes in industrial activities, energy consumption patterns, and environmental policies over time. Regarding FL, there is a relatively stable mean value throughout the years, indicating a consistent extent of forest cover. The SD and CV remain relatively low, suggesting minimal variability around the mean. The minimum and maximum values of FL indicate range within which the forested land area fluctuates, with the highest extent observed during 2011–2020 period. The minimal variability can be attributed to management practices and conservation efforts aimed at preserving forest ecosystems. However, minor fluctuations may arise due to factors such as deforestation, reforestation, and land-use changes. Both MaxT and MinT exhibit a gradual increasing trend over the years. Their mean values consistently rise from 1971 to 2020, with the highest mean recorded during 2011–2020. The SD and CV show relatively low variability around the mean. The minimum and maximum values of both MaxT and MinT represent range of maximum temperature fluctuations, with the highest recorded during 2011–2020 period. Regarding RF, it exhibits relatively stable mean values over the years, with only slight variations. The SD and CV reflect moderate variability around the mean, with higher values during the 2001–2010 and 2011–2020 periods. Minimum and maximum values indicate the range of rainfall amounts, with the highest recorded during 2011–2020 period. This variability can be influenced by natural climate patterns, such as El Niño and La Niña events, regional and local factors. Changes in atmospheric conditions, oceanic currents, and geographical features can affect precipitation patterns, leading to fluctuations in annual rainfall amounts. Understanding these trends and their drivers is crucial for planning effective strategies to address climate-related challenges and promote resilience.

Table 1. Climate variability in India.

Climate variables	Statistics	1971–1980	1981–1990	1991–2000	2001–2010	2011–2020	1971–2020
CO2 emissions (Kilotonnes)	Mean	26314	33368	40905	34254	21780	31324
	SD	1814	1929	3293	10015	14919	10375
	CV (%)	6.89	5.78	8.05	29.24	68.5	33.12
	Min	24188	29881	36512	21477	9970	9970
	Max	29626	35392	46964	45681	51092	51092
FL (‘000 ha)	Mean	62459	64105	65947	68639	71070	66444
	SD	608	86.3	1106	577	989	3208
	CV (%)	0.97	0.13	1.68	0.84	1.39	4.83
	Min	61695	63941	64303	67782	69762	61695
	Max	63576	64218	67591	69496	72876	72876
MaxT (°C)	Mean	29.317	29.336	29.563	30.909	31.05	30.035
	SD	0.215	0.219	0.323	0.209	0.241	0.82
	CV (%)	0.73	0.75	1.09	0.68	0.78	2.73
	Min	28.89	29.11	29.05	30.74	30.81	28.89
	Max	29.63	29.72	30.18	31.37	31.54	31.54
MinT (°C)	Mean	19.098	19.224	19.526	20.549	20.627	19.805
	SD	0.344	0.132	0.362	0.159	0.203	0.706
	CV (%)	1.8	0.68	1.86	0.77	0.98	3.57
	Min	18.62	18.96	19.15	20.37	20.28	18.62
	Max	19.55	19.42	20.39	20.90	20.88	20.9
RF (mm)	Mean	1106.1	1038.9	1090.4	1063.2	1075.1	1074.7
	SD	60.5	45	64.3	104.2	95.7	77.6
	CV (%)	5.47	4.33	5.89	9.81	8.91	7.22
	Min	1024	991	991.7	856.1	959	856.1
	Max	1184	1115	1195.5	1170.6	1229.1	1229.1

Source: Calculated by authors.

Raw Data Source: www.fao.org.

4.2. Unit root test

The stationarity test for selected variables using Augmented Dickey Fuller (ADF) test proposed by Dickey and Fuller (1979), as well as the Phillips-Perron (PP) test introduced by Phillips and Perron (1988) were performed at lag 4, determined by length criteria over the reference period (Table 2). The results indicated that all time-series variables showed stationarity in their first difference (except for rainfall which was already stationary at level I(0)). Furthermore, no variables were found to be integrated at the I(2) level. Therefore, ARDL model was employed, as the variables demonstrated a combination of I(0) and I(1) integrations, indicating stationarity.

Table 2. Results of ADF and PP unit root tests for selected variables (probability values).

Variables	ADF test statistics		PP test statistics		Order of Integration
	Level	First difference	Level	First difference
Real GNP	1.0000	0.0170	1.0000	0.0164	I (1)
CO2 emissions	0.4134	0.0030**	0.4451	0.0000**	I (1)
MaxT	0.0710	0.0000**	0.0991	0.0000**	I (1)
MinT	0.6132	0.0000**	0.3783	0.0000**	I (1)
FL	0.8987	0.0000**	0.4466	0.0005**	I (1)
RF	0.0004**	–	0.0004**	–	I (0)
GFCF	0.9956	0.0052**	0.5113	0.0162**	I (1)

Note. ** denote significance at 1% level.

