SPATIO-TEMPORAL ANALYSIS OF CLIMATE CHANGE AND FOOD PRODUCTION: A CROSS REGIONAL COMPARISON BASED ON PANEL DATA

Abstract

Climate Change has already been realized and endorsed by multi-disciplinary researchers as an outcome of excessive economic activity based on fossil fuels. Experiencing consistent natural disasters, the global policy dynamics shifted more towards sustainability. However, the central questions still debatable are the disproportionate nature of the climate change impact, adaptation and mitigation measures, and possible ways to reduce the pace of changing climate that is harmful to human existence. One of the immediate global policy responses to climate change was the formation of international organizations that came up with major global agreements, i.e., the Kyoto Protocol and the Paris Agreement. The current study employs exploratory analysis to study the relationship between climatic variability and geographical location, income, and industrialization levels of countries in regimes when two important agreements took place. However, global warming is constantly increasing in the period of analysis (1991 to 2018), irrespective of geographical location, income, and industrialization levels. The results show that global warming slowed after the policy measures taken by these organizations. Among income groups, high income countries are experiencing greater climate variability (temperature) in terms of magnitude, while LIC’s warming pace is more pronounced than any other group. Climate variability expressed by rainfall shows volatile behavior in all categories considered. There is no clear pattern in rainfall behavior throughout the time used in the analysis, thus, adding to the existing challenges. Climate variability behavior for the degree of industrialization shows that newly industrialized countries' pace of additional warming is relatively faster than others. Regional results show that Europe and Central Asian countries are experiencing greater temperature variability, followed by the Middle East and North African countries, North America, Sub-Saharan Africa, South Asia, East Asia Pacific, Latin America, and the Caribbean. The study also highlights the distinction between climate, climate variability, and climate change considering their short-term and long-term changes in climatic variables. The study quantifies the spatial determinants of climate, climate variability, climate change, and carbon intensity to accelerate mitigation and adaptation measures. The study also incorporates the spillover effect of these determinants on climatic variables by using the spatial Durbin model. For climate variability and carbon intensity, we have considered panel data for 116 countries from 1991 to 2018. While for climate and climate change, we have cross-sectional data averaged for 30 years (1989 to 2018). GDP per capita, energy intensity, population, industrialization, and urbanization are significant determinants of climate variability (temperature), while energy intensity, population size, and proportion of urban population spillover affect nearby countries' climate variability. For climate variability expressed by rainfall is affected by energy intensity, trade openness has a spillover effect on nearby countries' climate variability (rainfall). Carbon intensity over the same period is influenced by the GDP per capita, trade openness, population size, and urbanization, while energy intensity has a spillover effect on the carbon intensity of nearby countries. Climate change (temperature), population density, and trade are key spatial determinants. For impact assessment, the present study evaluates the impact of climate change on food production by considering wheat, rice, and maize production per agricultural land. Certain non-climatic (fertilizer, machinery, labor, openness) and climatic input factors (temperature, temperature variability, rainfall, and rainfall variability) are used in the analysis by the spatial Durbin model. Results vary with the type of crop considered. For wheat production per unit of land area, fertilizer usage, labor, trade openness, rainfall, and its variability are major input factors affecting wheat production. For the spillover effect, increased fertilizer use by neighboring countries has a negative significant spillover effect on the domestic country's production. Also, an increase in labor usage by neighboring countries negatively affects wheat production in the home country. Free movement and access to wheat in domestic and neighboring countries positively impact wheat production in domestic countries. An increase in rainfall variability in one region and its neighboring countries increases wheat production in the domestic country. For rice production per agricultural land, fertilizer, agriculture labor, average annual temperature and its variability, and rainfall are major input factors affecting rice production. Increased machinery usage, rainfall, and variability have spatial repercussions on nearby countries. Maize production per agricultural land is affected by increased fertilizer usage, labor, machinery, temperature, rainfall, and their respective variability. In the case of maize, non-climatic input factors such as fertilizer, labor, machinery, and trade openness of nearby countries have a spillover effect on the domestic country’s maize production.