Wholesome | From Headcount To Footprint

Overview

Motivation

Despite what non-believers may make you think – climate change IS real – and its rampant effects continue to affect the entire globe in the present. The worsening climate has only caused us to question the effectiveness of our climate efforts in reducing emissions. Given that the world’s population has only ever increased in number, this implies that more and more people should be able to contribute to the reduction of carbon emissions – but that’s not exactly the case. So, how exactly does population correlate to CO₂ emissions? We look at data from the Philippines and its neighboring countries to find out.

Background

Over the past five decades, the Philippines has witnessed a significant rise in its population, sparking major concerns about its environmental impact, specifically its carbon footprint. Hence, understanding the relationship between population growth and carbon emissions has become essential for devising effective sustainable development strategies. Furthermore, comparing the Philippines' carbon footprint to that of neighboring countries can provide valuable insights into the drivers of national emissions.

Two essential studies serve as the foundation for this research. The first study uses advanced time series data mining techniques to estimate carbon footprint emissions in the Philippines. Using data from 1990 to 2019, the study finds a strong association between population growth, energy production, and CO₂ emissions. Notably, it emphasizes the importance of population dynamics as a major source of carbon emissions.^[1]

The second study expands the scope to include five ASEAN countries, including the Philippines, and looks at the relationship between economic development, industrial value added, population, and carbon emissions. Panel regression study finds a positive relationship between economic and population growth and carbon emissions, with industrial value added having little impact.^[2]

To deepen our understanding, we intend to broaden both the spatial and temporal scope in our research. We can obtain deeper insights into the dynamics of population growth and its implications for carbon emissions by incorporating data from a broader range of sites outside of the ASEAN region and extending the time period to include more decades. This larger perspective will help to build more complete and nuanced methods for addressing environmental concerns.

^[1] Lim & Cortez. Carbon Footprint Prediction in the Philippines Using Time Series in Data Science

^[2] Prinadi, Sarungu, Suryantoro, & Gravitiani. Impact of Economic Growth, Industrial Value Added, and Population on Carbon Dioxide Emissions in the Asean Region

Problem

The project aims to answer the following:

◦ How has the population growth in the Philippines over the past fifty years impacted its carbon footprint?

◦ How significant is the disparity between the carbon emissions in the Philippines and other nearby countries?

Solution

The project aims to do the following:

◦ Understand and assess the correlation between population growth and carbon footprint.

◦ Formulate actionable plans that will address the continuously growing issue of carbon emmissions.

Null Hypothesis

There is NO significant correlation between population growth and carbon footprint.

Alternative Hypothesis

There is a significant direct correlation between population growth and carbon footprint.

Data

Description

The dataset includes 50 years of historical population data for the Philippines, as well as corresponding carbon emission statistics (including per capita). It also includes comparative data from 20 other nearby Asian countries to help analyze regional disparities.

Size

The data set has a total of 1050 observations.

Preprocessing

To ensure consistency, the data collected from the sources were properly formatted and compiled. It was also cross-checked with multiple sources and ensured to be the most updated for accuracy.

Collection

Data was gathered through desk research on government websites, international organization reports, and reputable academic databases.

For population data, the team’s main source of information is World Bank, while the supporting sources are Worldometer and MacroTrends. Population data first gathered from the World Bank website was subjected to cross-checking with the latter two websites. Any missing and inconsistent data from the World Bank website was added and replaced, respectively, in the data set for consistency and accuracy. Rest assured that the data from Worldometer and MacroTrends are credible as their source is the 2022 Revision of World Population Prospects. This is the twenty-seventh edition of the official United Nations population estimates and projections, which also includes data for selected dates from the 1950s and onwards.

For carbon emission data, all information was originally gathered from Our World in Data whose source is the Global Carbon Budget 2022 paper which can be accessed in ESSD Copernicus. However, we soon realized that around 20 years of CO₂ emission data for Timor-Leste was missing. Thus, more data was collected from the Emissions Database for Global Atmospheric Research (EDGAR). Their 2023 report includes a more complete list of fossil CO₂ emission data from 1970 to 2022 (most updated data so far), and so we decided to use their data instead.

Methods

Data Visualization

Question 1

How has the population growth in the Philippines over the past fifty years impacted its carbon footprint?

From the line plot shown, it can be seen that there is a straight and linear increase in the Philippine population from 1974 to 2022. And despite CO₂ emissions having increased substantially in the same amount of time, it does not rise as straightforwardly. Instead, emission data shows clear dips and steep rises between years, indicating that several factors other than consistent population increase may have contributed to its varying growth.

Question 2

How significant is the disparity between the carbon emissions in the Philippines and other nearby countries?

The bar plot describes the total CO₂ emissions of countries within the scope of the research for the past 50 years. It is clear that China is a noticeable outlier in the data, with about 3 times as much emissions as the next, Japan. Compared to the Philippines, these countries have significantly greater carbon emissions. A more general view of the plot, however, shows notably smaller disparities between the Philippines and the other 18 countries.

Hypothesis Testing

To assess which of the hypotheses to accept and reject, the team decided to use Spearman’s Rank Correlation on the dataset. To note, this method assumes that the samples are uncorrelated, corresponding to the null hypothesis. As the data includes samples from 21 countries including the Philippines, we decided to apply Spearman’s rank correlation to each country accordingly. This way, we get an overview of the dependency of carbon emissions and population data for each country, and identify a general consensus and possible outliers.

From testing, we got an almost unanimous result of p-values close to zero. This implies that the samples being uncorrelated is very unlikely, and since the majority of the correlation coefficient is close to 1, it can be said that there is a strong positive or direct correlation between population and carbon emissions. Thus, the null hypothesis is rejected in favor of the alternative.

