Comparative Analysis of CO2 Emissions by Country and Industry
Author: Wendy Lei and Yuhao Shen
Introduction
Understanding the dynamics of CO2 emissions is critical for formulating effective environmental policies and achieving the sustainability goals set by international bodies such as the United Nations Framework Convention on Climate Change (UNFCCC) and the Paris Agreement. The primary objective of this research is to analyze and compare the trends in carbon dioxide (CO2) emissions across five key countries—China, India, Japan, South Africa, and the United States. And to examine these trends within specific industries including coal, flaring, gas, land-use change, and oil. By studying these patterns, this report aims to uncover insights that can guide policy decisions and contribute to global efforts in mitigating climate change impacts.
By focusing on both country and industry-specific emissions, this study aims to highlight the areas where targeted interventions can potentially yield the most significant environmental benefits. The comparison among different countries and industries will also provide a clearer picture of global and sectoral progress toward de carbonization and help in prioritizing action in the most critical areas.
Scope
This analysis focuses on a comprehensive dataset that captures the annual CO2 emissions of the selected countries, divided into specified industry sectors. The time frame for this study spans from 1960 - 2017, providing a temporal context that helps in understanding both short-term fluctuations and long-term trends in emissions.
library(tidyverse)
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✔ purrr 1.0.2
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ggplot(sus_final_data) +aes(x = year, y = co2) +geom_point(shape ="circle", size =1.5, colour ="#112446") +labs(title ="Chosen coutries CO2 emission Trend (1960-2017) ",y ="CO2 Emission Per Capita (million tonnes)", color ="") +theme_minimal() +facet_wrap(vars(country), scales ="free_x")
The countries chosen for this analysis represent a mix of developed and developing economies, each playing a significant role in global CO2 emissions due to their size, economic activities, and energy consumption patterns. China and the United States are the world’s two largest CO2 emitters, reflecting the impact of industrial-scale economic activities. India, as a rapidly developing economy, presents a case of growing energy demands a midst developmental challenges. Japan, as a highly industrialized nation, provides insights into emissions management in a technology-driven economy, whereas South Africa, with its unique dependence on coal, offers a perspective on emission challenges in an African context.
Data Visualization and Analysis
1. CO2 Emission by Country
# Select necessary columns and reshape the data into a long format# Here I assume that you want to plot the total co2 for each country by yearco2_data_long <- sus_final_data %>%select(country, year, co2) %>%pivot_longer(cols =c(co2), names_to ="variable", values_to ="value" ) ggplot(co2_data_long, aes(x = year, y = value, fill = country)) +geom_area(position ='stack') +theme_minimal() +labs(title ="Total CO2 Emissions by Country Over Time",x ="Year",y ="Total CO2 Emissions (million tonnes)",fill ="Country" )
We first set out to find the overall CO2 emission from 1960 to 2017, and it shows a clear and significant aggregate upward trend. A more significant and almost vertical rise happened around the 2000s, mainly contributed by China, we assume that it has to do with the big event around that ear, which is China joining the WTO. After that event, we can see that the more advanced economies (e.g. Japan, U.S. and South Africa) had a small decline in CO2 emission.
To explore further, and find out the relationship between CO2 emission and economic development, we decieded to then look at the significant time period since China joined the WTO.
2. Impact on China after Joining the WTO
after_wto_china <- sus_final_data %>%filter(country =="China") %>%filter(year >=2000& year <=2017)ggplot(after_wto_china, aes(x = year)) +geom_line(aes(y = co2_per_capita, color ="CO2 Emission Per Capita"), size =1.5) +geom_point(aes(y = gdp_growth *100, color ="GDP Grotwh"), size =1.5) +geom_smooth(aes(y = gdp_growth *100, color ="GDP Growth Trend Line"), method ="lm") +scale_y_continuous(sec.axis =sec_axis(~./100, name ="GDP Growth (scaled)")) +labs(title ="Impact on China after Joining the WTO",y ="CO2 Emission Per Capita (million tonnes)", color ="") +theme_minimal() +facet_wrap(vars(country), scales ="free")
Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
ℹ Please use `linewidth` instead.
`geom_smooth()` using formula = 'y ~ x'
Post-WTO Economic Boom and Environmental Cost: After China’s entry into the WTO in 2001, the nation experienced rapid industrialization and economic expansion, leading to a surge in CO2 emissions per capita as reflected in the steep climb up to around 2010. This rise corresponds with China’s efforts to become the “world’s factory,” resulting in increased energy consumption, primarily from fossil fuels, to fuel its manufacturing and export-led growth.
