This project is an analysis on the correlation between tree cover loss and carbon emissions. This project aims to give a better understanding on how to approach the reduction of carbon emissions.
Climate change is the phenomenon of great shifts in global temperature and weather patterns that occur over long periods of time; however, this rate of change of our climate has been accelerating.
One of the leading causes of climate change is the large amount of carbon dioxide emissions that are released into the atmosphere.
Additionally, one of the biggest sources of carbon emissions is deforestation.
The amount of deforestation and tree cover loss that occurs in the Philippines causes a large amount of carbon emissions.
Our project is an analysis on the correlation between tree cover loss and carbon emissions. This project aims to use data science to gain a better understanding on how to approach the reduction of carbon emissions.
Climate change is a major environmental problem we face today. For example, recently, scientists have declared March of 2024 to be the warmest March ever recorded globally. Moreover, over seven thousand schools in the Philippines have suspended onsite classes this April due to the extreme heat. Clearly, this phenomenon is a very real and tangible problem that we as Filipinos face in our daily lives.
One of the significant causes of this problem is the increasing levels of carbon dioxide in the atmosphere which intensifies the greenhouse effect and in turn, increases the global temperature. Additionally, we know that deforestation is one of the big causes of carbon emissions. This is because trees can absorb carbon dioxide from the air, and store it within their systems, and when they are cut down, a large amount of the carbon they store is released back into the atmosphere.
There is no difference in the annual level of carbon emissions experienced by the Philippines and by each province based on the amount of tree cover loss they experienced.
The annual level of carbon emissions experienced by the Philippines and by each province is higher proportional to the amount of tree cover loss experienced.
Analyze the correlation between tree coverage loss and levels of carbon emissions.
Our datasets were sourced from Global Forest Watch
The first dataset details the annual tree cover loss per region and per municipality from the years 2001 to 2022, with separate measures for each level of percent canopy cover (>10%, 15%, 20%, 25%, 30%, 50% and 75% in the year 2000).
The second dataset details the recorded amount of annual forest greenhouse gas emissions per region and per municipality from the years 2001 to 2022, with separate measures for each upper level of percent canopy cover (>30%, 50% and 75% in the year 2000).
For our project, we will be using the annual tree cover loss and forest greenhouse gas emissions per region at the 30% canopy cover level. Processing the datasets resulted in 2 datasets with data for 81 provinces, each with 22 attributes, thus leading to an overall dataset size of 3564 data points.
To measure the correlation between the tree cover loss and carbon emission, Pearson correlation coefficients between were calculated tree cover loss in the provinces in the Philippines and carbon emission in the years 2001-2023.
To test the hypothesis, an F-regression method was used to attain a p-value corresponding to the significance of the tree cover loss variable on the amount of carbon emissions from Philippine provinces from the years 2001-2023. The test returned an F-statistic of 7e+14 and a p-value of 0, which means the null hypothesis stating that the tree cover loss does not affect carbon emissions is rejected.
Carbon Emmissions: Mg CO2e/yr
Tree Cover Loss: Hectares of tree cover loss per year
The hypothesis test returned an F-statistic of 7e+14 and a p-value of 0, which means the null hypothesis stating that the tree cover loss does not affect carbon emissions is rejected.
Based on the graph, there appears to be a high linearity between the amount of tree cover loss experienced by a province and the total amount of carbon emissions it outputs. This is backed by the linear regression we performed. It can be seen that the differences between the predicted values and the dataset’s existing values are consistently small, revealing a trend. Even the data point with the highest amount of both tree cover loss and carbon emission, namely Palawan in 2022, is close to the value predicted by the linear regression model.
Based on the statistical analysis above, there is a high correlation between the amount of tree cover loss and the amount of carbon emissions experienced by a province. This is notable since carbon emissions are one of the leading causes of climate change. This could affect temperatures within these regions, which could affect crop yields which are one of our biggest exports. It can also put more of the population at risk of heat-related illnesses such as heatstroke. Thus, we demand that the government put a greater focus on creating regulations in order to combat the amount of deforestation and tree loss within our country. This should be one of our top priorities in order to combat climate change within our nation.
Hi! I'm Renzo. I'm a third year Comp Sci student from the University of the Philippines. I like programming and I am interested in service software development. I like reading and playing video games. Currently I am also watching Delicious in Dungeon.
I'm Zach and I'm a 3rd year Computer Science student in UP Diliman. I aspire to become a web developer in the future. I have a strong interest in music and I enjoy performing in gigs.
Hi! I'm Robbie and I'm in a 3rd Yr Computer Science student from the University of the Philippines Diliman. I'm passionate about coding and am looking into different aspects of programming, one of them being game development. I love playing RTS, FPS and survival games. I think more people should watch Frieren and Eighty-Six.
