For years humanity has been able to unlock ingenious inventions to battle against obstacles of our development, from antibiotics to the internet. Yet one obstacle remains one of the most formidable challenges we face, one where ingenious inventions don’t seem to help; climate change. As our era grows in environmental uncertainty, we face and feel the intense consequences of our human activity. It seems hotter, and more humid than usual, or on the contrary, cooler and foggier than we’re used to. Increasing worries concerning climate change have called for various approaches to finding mitigation solutions, but all approaches begin with understanding patterns of climate change, an understanding stemming from econometric tools and statistical methods that illuminate the intricacies between climate variables (temperature and precipitation) and economic outcomes (GDP and mortality rates).
We see effects of climate change everywhere; the obvious being remarkable differences in the weather from a few years ago, but also, changing sea levels, loss of organism species, shifting poverty and community displacements that have led to disruptions in socio-economic stability. All the more contributing to the pressure we face to right our wrongs.
The desperation we face, coupled with the duties we collectively bear as inhabitants of Earth has led to the emergence of sub-branches within well-known academia, a prominent one being Climate Econometrics. This branch was first discovered when prominent figures at Eagle House in Jericho, Oxford (like Sir David Henry) noticed similarities between economic and climate time series data, indicating that perhaps traditional modelling approaches used in economics can be applied to studying climate time series. Such models include the classic time series analysis we’re all familiar with, whereby a model like the autoregressive integrated moving average (ARIMA) can be adapted to study climate data whilst considering trends like seasonality, especially useful as ARIMA can predict future trends in climate change based on historical patterns.
But before we get ahead of ourselves, let’s breakdown the point of this article. In the time we have remaining together, I’ll explain why studies like climate econometrics are necessary as a step to find mitigation strategies. And to end, I’ll discuss some econometric models used in understanding climate change.
Rural communities especially, experience the destruction of climate change first hand. Inhabitants of such communities tend to make a living off of agriculture, yet are currently waking up to scorching summer days where the soil they depend on shrivels and dries in the span of mere hours. Scenarios like this, where soil becomes obsolete and farmers are left with no opportunity to start a new harvest, are becoming more common as average temperatures rise. It doesn’t take a rocket scientist to figure out that slight changes in average temperatures affect agricultural productivity significantly, but it takes a climate econometrician to face this reality and make something of available data.
Staple foods birthed from rural communities like wheat and maize are becoming more and more scarce with every failed harvest caused by dry soil. This diminishes food security. The FAO warns that a one degree (Celcius) rise in average temperatures leads to a ten percent decline in overall crop productivity. The statistic itself is worrisome, but beyond the numbers, this reflects the struggle that families face to make a livelihood, or in the worst case, to feed themselves every day.
This is why it’s necessary to invent new branches dedicated to contributing long-lasting sustainable solutions that global leaders can adapt into policies. For those still not convinced that climate change has international ripples; if rural communities that provide the most basic food staples face shortages in harvest, it doesn’t just mean that the community itself may wipe out, it also means that local markets begin to suffer, consequently affecting intra-regional and inter-regional trade, then affecting national economies and over time, international economies.
Food shortages and destroyed harvests are not the only consequence of climate change that makes climate econometrics necessary as a branch of study. There is the health-related threat we face as a public. Rising temperatures pave the way for heat-related diseases like strokes as well as vector-borne diseases like malaria and dengue fever. Such illnesses are prevalent in warmer climates and spread in the blink of an eye when in lower-developed communities who suffer from lack of access to basic healthcare. To shed some perspective on how devastating the health-related effects of climate change may be, we source the WHO which estimates that climate change may create an additional 250,000 deaths between 2030 and 2050 alone - 250,000 individuals, lives, and futures. Without committed support like the aid we get from discoveries in climate econometrics, we may not be able to reduce this death count, posing yet another reason why climate econometrics remains necessary.
As we’ve discussed, threats of climate changes extend to the global economy. Climate change becomes an issue for everyone; humanitarians, economists, politicians and finally civilians. It is thus everyone’s responsibility to do something about it. Climate econometricians have taken it upon themselves to apply theories to real-life climate simulations. We’ve briefly discussed above the use of time series analysis in the field of climate change. Let us continue the rest of this article by exploring alternative uses of econometric models used to understand patterns of climate change better.
Vector autoregression is another tool used to analyse possible interdependence between multivariate time series variables. This model is utilised to understand relationships between climate parameters like precipitation, temperature and wind speed. Furthermore, cointegration analysis can be applied to climate change in order to understand long-term relationships between climate variables to identify connections between economic activity and climate indicators, generally analysed over time.
Lastly, up-and-coming machine learning models enable climate econometricians to predict climate variables and assess impacts of economic activity on climate change. Whilst this provides outcomes similar to ones achieved by using cointegration analysis, this approach differs as it makes use of forecasting models built from machine learning algorithms like neural networks or support vector machines.
What we have learnt from this relatively long article is that climate econometricians grasp the complex relationships between climate variables and economic outcomes, by employing various econometric methods on the basis of real-world data, be it historical patterns or future projections, the result can be contributed to environment-prioritising policies. Climate econometrics provides us the opportunity to make informed and logical political decisions that safeguard our economies and hence our people, and that is why it remains necessary, along with other branches of study dedicated to mitigating climate change as best as we presently can. Because it is imperative to face the reality of decades of human activity by collaboratively navigating the challenges of climate change.