CO2 Emissions Plateauing: Despite the initial surge, there is a visible plateauing and subsequent decline in CO2 emissions per capita, indicating a gradual shift in China’s economic structure towards less carbon-intensive activities or an improvement in carbon efficiency. This could also reflect the implementation of more robust environmental policies and a growing investment in renewable energy sources as part of China’s commitment to mitigating climate change.
Decoupling GDP Growth from Emissions: The trend in GDP growth remains positive, albeit with some fluctuation, suggesting that the Chinese economy continues to expand. The decoupling of GDP growth from emissions growth—where the former continues to rise while the latter stabilizes or falls—is a critical goal for sustainable development. This trend in the data suggests progress in this area, albeit the challenge of continuing this trend remains.
Sustainability Transition Challenges: The reduction in emissions growth rate amid continued economic growth could signal a transition towards a more sustainable economy. However, though the absolute emission per capita remains low compared to other four countries, the aggregate levels of CO2 emissions remain high, underscoring the ongoing challenge China faces in balancing economic development with environmental sustainability.
3. China CO2 emission Per Capita and GDP Growth (1960-2017)
#esquisser(sus_final_data)sus_final_data_china <- sus_final_data %>%filter(country =="China")ggplot(sus_final_data_china, aes(x = year)) +geom_line(aes(y = co2_per_capita, color ="CO2 Emission Per Capita"), size =1.5) +geom_point(aes(y = gdp_growth *100, color ="GDP Growth"), size =1.5) +geom_smooth(aes(y = gdp_growth *100, color ="GDP Growth Trend Line"), method ="lm") +scale_y_continuous(sec.axis =sec_axis(~./100, name ="GDP Growth (scaled)")) +labs(title ="China CO2 emission Per Capita and GDP Growth (1960-2017)",y ="CO2 Emission Per Capita (million tonnes)", color ="") +theme_minimal() +facet_wrap(vars(country), scales ="free")
`geom_smooth()` using formula = 'y ~ x'
Warning: Removed 1 row containing non-finite outside the scale range
(`stat_smooth()`).
Warning: Removed 1 row containing missing values or values outside the scale range
(`geom_point()`).
Rapid Industrialization and Rising Emissions: Starting from the 1960s, China’s CO2 emissions per capita began to rise steadily, coinciding with periods of industrialization and economic reforms. Particularly post-2000, the graph shows a sharp increase in emissions, aligning with China’s rapid economic expansion and increased reliance on fossil fuels for energy.
Economic Growth Outpacing Emissions Reduction: The GDP growth rate, represented by green dots and a blue trend line, shows consistent positive growth, highlighting China’s economic rise to become a global powerhouse. However, the red line for CO2 emissions per capita also climbs, suggesting that for a significant period, economic growth was strongly linked to increasing CO2 emissions.
Decoupling Trends Emerging: After the early 2000s peak, there is a noticeable flattening and then a slight decrease in the CO2 emissions per capita trend line, while GDP growth continues. Again, this change indicates an emerging decoupling of emissions from economic growth, possibly due to advances in technology, energy efficiency improvements, and a shift toward renewable energy sources.
4. China’s Impact after Joining the WTO
after_wto <- sus_final_data %>%filter(year >=2000& year <=2017)ggplot(after_wto, aes(x = year)) +geom_line(aes(y = co2_per_capita, color ="CO2 Emission Per Capita"), size =1.5) +geom_point(aes(y = gdp_growth *100, color ="GDP Grotwh"), size =1.5) +geom_smooth(aes(y = gdp_growth *100, color ="GDP Growth Trend Line"), method ="lm") +scale_y_continuous(sec.axis =sec_axis(~./100, name ="GDP Growth (scaled)")) +labs(title ="China's Impact after Joining the WTO",y ="CO2 Emission Per Capita (million tonnes)", color ="") +theme_minimal() +facet_wrap(vars(country), scales ="free")
`geom_smooth()` using formula = 'y ~ x'
The rise of globalization since the establishment of the WTO in 1995 has been paralleled by an increase in emissions, as seen in countries like China and India. This suggests that economic growth driven by global trade can lead to higher CO2 emissions per capita, particularly in rapidly industrializing nations. The relatively stable emissions in Japan, despite GDP growth, indicate a balance between economic development and environmental sustainability.