Raw Data Source: www.fao.org.

4.3. Lag selection criteria

The first step of the empirical investigation involved selecting the appropriate lag for the model. To determine the lag, four criteria were employed: the Final Prediction Error (FPE), Akaike Information Criterion (AIC), Schwarz Criterion (SC), and Hannan-Quinn criterion (HQ). These criteria are presented in Table 3, where the different lag values were evaluated. The underlying principle is the lag producing minimum value for these criteria is considered as the best choice for the model. All four criteria pointed towards a lag of four as the optimal selection. Consequently, the AIC lag of four was chosen for the entire duration of the study. By selecting the optimal lag, the researchers ensured that the model captured the relevant dynamics and relationships among the variables while avoiding excessive complexity or the omission of important information. This approach enhances the robustness and reliability of the subsequent analysis in the study.

Table 3. Results of VAR lag order selection criteria.

Lag	LogL	LR	FPE	AIC	SC	HQ
0	480.8673	NA	2.66e − 18	−20.60292	−20.32465	−20.49868
1	768.3322	55.22888	1.06e − 22	−30.97096	−24.22902	−28.88725
2	815.2585	58.32497	1.09e − 22	−30.88080	−25.05860	−29.27587
3	871.1532	63.24849	1.23e − 22	−31.18057	−26.70673	−29.31717
4	945.8746	474.9420*	8.57e − 23*	−32.29890*	−28.74479*	−30.13703*

Note. *indicates lag order selected by the criterion; LR - sequential modified LR test statistic (at 5% Level).

Source: Calculated by authors.

4.4. ARDL model

Table 4 showcases an ARDL model unravelling the short-term correlation between carefully chosen explanatory variables and the dependent variable, real GNP. Notably, real GNP itself in its preceding period (GNP(−1)) exhibits a noteworthy positive impact on current real GNP in India. A 1% increase in GNP in the lagged period corresponds to a 0.976% increase in real GNP in the current year. This phenomenon can be attributed to the momentum or persistence in economic growth. When real GNP experiences growth in the previous period (GNP(−1)), it signifies ongoing positive economic trends, possibly driven by factors such as increased consumer confidence, business investments, or government policies. This positive momentum tends to spill over into the current year, influencing and contributing to a continued rise in real GNP. Additionally, sustained economic growth in the lagged period may lead to increased productivity, job creation, and overall economic stability, fostering a conducive environment for further expansion in the subsequent period. Moreover, the Wald test results in Table 5 affirm a significant positive association between the current Real GNP and its lagged period (GNP(−1)).

Table 4. ARDL model for determinants of real GNP.

Variable	Coefficient	Std. error	t-statistic
GNP(−1)	0.976**	0.106	9.241
GFCF	0.499**	0.076	6.554
GFCF(−1)	0.512**	0.105	4.892
GFCF(−2)	−0.112	0.088	−1.272
GFCF(−3)	0.156	0.161	0.967
FL	1.674**	0.647	2.589
CO2	−0.013**	0.002	−5.166
CO2(−1)	−0.040**	0.003	−13.925
CO2(−2)	−0.009**	0.003	−3.502
CO2(−3)	0.031	0.021	1.434
MaxT	−0.884**	0.213	−4.157
MaxT(−1)	−3.939**	0.998	−3.948
MinT	−0.997**	0.323	−3.083
MinT(−1)	−1.032	0.665	−1.551
RF	0.099**	0.022	4.400
RF(−1)	0.165*	0.074	2.232
RF(−2)	0.026**	0.006	4.022
RF(−3)	0.163	0.099	1.649
RF(−4)	0.146	0.099	1.481
C	3.615*	1.048	3.450
R-squared	1.000	Mean dependent var	13.175
Adjusted R-squared	0.999	S.D. dependent var	1.001
S.E. of regression	0.028	Akaike info criterion	−3.999
Sum squared resid	0.021	Schwarz criterion	−3.203
Log likelihood	111.966	Hannan-Quinn criter.	−3.701
F-statistic	2980.448	Durbin-Watson stat	2.006
Prob(F-statistic)	0.000

Note. p-values and any subsequent tests do not account for model.

Dependent Variable: GNP.

Method: ARDL.

Sample (adjusted): 1975 2020.

