Transforming Data, Changing the Climate

According to National World, our atmosphere now contains more carbon dioxide (CO2) than any other moment in the history of mankind. As much as CO2 helps plants thrive and grow, its excessive and dramatic increase has become a critical cause of climate change and global warming.

From 1901 to 2020, global temperatures have increased by approximately 1.1°C — however, climate change extends far beyond increases in temperature. It also encompasses alterations in weather cycles — such as, droughts, floods, hurricanes, and more — as well as changes in other aspects, including electricity, transportation, wildlife, agriculture, ecosystems, and human health.

As a result of extreme climate changes over recent years, scientists — such as, climatologists, meteorologists, and ecologists — have increased their usage of analytical and Business Intelligence (BI) tools and softwares.

For instance, climate analysis is employed to get comprehensive insight into the earth’s historical and current climates, as well as to forecast future climate responses to fluctuations in natural and human-induced factors. These factors include the Sun, greenhouse gases (e.g., water vapor, carbon dioxide, and methane), and aerosols (e.g., from dust storms, pollution, fires, sea spray, or volcanic eruptions).

So, let’s take a look at some of the keyways in which data analytics is contributing to the fight against climate change.

1. Reduce carbon footprint

Carbon footprint refers to the cumulative greenhouse gas emissions (e.g., carbon dioxide and methane) produced by a person, activity, organization, business, location, or item. It encompasses direct emissions from fossil fuel combustion, land development, food consumption, transportation, goods manufacturing, logistics, and more. Essentially, almost everything around us contributes to carbon footprint emissions — which happens to be detrimental to the environment.?

For instance, the logistics industry is one of the largest contributors to carbon footprint. Almost every day, hundreds of trucks, cargo planes, vans, ships, and more are transported to numerous addresses to deliver goods. These actions result in high carbon emissions — particularly, CO2 emissions from fuel burning. In order to reduce their carbon footprint, logistics companies can employ data analytics tools — such as, artificial intelligence (AI) and machine learning — as well as various datasets — such as, distances traveled, vehicle routing, transit time, and holding tracking (when vehicles sit unused while waiting for planned pick-up’s and drop-off’s) for analysis.

AI-powered algorithms can give valuable?insights into how?shippers or carriers can optimize operations (e.g., routes) and become more fuel-efficient after processing massive amounts of datasets and discovering trends of waste.

For example, AI can instantly identify whether patterns exist within certain routes where prolonged traffic congestion or recurring scheduling issues result in greater fuel waste. Besides monitoring carbon emissions, companies can also predict future carbon footprint levels with predictive analytics and even reduce current emissions with the help of prescriptive analytics.

2. Recover lost ecosystems

An ecosystem refers to a natural environment where plants, animals, and other species can coexist together with the help of climates and landforms. Environmental disruptions — such as, logging, muddying rivers, storms, wildfires, excessive grazing, and more — impact the form and function of ecosystems. As a result, ecological restoration is necessary with the main goal to restore, restart, or expedite the regeneration of a disrupted ecosystem. Rehabilitation actions could be planned to recreate a pre-disturbed?ecosystem or to build a fresh ecosystem where none existed before.

With data analytics and BI solutions, recovering lost ecosystems is made much easier with more accurate and data-driven actions. Land mapping tools can help scientists scan and analyze various landforms on earth that require conservation efforts. Using descriptive analytics, scientists can then collect, analyze, and visualize several datasets — such as, land size, climate patterns, plant and animal species, soil health, and more — to understand the current environmental situation. With diagnostic analytics, they can understand why certain ecosystems were destroyed or lost — for instance, due to floodings, near volcanic areas, prolonged droughts, and more.

With a proper understanding of lost ecosystems and current circumstances, scientists can employ predictive analytics to analyze big data from past and current conditions to forecast whether the ecosystems can be recovered with proper conservation efforts. With the predictive insights, scientists can propose data-driven solutions that help recover lost ecosystems, which in turn help in tackling climate change issues.

3. Predict natural disasters

100 years ago, no one could’ve imagined having the ability to predict natural disasters — or taking the necessary preemptive measures to save lives and resources. Now, with predictive analytics technologies, natural disaster forecasting can effectively anticipate these catastrophic events amidst climate change issues. These technologies can detect warning indicators of impending disaster with more reliability and accuracy. With these indicators, authorities are able to organize contingency responses and protocols to limit the destruction caused by natural disasters.

Apart from predictive tools, big data also holds the key when it comes to forecasting natural disasters. Did you know that from 1990 to 2019, a total of 9924 natural disasters occurred around the world — and in 2021 alone, 432 disasters took place? These past natural disasters contain a wealth of data — such as, precipitation, wind speeds, climate cycles, and more — that can be collected, cleaned, and analyzed using predictive analytics software tools to get valuable insights into natural disasters. This helps weather forecasters in spotting early warning indicators in any meteorological event, allowing them to predict if a normal rainstorm will escalate into a natural disaster.

In addition, machine learning algorithms can also?collect and analyze climate change data to assist in forecasting natural disasters. The algorithm’s grasp of the distinction between natural disasters and routine weather grows as more training data is inputted. For instance, when a natural disaster strikes, data analytics tools can examine the climate characteristics and nuances before correlating them to past natural disaster data. If the current weather patterns match earlier data findings, then it’s a clear hint that a calamity is approaching. This is useful because the technique of spotting natural disasters using predictive analytics and machine learning algorithms is faster and more accurate, hence allowing scientists to announce warnings on time for safety precautions.

In conclusion, climate change is not just an environmental or weather-related issue, but it affects our daily lives, health, and safety as well. As cliché as it sounds, we are responsible to leave a better earth for the future generations, because, well, there is no “Planet B”. This is why leading BI companies — such as, Cubeware — are developing tools and platforms that help conserve the planet through data-driven climate change initiatives, leading to accurate actions with conservatory efforts.

To learn more about data analytics, visit us at www.cubeware.com. In addition to building end-to-end data analytics and BI solutions, Cubeware regularly curates educational articles on the most relevant components of the data analytics industry.