South Asia, with its predominantly agrarian economy and high rural population density, faces significant challenges to food security. The region's vulnerability to land degradation, coupled with its large population and limited arable land, exacerbates this issue. Food security is significantly influenced by food production, which is, in turn, heavily impacted by global factors. Extreme weather events, trade restrictions, and rising oil prices have all contributed to the destabilization of food security in the region. Climate change poses a particular threat, impacting rain-fed croplands and livelihoods of small-scale farmers. Recent events such as extreme rainfall in Pakistan in 2022 and heatwaves in India have severely damaged agricultural production. In addition, Sri Lanka’s recent economic crisis, partly caused by fertilizer bans and climate change, highlights the interconnectedness of these issues. Climate services, while crucial, are often inaccessible or misunderstood by farmers. The agricultural sector's dependence on fossil fuels, particularly for machinery and inputs, makes it vulnerable to oil price shocks and contributes to environmental issues. Limited access to agricultural credit also hinders the adoption of climate-adaptive technologies and efficient farming practices. Therefore, this study aims to empirically analyze the relationship between climate change, agricultural credit, renewable energy, and food security in South Asian countries between 1990 and 2021, focusing on both the short-run and long-run effects, using a robust econometric model.

The empirical literature review extensively examines existing research on the impact of climate change, agricultural credit, and renewable energy on food security. Studies consistently highlight the negative consequences of climate change on crop production, livestock, and fisheries, leading to food insecurity and poverty. Climate-related events such as floods and droughts significantly impact agricultural yields and livelihoods, especially in developing countries with limited adaptive capacities. The literature also emphasizes the importance of agricultural credit in enhancing food security. Access to credit enables farmers to invest in improved inputs, technology, and climate-resilient practices, increasing productivity and overall food availability. However, smallholder farmers often face barriers in accessing formal credit, relying instead on informal, often insufficient sources. The role of renewable energy in food security is also explored, with a focus on its potential to reduce the reliance on fossil fuels, mitigate environmental degradation, and improve the sustainability of agricultural production. Studies indicate that using renewable energy can reduce energy costs, increase farmer incomes, and enhance food security. However, the long-term sustainability and scalability of renewable energy solutions in the context of growing energy demands also need to be considered.

This study uses panel data from five South Asian countries (Pakistan, Bangladesh, India, Sri Lanka, and Nepal) spanning from 1990 to 2021. Food security is measured using a food production index from the World Bank's World Development Indicators (WDI). Renewable energy is represented by total renewable energy consumption data from the International Energy Agency (IEA). Climate change is proxied using temperature change data from the FAO. Agricultural credit data is also sourced from the FAO. Control variables include population and inflation rates, also from the WDI. To analyze the dynamic relationships between these variables, the study employs the Dynamic Common Correlated Effects (DCCE) estimation method developed by Chudik and Pesaran (2015). This method is chosen because it explicitly addresses cross-sectional dependence, slope heterogeneity, and endogeneity issues, providing robust cointegration results. The study first conducts descriptive analysis of the data, followed by tests for cross-sectional dependence using the biased-adjusted LM-CD test. Pesaran's CADF and CIPS tests are then applied to check for variable stationarity. The Pedroni cointegration test and Westerlund's cointegration test are used to examine the long-run relationships between food security and the explanatory variables. Finally, the DCCE method is used to estimate both short-run and long-run coefficients, providing a comprehensive understanding of the relationships.

The descriptive analysis provides summary statistics for each variable, including mean, standard deviation, minimum, maximum, kurtosis and skewness. Cross sectional dependence test confirms the presence of cross-sectional dependence among the variables. The results of unit root tests (CADF and CIPS) show that food security, climate change, renewable energy, agricultural credit, inflation, and population are stationary either at level or at first difference. The Pedroni cointegration test reveals a long-term relationship between the variables. The Westerlund cointegration test confirms the presence of cointegration, signifying a long-run equilibrium among the variables. The DCCE estimation results indicate that climate change has a statistically significant negative impact on food security in both the short-run and the long-run. Renewable energy shows a positive and significant effect on food security only in the short-run. This may be because the use of renewable energy sources is a short term solution to address the climate change risks. In the long run, renewable energy has little impact on food security. Agricultural credit has a positive and significant effect on food security in both the short-run and the long-run. Inflation and population growth both negatively affect food security in the long-run, with inflation also negatively affecting it in the short-run. The R-squared value suggests that the model explains a substantial portion of the variation in food security.

The findings of this study confirm the significant negative impact of climate change on food security in South Asia, corroborating existing literature. The adverse effects of climate change on agricultural productivity highlight the urgent need for climate change adaptation and mitigation strategies. The positive short-run effect of renewable energy on food security emphasizes the importance of transitioning towards sustainable energy sources in agriculture. However, the long-run insignificance of renewable energy highlights the need for broader systemic changes to address the long-term challenges of food security. The positive and significant effect of agricultural credit underscores the crucial role of accessible and affordable credit in enhancing agricultural productivity and food security. Policies aimed at improving access to credit for small-scale farmers are vital. The negative impact of inflation and population growth emphasizes the need for comprehensive strategies addressing these macro-economic factors to ensure long-term food security.

This study provides valuable insights into the complex interplay between climate change, renewable energy, agricultural credit, and food security in South Asia. Climate change poses a significant threat to food security, requiring immediate action. While renewable energy offers short-term benefits, long-term solutions require broader systemic changes. Improving access to agricultural credit is crucial for enhancing productivity and resilience. Future research could focus on investigating specific climate-resilient agricultural practices, exploring regional variations in the impacts of climate change, and examining the effectiveness of policy interventions aimed at improving food security.

This study uses temperature change as a proxy for climate change, which may not fully capture the multifaceted nature of climate change impacts on agriculture. The study is limited to five South Asian countries, and the findings may not be generalizable to other regions. The data used in the study is based on existing statistical data. While the DCCE methodology addresses many econometric challenges, potential unobserved factors could influence the relationships between variables.