Included observations: 46 after adjustments.

Maximum dependent lags: 1 (Automatic selection).

Model selection method: Akaike info criterion (AIC).

Dynamic regressors (4 lags, automatic): GFCF FL CO₂ MaxT MinT RF.

Fixed regressors: C.

Number of models evaluated: 15625.

Selected Model: ARDL(1, 3, 0, 3, 1, 1, 4).

** & * indicates significance at 1 and 5 percent levels respectively.

Table 5. Short run relation between Real GNP and its determinants – Wald test (ARDL(4, 3, 4, 4, 1, 4, 4)).

S.No	Determinants	Test statistic	Value	df	Probability
1	real GNP(-1) = 0	F-statistic	85.39209	(1, 26)	0.0000
		Chi-square	85.39209	1	0.0000
2	GFCF = GFCF(-1) = GFCF(-2) = GFCF(-3) = 0	F-statistic	11.33376	(4, 26)	0.0000
		Chi-square	45.33503	4	0.0000
3	FL = 0	F-statistic	6.701196	(1, 26)	0.0156
		Chi-square	6.701196	1	0.0096
4	CO2 = CO2(-1) = CO2(-2) =  CO2(-3) = 0	F-statistic	13.4769	(4, 26)	0.0000
		Chi-square	48.96606	4	0.0000
5	MaxT = MaxT(-1) = 0	F-statistic	8.160005	(2, 26)	0.0018
		Chi-square	16.32001	2	0.0003
6	MinT = MinT(-1) = 0	F-statistic	12.41887	(2, 26)	0.0000
		Chi-square	26.11298	2	0.0000
7	RF = RF(-1) = RF(-2) =  RF(-3) = RF(-4) = 0	F-statistic	3.000337	(5, 26)	0.0287
		Chi-square	15.00168	5	0.0104

GFCF emerges as a pivotal determinant significantly shaping long-term economic growth, with a noteworthy 0.50% increase in economic growth corresponding to a 1% rise in GFCF (Table 4). This influence extends even to the first lag (GFCF(−1)), indicating a sustained positive impact on real GNP in India. The efficiency in utilizing fixed factors of production and the adoption of recent technological advancements across primary, secondary, and tertiary sectors contribute to this positive association. Notably, this finding aligns with prior research conducted by Bakare (2011), Ugochukwu and Chinyere (2013), Kanu and Ozurumba (2014), Mordecki and Ramírez (2014), Neanywa and Makhenyane (2016), and other scholars. These studies collectively underscore the crucial role of robust capital investment in enhancing overall economic performance. Strategic investment policies and the adoption of modern production techniques are emphasized, directing resources toward infrastructure development, technological innovation, and capacity building. This positive relationship between GFCF and economic growth highlights the significance of such strategic investment policies, contributing to increased productivity, improved competitiveness, and overall economic advancement. The Wald test in Table 5 further reinforces this insight, indicating a joint significant positive association between the level and three lags of GFCF with real GNP.

FL demonstrates a significant and positive association with real GNP in India, consistent with initial expectations. A 1% increase in FL corresponds to a noteworthy 1.674% increase in economic growth, as evident in Table 4, and this relationship is further affirmed by the Wald test in Table 5. Notably, the forestry and logging sector made a substantial contribution of approximately Rs. 1.96 trillion to the agricultural gross value in the fiscal year 2019. This sector has exhibited a consistent upward trend in its contribution since the fiscal year 2013 (Agricultural Statistics at a Glance, 2020). In the context of ecological security, India has undertaken measures to safeguard around 20% of its forest area. This strategic approach ensures the preservation of forest land as a valuable resource, thereby contributing significantly to the sustained long-term economic growth of the nation.

