Keywords: global warming, statistical analysis, Granger causality, impulse response function, vector autoregression, variance decomposition, agricultural productivity
Statistical inquiry into the causes of global temperature changes
Observed changes in average global temperatures over time have led to two general avenues of discussion in the environmental literature. The scientific community has concentrated on the statistical detection of global warming and the determination of biological and industrial factors causing average world temperatures to rise. A second avenue of thought considers the issue of economic abatement by attempting to measure the pecuniary costs of global warming and the elimination of factors influencing this problem. This paper concentrates not on developing an economic model of global warming and environmental damage, but rather on examining the problem from a purely statistical vantage point. Utilising annual data from 1950 to 1991 and optimally determined vector auto regression specifications, it is shown that general industrial growth and greenhouse gas emission levels statistically cause a persistent increase in average global temperatures. In addition, this analysis shows that increasing average world temperatures have a statistically significant negative causal impact on agricultural productivity. Given that global warming is a long-term process culminating from decades of industrial activity, statistically significant causal results derived in this paper using short term data are interesting. Statistical results suggest that trends in average temperatures respond to short-run fluctuations in industrial activity and population growth.