Note that all but one country actually showed a p-value close to 0.5, which is Palau. The probable statistical independence between the country’s carbon emissions and population data could probably be attributed to the fact that Palau has in fact the lowest population statistic in the data set. Perhaps that data characteristic also somehow implies that there are more significant factors other than population that can affect carbon emissions for countries whose population are on the lower side of the spectrum.

Results

Discussion

From our data exploration, we can gather several notable results:

Rejecting our null hypothesis in favor of the alternative reveals an answer to our first research question. The direct or positive correlation between population and carbon emission implies that as population growth in the Philippines increased, its carbon footprint increased as well. However, it is important to note that population is not the be-all and end-all cause of the country’s emissions. Other factors likely affected it, considering that our visualization still showed dips in total emissions despite a continuous increase in population data.

There is little disparity between the carbon emissions of the Philippines and the majority of the countries included in this project. However, this generalization does not apply to Japan, and most definitely not China. While Japan has considerable amounts of emissions totalling at around 56 billion tons, it certainly does not come close to China whose total comes up to 260 billion tons. Compared to these outliers, the Philippines’ emission data for the past 50 years only amounts to 3.50 billion tons, which is undeniably only a fraction of the recorded emissions from China.

Implications

These results have significant implications as we can clearly see where the main issues lie.

First, by confirming that population growth can influence and contribute to carbon emissions, we can isolate which countries have denser populations and urge them to be more proactive in taking climate action. Policies that address CO₂ emissions should be more strictly implemented in significantly populated areas, as they have the most potential to either worsen or improve their carbon emission output. Authorities could also formulate better laws and policies to address the issue based on the findings from this project. That being said, despite the Philippines not being a top contributor to CO₂ emissions, our governmental authorities should still take action as our climate action programs and policies are nearly imperceivable.

Second, having established that population is only part of the total contributors to CO₂ emissions, this project opens up further areas of research concerning other factors that could affect emission data. This is also a crucial endeavor as carbon emission is one of the most prevalent causes of global warming, and identifying other and more reducible factors that contribute to its unwanted growth could be more beneficial than trying to control population growth itself.

Limitations

It is also crucial to note that despite the results of this project, we were ultimately met with a few limitations that should be kept in mind for future endeavors.

The data that we used in this project was collected purposely, and thus may have introduced biased results. There are also present inconsistencies in the data that cannot be avoided, as different organizations and databases have different calculations of CO₂ emissions and population. More comprehensive data (not just population) could also be helpful for more nuanced policy recommendations after concluding the results.

The usage of the Spearman’s Rank Correlation as our statistical technique required the data to be monotonic in nature, which could cause issues for data that does not follow that trend. From testing, it seemed that Palau was actually not monotonic, and so the differing result from its p-value could be attributed to the technique itself. Perhaps better statistical treatments could have been employed.

The scope of this project may have been too large for the overall theme of “Pilipinas in a Nutshell”, and thus we weren’t able to focus on Philippine data and rather focused on the bigger picture. As such, we lack data regarding which regions in the Philippines are denser in terms of population and thus have higher contributions to CO₂ emissions. Should the focus shift outside the Philippines setting, however, we instead recommend extending the temporal or geographical scope of the research as only about 20 other countries are included in this project.

Being short on time unfortunately cost us our machine learning implementation. Consequently, our ability to fully leverage machine learning was hindered, and we must rely solely on the visualized data for drawing conclusions, which limits the depth of our analysis.

Conclusion

This project aimed to see how the population growth in the Philippines has impacted its carbon footprint. And indeed, we found out that the country’s population and CO₂ emissions rose together throughout the last 50 years. We were also able to grasp the disparity of our CO₂ emissions from the ones of other nearby countries. Ultimately, we were able to achieve the main objectives of this project.

However, we strongly believe that this endeavor doesn’t stop here. As the issue of rising CO₂ emissions becomes more relevant than ever, we implore the following sectors of society to take action.

Lawmakers

The results of this study emphasize the need for stronger foundations, not only for climate change policies, but also for sustainable development strategies, public awareness initiatives, and effective population management. The government must prioritize robust action plans that address these points.

Researchers

Building on this study, future researchers should further investigate CO₂ emissions considering various factors besides population growth, such as economic and industrial activities. Although the correlation discovered shows promise, it is without a doubt that this issue goes way beyond this one facet.

Filipinos

And finally, we urge every single Filipino to constantly educate themselves on the impacts of CO₂ emissions, actively adopt sustainable practices at individual and community levels, and bravely advocate for climate protection. By taking these steps, we can safeguard a more secure future for our country.

Hearing all of this might seem daunting. However, with each sustainable choice made, every single one of us contributes to meaningful change. Together, we can shape a future where our actions redefine "from headcount to footprint."

Team

Kyle Matthew Martinez

Heya, I’m Teo! I’m a third-year Computer Science student at the University of the Philippines-Diliman. Data science may be an unfamiliar subject to me, but I’m willing to learn through projects like these. When I’m not struggling with my academics, I usually make illustrations, play games, and sleep.

Maria Kaila Ondoy

Hello, world! My name is Kaila, and I’m a third-year undergraduate taking BS Computer Science at the University of the Philippines-Diliman. I’ve always had a love for creating and problem solving. Now, I’m looking forward to delving into the field of data science and working with the team!

Paul Jaren Perez

Hello, I'm Jaren! I am currently a third-year student taking BS Computer Science at the University of the Philippines-Diliman. This is my first time ever working on a data science project and although it honestly feels overwhelming, I am extremely excited to see what's in store for me and the team!

From Headcount To Footprint

Understanding the Impact of Population Growth to the Carbon Footprint of the Philippines