CO₂ emissions wield a significant negative influence on real GNP in India. A 1% reduction in CO₂ emissions corresponds to a nuanced 0.013% increase in real GNP. This impact extends to lags 1 and 2, where both CO₂(−1) and CO₂(−2) exhibit a noteworthy negative influence on real GNP. The comprehensive analysis in the Wald test (Table 5) underscores a joint and significant negative association between the current level and three lags of CO₂ with real GNP. Beyond the economic implications, the reduction of CO₂ emissions serves a dual purpose by not only mitigating the adverse impacts of air pollution but also alleviating the strain on healthcare systems. It is imperative to note that India holds the third position globally in terms of CO₂ emissions, trailing behind China and the USA, with a share of approximately 7.1% in 2016. Given this scenario, prioritizing measures for emission reduction becomes paramount to ensure sustained economic growth (Mishra et al., 2020). The detrimental effects of CO₂ emissions on the environment manifest in the escalating temperatures across the Tibetan Plateau, posing a threat to the flow rates of major rivers such as the Ganges, Brahmaputra, and Yamuna. From 1901 to 2018, temperatures in India have witnessed a rise of 0.7 °C (1.3 °F). Effectively addressing this challenge necessitates a decoupling between CO₂ emissions and economic growth. Implementation of a carbon tax, promotion of afforestation, and a heightened reliance on renewable energy sources (such as solar, wind, biomass, and geothermal power) emerge as pivotal steps in this direction. Moreover, embracing sustainable practices in agriculture, infrastructure, and transportation, coupled with efforts to control population growth, assumes critical importance. Recent developments, including economic slowdown, the surge in renewable energy, and the impact of the Covid-19 pandemic, have collectively led to a year-on-year reduction in India’s CO₂ emissions over the past four decades. By March 2020, emissions had diminished by approximately 1%, attributed to reduced coal consumption and stagnant oil consumption. These developments underscore the potential for achieving sustainable emission reduction goals while fostering economic growth.

In India, the escalating weather volatility attributed to climate change introduces a heightened level of unpredictability to the nation’s output and income. The surge in temperature volatility, a consequence of global warming, gives rise to adverse effects such as increased heat stress, sea level rise, cyclones, and droughts, consequently leading to a decline in agricultural output. This ripple effect contributes to a deceleration in the pace of real GNP growth within the country. The study’s findings specifically highlight the negative and statistically significant influences of MaxT in both its current level and its first lag on real GNP (−0.884 and −3.939%, respectively). The corroborating evidence from the Wald test (Table 5) emphasizes a joint and significant negative association between the level and the first lag of MaxT with real GNP. The adverse impacts of rising MaxT extend beyond mere economic implications, affecting sectors ranging from agriculture to industry and services. Moreover, earlier research studies (Jonathan et al., 2017; Matthias & Leonie, 2020; Jessie et al., 2021) underscore the far-reaching consequences of a global temperature increase of approximately 3.5 °C. This scenario could potentially result in a 7–14% reduction in global economic output by the year 2100. Notably, semi-arid tropical countries, including India, face a disproportionately severe impact from climate change. Recognizing these vulnerabilities, it is imperative for India to proactively address climate change and its associated temperature rise to safeguard its economic growth. Implementing strategic measures to curtail greenhouse gas emissions, embracing climate-resilient agricultural practices, and championing sustainable development initiatives emerge as essential steps to mitigate the adverse effects of temperature volatility on the Indian economy. By prioritizing these initiatives, India can work towards ensuring a more resilient and sustainable trajectory for its economic growth amidst the challenges posed by climate change.

Similarly, MinT emerges as another factor exerting a notable negative and statistically significant influence on India’s real GNP. The coefficient of −0.997 signifies that for every 1 °C increase in MinT, there is a corresponding decrease of 0.997% in the country’s real GNP. The corroborative evidence from the Wald test (Table 5) underscores a joint and significant negative association between the level and the first lag of MinT with real GNP. This adverse impact is attributable to the disruptions induced in the normal functioning of various economic sectors, particularly the agriculture sector, which is heavily reliant on favorable temperature conditions. The escalating minimum temperature associated with climate change poses formidable challenges to agricultural productivity, significantly impacting crop growth and yield. Elevated minimum temperatures contribute to heat stress, reduced water availability, and an increased prevalence of pests and diseases, all of which collectively contribute to a downturn in agricultural output. Study conducted by Roy and Haider (2018) further accentuate the adverse effects of climate change-induced temperature increases on the agrarian economy. The disruptions caused by higher minimum temperatures can lead to decreased productivity, income losses for farmers, and an increased vulnerability to food insecurity and poverty. Addressing these challenges necessitates the implementation of climate-resilient agricultural practices, the promotion of sustainable water management, and the adoption of innovative technologies. By undertaking these measures, India can effectively mitigate the negative repercussions of temperature rise on its economic landscape.

Similarly, RF manifests a significant positive influence on real GNP in both its level form and the preceding two lags, with coefficients of 0.099, 0.165, and 0.026%, respectively. The corroborating evidence from the Wald test further affirms a joint and significant positive association between the level and the four lags of RF with real GNP. This positive impact can be attributed to the beneficial effects of adequate rainfall on various economic sectors, particularly agriculture. Favourable rainfall conditions contribute to increased soil moisture, supporting crop growth and enhancing agricultural productivity. The positive correlation between RF and real GNP underscores the pivotal role of precipitation in sustaining agricultural output, which, in turn, contributes to overall economic growth. Ensuring sustainable water management practices and implementing measures to cope with changing precipitation patterns are imperative for harnessing the positive impacts of rainfall on economic development. By addressing these aspects, India can optimize the benefits of rainfall for its economy and enhance its resilience in the face of climate variability.

