Data-driven approaches and applications have been witnessing a significant rise in recognition. Access to mountains of additional data, matched only by the advancement of algorithmic models and practical applications for these, give us an opportunity to use it for greater social and environmental good.
On the ground, a new wave of climate activism across the planet are pushing politicians and governments to take action on the climate crisis. In order to take impactful climate actions, it is key to measure and understand the factual reality of the problems at stake. In other words, data is the cornerstone for success in the fight against climate change.
Do you take your data with or without gaps?
Plan A analyses and compares countries on six dimensions – also known as the Plan A Themes. These themes help us categorise and analyse distinct datasets that are incredibly significant to predict, better understand the scenarios, and help avoid disastrous consequences of climate change.
In order to foresee precisely how climate change will impact a specific country or geography, it is important to analyse and correlate data of the indicators that paint an otherwise patchy picture of planetary ecosystems, social mechanisms and economic factors. The quest for ever-more accurate, up-to-date and meaningful data points to actually be able to feed those complicated predictive systems our computers are able to handle is at least three-quarter of the job.
The potential gaps in climate data
Good data is data that is sufficient to derive the results that validate the hypothesis of respective research study. If too many data points come to be missing, the entire machinery can disfunction, reinforcing certain observations rather than others because of a concentrated lack of data. This is the so-called survivor bias and is one of the many traps that lay on the quest for accurate predictions. Other types of biases associated with gender or social status have been accurately pointed out by statisticians. For more on this, we recommend Safiya Umoja Noble’s book on search algorithm racial and sexist bias.
Data is an essential part of building a technical infrastructure to realise global sustainable development.
The lack of quality data (for example irregular time series of indicators) in this regard hinders our ability to accurately predict the impacts of climate change. Our findings confirm that countries with little historic responsibility in emitting GHG emissions will suffer the most from the adverse impacts of climate change.
In the above plot, we present 25 selected climate indicators and the availability of data according to countries and regional areas. This paints an unequal picture of climate change on the basis of data gathering capabilities. When a country or region falls below 60% of data point completion, estimating the implications of climate change becomes a lot harder. About 100 countries, mostly in the Global South do not produce sufficiently reliable data for climate modelling and other associated applications.
Read also: The data-driven engine of Plan A
This data-short imaginary region covers 106.6 million people and 96 nations across all continents. There are ways around it, such as regressive analysis, proxy indicators or region-based aggregation. These methods allow us to patch up some of the missing information, but like our Data Team always says, nothing fills a data gap like data.
For a data-driven approach to climate action
Impactful climate actions at every level – governmental policy, private business innovation, individual/community practices) can and should be guided by realistic precisions, themselves driven by accurate and reliable data. Managing without monitoring and monitoring without measuring is an exercise in futility.
Likewise, no mitigation or adaptation project is beneficial if we fail to accurately measure these impacts. Climate models need a crisp and accurate picture to predict the biggest climate-induced changes on local communities and ecosystems.
Without this, we are just fumbling in the dark, pulling where we should push and have no certainty on the effects of our actions. And if there is one thing we have gathered data on is that time is not a currency we are rich in right now.
Do you know/have (open source) datasets that represents influences of climate change? Would you like to collaborate with us on climate analytics? Please talk to us at [email protected]