The varied impact of trade on CO2 emissions in different countries underscores the challenges faced by the WTO in harmonizing trade with environmental objectives. While some member countries have effectively managed economic growth without proportional increases in emissions, others still struggle with this balance.
5. CO2 Emissions by Industry Over time
#esquisser(sus_final_data_china)# Reshape the data to a long formatdata_long <- sus_final_data %>%select(year, cement_co2, coal_co2, flaring_co2, gas_co2, land_use_change_co2, oil_co2) %>%pivot_longer(cols =starts_with(c("cement", "coal", "flaring", "gas", "land_use_change", "oil")),names_to ="sector",values_to ="co2" )# Plottingggplot(data_long, aes(x = year, y = co2, fill = sector)) +geom_bar(stat ="identity") +# Stacked bar charttheme_minimal() +labs(title ="CO2 Emissions by Industry Over time",x ="Year",y ="CO2 Emissions (million tonnes)",fill ="Sector" )
The stacked bar chart presents CO2 emissions by industry over time, depicting how different sectors have contributed to overall carbon emissions from the 1960s through to the 2017.
Rising Emissions: There has been a clear and steady increase in total CO2 emissions across all sectors except for land use change induced CO2 emission, indicating a global rise in industrial activities and energy consumption over the past six decades.
Dominant Industries: The coal industry is the largest contributor to CO2 emissions, followed by oil. These two sectors alone account for a significant portion of the emissions, underscoring the traditional reliance on fossil fuels for energy and manufacturing processes.
Minor Sectors: Flaring and cement production contribute relatively less to CO2 emissions, but they are still notable sources. The impact of flaring, which involves burning off excess gas, is particularly significant as it is a direct source of emissions without an energy generation benefit. Cement production is assumed to have a large correlation to the infrastructure projects in China’s development and we’ll also be looking into that. CO2 emissions from land-use change appear to be the smallest portion, but this sector’s impact on emissions is not negligible, especially considering the associated effects on biodiversity and ecological balance.
Policy Implications: The trend emphasizes the urgent need for policy interventions aimed at reducing reliance on coal and oil and it is what’s happening now with the forward movement in renewable energy. Transitioning to cleaner energy sources and improving efficiency in these industries could significantly reduce overall emissions.
6. Coal CO2 Emission and GDP Per Capita in China
# Filter the data for Chinachina_coal <- sus_final_data %>%filter(country =="China") %>%select(year, coal_co2, co2_per_capita, gdppp)ggplot(china_coal, aes(x= year)) +geom_line(aes(y = gdppp, color ="GDP Per Capita")) +geom_point(aes(y = coal_co2, color ="Coal CO2 Emission")) +geom_smooth(aes(y = coal_co2, color ="Coal CO2 Emission Trend Line"), method ="lm") +scale_y_continuous(sec.axis =sec_axis(~ . +10, name ="GDP Per Capita (USD)")) +theme_minimal() +labs(title ="Relationship Between Coal CO2 Emissions and GDP Per Capita in China",x ="GDP Per Capita",y ="Coal CO2 Emissions (million tonnes)",color ="Indicator" )
`geom_smooth()` using formula = 'y ~ x'
The red dots representing coal CO2 emissions display an upward trajectory alongside the increasing GDP per capita, marked by the blue line, suggesting a strong positive correlation between economic growth and coal-based emissions, reflecting China’s industrialization process as well as increased energy demand. At the same time it has got heavy reliance on coal for energy to fuel during its rapid economic development which is not very sustainable and we could see the emission plateau in the most recent data points suggesting a possibe policy as well as structural shift.
The policy shift comes from Xi’s attitude of “Lucid waters and lush mountains are invaluable assets” as well as reaching the carbon peak goal by 2030. And the structural shift comes from China’s determination to lose its label as a dirty sweat factory on the international scene. This cap of coal emission could mark the transition point for China to enter into a tech and innovation driven society.
Overall, the increasing GDP per capita is a positive economic indicator, but the rising CO2 emissions from coal show the environmental costs of such growth. This necessitates innovative solutions and policies that can support continued economic prosperity while reducing environmental impact.