4.5. ARDL bound test

The ARDL Bound Test serves as a crucial tool for examining potential cointegration among the variables under consideration. As depicted in Table 6, this test encompasses both the F-bounds test and t-bounds test, aiming to scrutinize the existence of a long-run relationship among the variables. The calculated F-statistic surpasses the upper bounds I(1) critical values at the 1 and 5% significance levels, leading to the rejection of the null hypothesis stating "no cointegration among variables in the long run." This outcome indicates the presence of a sustained or co-integration relationship between real GNP and the selected independent variables across the entire reference period. This discovery holds significance as it implies that variations in real GNP are not mere short-term or transitory fluctuations but rather possess a more enduring association with climate variables and other factors considered in the analysis. These findings align with the conclusions drawn in earlier studies by Maki (2012) and Sefa and Fatih (2021). Consequently, the consistency with the existing body of literature enhances the credibility of the findings and underscores the robustness of the results.

Table 6. ARDL bound test results.

F-bounds test		HO: No levels relationship
Test statistic	Value	Signif	I(0)	I(1)
			Asymptotic: n = 1000
F-statistic	5.113407	10%	2.12	3.23
K	6	5%	2.45	3.61
		2.5%	2.75	3.99
		1%	3.15	4.43
Actual Sample Size	46		Finite Sample: n = 50
		10%	2.309	3.507
		5%	2.726	4.057
		1%	3.656	5.331
			Finite Sample: n = 45
		10%	2.327	3.541
		5%	2.764	4.123
		1%	3.79	5.411

ARDL Long Run Form and Bounds Test.

Dependent Variable: D(real GNP).

Selected Model: ARDL(4, 3, 4, 4, 1, 4, 4).

As corroborated by the ARDL Bounds test, an ECM-Long Run Test was executed to establish a long-run association between the variables. As depicted in Table 7, the coefficient of ECT or CointEq(−1) that is, GNP(−1), which signifies the adjustment coefficient, exhibits statistical significance (p-value: 0.0000) with a negative coefficient of −0.324. This negative coefficient implies that the speed of adjustment towards long-run equilibrium is 32.4%. This statistical confirmation underscores the presence of a lasting or long-run effect of the selected independent variables on the real GNP of the country. This test further unveils the intricate interplay of variables influencing India’s long-term economic growth. The significance of lagged GNP, GFCF, Forest Land, and temperature and rainfall variables underscore the multifaceted nature of factors shaping sustained economic development.

Table 7. ECM-long run test.

Variable	Coefficient	Std. error	t-statistic
C	3.615**	0.948	3.815
GNP(−1)*	−0.324**	0.106	−3.068
GFCF(−1)	0.231**	0.078	2.971
FL	1.674*	0.647	2.589
CO2(−1)	−0.049	0.030	−1.617
MaxT(−1)	−3.056**	1.085	−2.817
MinT(−1)	−0.035	0.813	−0.043
RF(−1)	0.599**	0.176	3.406
D(GFCF)	0.499**	0.076	6.554
D(GFCF(−1))	0.044	0.065	0.666
D(GFCF(−2))	0.156*	0.061	2.550
D(CO2)	−0.013	0.025	−0.517
D(CO2(−1))	−0.022	0.021	−1.024
D(CO2(−2))	−0.031	0.021	−1.434
D(MaxT)	−0.884	1.013	−0.873
D(MinT)	0.997	0.723	1.378
D(RF)	0.099	0.062	1.584
D(RF(−1))	0.335*	0.135	2.490
D(RF(−2))	−0.309	0.211	−1.464
D(RF(−3))	−0.146	0.169	−0.864

Note. p-value incompatible with t-Bounds distribution.

Dependent Variable: D(GNP).

Selected Model: ARDL(1, 3, 0, 3, 1, 1, 4).

Case 3: Unrestricted Constant and No Trend.

Sample: 1971 2020.

Included observations: 46.

** & * indicates significance at 1 and 5 percent levels respectively.

This study centrally addresses the intricate nexus between climate change variables and India’s real GNP, scrutinizing a diverse array of factors such as CO₂ emissions, FL, RF, MaxT, MinT, and GFCF. The findings from ARDL highlights valuable insights into the long-term dynamics of these variables, with notable implications for policy formulation and strategic decision-making. CO₂ emissions exhibit a significant negative impact on real GNP, emphasizing the importance of emission reduction measures not only for environmental conservation but also for sustaining economic growth (Grossman & Krueger, 1995; Hitz & Smith, 2004; Hope, 2006; Yohe et al., 2007). FL emerges as a positive determinant, highlighting the substantial contribution of the forestry sector to economic growth. Additionally, GFCF proves to be a crucial factor, signifying the importance of strategic investments in infrastructure, technology, and capacity building for sustained economic advancement. The study also delves into the adverse effects of MaxT and MinT both showing significant negative influences on real GNP. This underscores the vulnerability of India’s economy to climate-induced temperature changes, particularly in the agricultural sector. Furthermore, RF demonstrates a positive impact, reflecting support to agriculture, industries, enhancing hydropower generation, sustaining livestock and fisheries, promoting biodiversity, improving public health, and contributing to tourism. Theoretical contributions of this study lie in its comprehensive exploration of the climate-economic nexus in India, filling a notable gap in prior research. By employing the ECM-Long Run Test, the study not only elucidates the long-term relationships but also provides a nuanced understanding of short-term dynamics, allowing for robust insights into the adjustment process. The findings contribute to advancing scientific knowledge by offering a holistic view of the complex interactions between climate variables and economic outcomes, guiding policymakers toward informed decisions for sustainable and resilient development pathways. This research serves as a beacon, not only identifying problems but also presenting actionable solutions, emphasizing the imperative of addressing climate change impact for India’s prosperity and sustainability.

4.6. Diagnostic tests

These tests were conducted to ensure the reliability and validity of the employed ARDL model, thereby avoiding erroneous interpretations and conclusions from the study. Table 8 demonstrate that the ARDL model is free from serial correlation (Breusch-Godfrey Serial Correlation test) and heteroscedasticity (Breusch-Pagan-Godfrey test) issues. Furthermore, the normality assessed by Jarque Bera test (0.4811) with a probability value of 0.7835 indicate that the model is normally distributed (Figure 3). Ramsey RESET test also confirmed stability of the model at a 5% significance level, providing further assurance of the model’s reliability. Lastly, the Cumulative Sum of Recursive Residuals (CUSUM) and Cumulative Sum of Squares (CUSUMSQ) plots (Figures 4, 5) demonstrates that both CUSUM and CUSUMSQ statistics fall within the critical bounds at 5% level, indicating that the applied ARDL model remains stable throughout the observation period (Brown et al., 1975). Overall, these diagnostic tests contribute to the robustness and stability of estimated ARDL model, enhancing the reliability of the findings and supporting the validity of the conclusions drawn from the study.

Figure 3. Normality test of ARDL model.

Figure 4. CUSUM test.

Figure 5. CUSUMSQ test.

Table 8. Results of diagnostic tests.

Test	Statistic	Probability
Breusch-Godfrey Serial Correlation test (F statistic (Prob 19, 26))	0.8428	0.6451
Breusch-Godfrey Serial Correlation LM Test (F statistic (Prob 4,22))	0.2313	0.9178
Jarque-Bera – Normality test	0.4811	0.7835
Ramsey RESET Test (log likelihood ratio)
‘t’ test	0.5054	0.6177
‘F’ test	0.2554	0.6177

4.7. ARDL-granger causality test

The analysis in Table 9, employing the lag length determined by the AIC of the ARDL model, elucidates significant long-run influences of all variables on real GNP. Notably, bi-directional causality exists between CO₂ emissions and real GNP (Table 9), reflecting the challenge of balancing industrialization-driven economic growth with environmental concerns. The association between MinT and real GNP also follows a similar bi-directional pattern. The pursuit of higher GNP contributes to increased greenhouse gas emissions, necessitating a delicate balance to address environmental repercussions. Similarly, GFCF exhibits a bi-directional relationship with real GNP, showcasing the intertwined dynamics of capital investment and economic output. FL, MaxT, and RF, on the other hand, demonstrate a unidirectional association with real GNP. As the nation advances in adopting climate-resilient technologies and sustainable practices, there is potential for concurrent improvements in environmental quality and economic growth. Addressing the adverse effects of human-induced climate change is imperative for averting destructive consequences in the long run.

Table 9. ARDL-Granger causality test.

Direction of Causality - HO	Obs	F-statistic	Decision	Direction of causality
GFCF does not Granger Cause real GNP	46	8.798**	Reject HO	Bi-directional
real GNP does not Granger Cause GFCF		3.189**	Reject HO
FL does not Granger Cause real GNP	46	2.899**	Reject HO	Unidirectional
real GNP does not Granger Cause FL		0.126	Do not Reject HO
CO2 does not Granger Cause real GNP	46	3.756**	Reject HO	Bi-directional
real GNP does not Granger Cause CO2		2.511**	Reject HO
MaxT does not Granger Cause real GNP	46	7.226**	Reject HO	Unidirectional
real GNP does not Granger Cause MaxT		1.214	Do not Reject HO
MinT does not Granger Cause real GNP	46	5.333**	Reject HO	Bi-directional
real GNP does not Granger Cause MinT		2.145*	Reject HO
RF does not Granger Cause real GNP	46	2.131*	Reject HO	Unidirectional
real GNP does not Granger Cause RF		0.810	Do not Reject HO

** & * indicates significance at 1 and 5 percent levels respectively.

5. Conclusions and suggestions

The prevalence and repercussions of extreme weather events, coupled with the climate change crisis, present formidable challenges to India’s economic trajectory. India stands among the top five nations grappling with substantial economic losses and human casualties arising from climate-related disasters. Previous scholarly endeavours (Stern et al., 2006; Nordhaus, 1991; Tol, 2008; Yohe & Schlesinger, 2002) have underscored the nexus between climate variability and economic growth, emphasizing the susceptibility of India to the impacts of climate change. This vulnerability is exacerbated by a substantial reliance on climate-sensitive sectors and a gradual pace in formulating and implementing resilient strategies. Hence, comprehending the intricate relationship between climate variability and India’s real GNP becomes imperative for crafting sustainable development pathways resilient to climatic adversities. Employing the ARDL model, this study discerned noteworthy associations. RF, FL and GFCF demonstrated robust and positive correlations with real GNP. In contrast, variables such as CO₂ emissions, MaxT and MinT exhibited notable variability. The empirical findings not only resonate with prior research in both short and long-run contexts (Roy & Haider, 2018; Belford et al., 2020) but also diverge from the conclusions drawn by Adejumo (2021), whose research posited a positive impact of rainfall on economic growth in Nigeria. The model’s stability and reliability were affirmed through statistical measures, including a significantly negative coefficient for the ECT, denoting a 32.4% speed of adjustment towards long-run equilibrium, and a high adjusted R² value of 99%, indicating the model’s robust explanatory power. The Granger causality test revealed that all variables significantly influenced real GNP in the long run, with a bidirectional causality observed between real GNP and CO₂ emissions, GFCF and MinT. This study emphasizes the importance of addressing climate change, reducing CO₂ emissions, promoting sustainable practices, and managing temperature volatility and rainfall variability to ensure sustainable economic growth in India. Addressing climate change, reducing CO₂ emissions, and promoting sustainable practices such as afforestation, renewable energy adoption, and climate-resilient agriculture can help mitigate the adverse effects of climate change on economic growth. Furthermore, protecting and maintaining forest land is vital for sustainable economic development. Encouraging strategic investment policies and technological advancements, particularly in capital formation, can foster sustained economic growth. Given the adverse impacts of temperature volatility and rainfall variability on economic growth, it is imperative to prioritize climate adaptation measures. Enhancing resilience in agriculture, infrastructure, and water management can help India achieve sustainable and inclusive economic growth while safeguarding the environment for future generations.

In light of these insights, the study advocates imperative actions to address climate change, curtail CO₂ emissions, advocate sustainable practices, and adeptly manage temperature volatility and rainfall variability. Implementing afforestation, adopting renewable energy, and promoting climate-resilient agriculture emerge as vital strategies to mitigate the adverse effects of climate change on economic growth. Furthermore, safeguarding and nurturing forest land are identified as pivotal for sustainable economic development. The study underscores the significance of endorsing strategic investment policies and embracing technological advancements, particularly in capital formation, to foster sustained economic growth. Given the detrimental impacts of temperature volatility and rainfall variability on economic growth, the study advocates prioritizing climate adaptation measures. Enhancing resilience in agriculture, infrastructure, and water management emerges as a linchpin for India to achieve sustainable and inclusive economic growth while safeguarding the environment for future generations. In delineating future research directions, the study posits a need for comprehensive investigations into the specific impacts of climate variability on diverse sectors of India’s economy, with a particular emphasis on the vulnerability of agriculture. The effectiveness of climate resilience measures and policies warrants scrutiny at both national and regional levels, considering India’s diverse climate zones and regional disparities. Moreover, delving into the potential co-benefits of climate action on economic growth is highlighted as a crucial avenue. Investigating how sustainable development practices can synergistically lead to reduced emissions and foster economic prosperity represents a pivotal research trajectory. Lastly, the study underscores the imperative for interdisciplinary research, integrating climate science, economics, and social sciences to provide a holistic understanding of the intricate relationship between climate change and economic growth in India. In conclusion, future research in India should be geared towards generating actionable insights that underpin evidence-based policymaking, advocate for sustainable practices, and champion inclusive economic growth. This research agenda is essential in navigating the multifaceted challenges posed by climate change and fostering a resilient and prosperous future for India.

Authors’ contributions

K. Nirmal Ravi Kumar: Conceptualization, Methodology; Adinan Bahahudeen Shafiwu: Data curation, Writing – original draff and Supervision; K. Gurava and M. Jagan Validation, Writing – review & editing.

Disclosure statement

The authors declare no conflict of interest.

Data availability statement

Data Available upon Request

Additional information

Funding

This research received no external funding.

Notes on contributors

K. Nirmal Ravi Kumar

Dr. K. Nirmal Ravi Kumar holding Master’s and Ph.D. degrees in Agricultural Economics, has studied at Acharya N.G. Ranga Agricultural University (ANGRAU). He has a brilliant academic career with specialization in ‘Agricultural Marketing’ both in his post-graduate and doctoral programmes. Dr. Kumar is currently Professor & Head (Agril. Economics) in Agricultural College, Bapatla. He is actively involved both in agricultural research and teaching activities during the past twenty-two years in the ANGRAU. He also worked as Director (Agricultural Marketing) in National Institute of Agricultural Extension Management (MANAGE), Ministry of Agriculture & Farmers’ Welfare, Government of India. He was the recipient of ‘Sri Mocherla Dattatreyulu Gold Medal’ (2013) and ‘State Best Teacher Award’ (2016). Dr. Kumar has written extensively and has to his credit 10 books, and six (6) published articles in reputable journal of higher impact.

K. Gurava Reddy

Dr. K. Gurava Reddy has played a pivotal role in advancing agricultural research and development, particularly in the domain of enhancing smallholder livelihoods through innovative institutional arrangements. Serving as the Technical Secretary to the Vice Chancellor of ANGRAU and a Visiting Scientist at ICRISAT, his contributions span various areas. Noteworthy achievements include training watershed teams in Participatory Rural Appraisal techniques, conducting research on critical issues such as cotton farmers’ suicides and the productivity of scientists, and promoting Dry Direct Seeded Rice cultivation, significantly expanding its acreage in Guntur district. Dr. K. Gurava Reddy’s multifaceted contributions underscore his dedication to advancing agricultural practices and improving rural livelihoods.

M. Jagan Mohan Reddy

Dr. M. Jagan Mohan Reddy has 23 years of experience in the areas of teaching, extension, communication and administration in the university. He started his career as Assistant Extension Specialist in Agriculture Research Station, Tandur in 1998, from there he continued his journey has a good teacher and administrator till date. He worked has a teacher at College of Agriculture for 10 years, Extension specialist and Programme Coordinator at KVK, Palem, Mahabubnagar dist and DAATT Centre, Ranga Reddy district for 6 years, 1 year at Agril. Information & Communication Centre, Rajendranagar, Hyderabad. He published nearly 105 research papers; Guided 12 students : M.Sc (Ag)-10-major; 5 minor and Ph.D-02 scholars were completed as major; 2 are under guidance as major, and organized 3 webinars.

Adinan Bahahudeen Shafiwu

Adinan Bahahudeen Shafiwu, Ph.D., is currently a J-Pal African Scholar who has just finished his project on Digitalization of Cocoa Value Chain in Ghana under the DigiFi initiatives. He has worked and participated in J-pal initiatives that involves Randomized Control Trial. As part of his training under J-Pal he participated in both DIWA and Development Methodologies workshops here in Ghana and Morocco-Rabat respectively. He holds a PhD and MPhil in (Agricultural Economics) and also a chartered Health Economics certificates and currently works with the University for Development Studies in the capacity as a lecturer. He is an expert in Adoption Studies, efficiency, welfare and consumption studies. He is into modeling and has deep seated knowledge in econometrics and its models. A good data manager. He has also consulted for Plan International Ghana and Oxfarm on both midline and Baseline survey. He has published over twenty (20) research articles in reputable journals with higher impact. He is Highly regarded for his good leadership, communication, proposal writing, research, and analytical skills. Able to establish, implement and monitor systems for effectiveness and efficiency at organizations.