7. Cement CO2 Emission and GDP Per Capita in China
library(ggplot2)library(dplyr)# Filter the data for Chinachina_cement <- sus_final_data %>%filter(country =="China") %>%select(year, cement_co2, co2_per_capita, gdppp) %>%mutate(gdppp_scaled = gdppp /max(gdppp) *max(cement_co2)) ggplot(china_cement, aes(x = year)) +geom_line(aes(y = gdppp_scaled, color ="GDP Per Capita")) +geom_point(aes(y = cement_co2, color ="Cement CO2 Emission")) +geom_smooth(aes(y = cement_co2, color ="Cement CO2 Emission Trend Line"), method ="lm") +scale_y_continuous(sec.axis =sec_axis(~ . *10, name ="GDP Per Capita (USD)"))+scale_color_manual(values =c("GDP Per Capita"="blue", "Cement CO2 Emission"="red", "Cement CO2 Emission Trend Line"="green") ) +theme_minimal() +labs(title ="Relationship Between Cement CO2 Emissions and GDP Per Capita in China",x ="Year",y ="Cement CO2 Emissions (million tonnes) ",color ="Indicator" )
`geom_smooth()` using formula = 'y ~ x'
The steady and significant rise in cement-related CO2 emissions corresponds with China’s extensive infrastructure development. As the GDP per capita increases, signifying economic growth, there is a parallel increase in cement CO2 emissions, likely due to large-scale urbanization and construction projects that demand high volumes of cement.
The red dots indicate a rising trend in emissions from the cement industry, which is among the most carbon-intensive due to the energy consumption and chemical processes involved in cement production. This trend underlines the carbon intensity of China’s development path. And we can also notice that the reddots fit really well with the gdp per capita, it also proceeds the gdp per capita growth for 5-10 years, which could indicate that completion of infrasturcutre projects could lead to later growth and boom.
Similarly, the most recent data points show the beginnings of a plateau in cement CO2 emissions, which could suggest that China is starting to experience the effects of policy shifts aimed at sustainability, such as improved efficiency, adoption of alternative building materials, or a slowdown in the pace of new construction. The observed trend may be indicative of a transition phase in China’s industrial practices, potentially reflecting efforts to align with President Xi Jinping’s environmental philosophy and China’s commitment to peak carbon emissions by 2030, transitioning from high-speed to high-quality growth.
8. Oil CO2 Emission and GDP Per Capita in United States
library(ggplot2)library(dplyr)# Filter the data for Chinaus_oil <- sus_final_data %>%filter(country =="United States") %>%select(year, oil_co2, co2_per_capita, gdppp) %>%mutate(gdppp_scaled = gdppp /max(gdppp) *max(oil_co2)) ggplot(us_oil, aes(x = year)) +geom_line(aes(y = gdppp_scaled, color ="GDP Per Capita")) +geom_point(aes(y = oil_co2, color ="Oil CO2 Emission")) +geom_smooth(aes(y = oil_co2, color ="Oil CO2 Emission Trend Line"), method ="lm") +scale_y_continuous(sec.axis =sec_axis(~ . *20, name ="GDP Per Capita (USD)"))+scale_color_manual(values =c("GDP Per Capita"="blue", "Oil CO2 Emission"="red", "Oil CO2 Emission Trend Line"="green") ) +theme_minimal() +labs(title ="Relationship Between Oil CO2 Emissions and GDP Per Capita in United States",x ="Year",y ="Oil CO2 Emissions (million tonnes) ",color ="Indicator" )
`geom_smooth()` using formula = 'y ~ x'
The U.S. economy has historically been strongly linked to the oil industry, both in terms of production and consumption. The chart indicates an upward trend in both GDP per Capita and Oil CO2 emissions over time in the United States. This suggests that as the US economy has grown, paralleled by a increase in the CO2 emissions from.
The chart may also suggest the influence of energy policies and market practices in the U.S. over time. The periods of steeper increases in oil-related CO2 emissions could correlate with industrial booms, less stringent environmental regulations, or lower fuel prices that incentivize increased oil consumption.
Conclusion
Our examination of CO2 emissions from 1960 to 2017 reveals that emissions have grown significantly, but there are encouraging signs of change. China, after its initial post-WTO surge in emissions, is showing a leveling off, suggesting positive steps towards environmental sustainability. Similarly, in the United States, economic growth is no longer directly tied to increased oil-related emissions, hinting at a more energy-efficient growth pattern.
The shift towards stabilizing emissions demonstrates a global effort to balance economic development with environmental health. The challenge remains to maintain this momentum and enhance our strategies to achieve a sustainable future while continuing to support economic growth.
In conclusion, this research reaffirms the urgent need for continued innovation in policy and practice to achieve the dual goals of economic prosperity and environmental stewardship. It calls for collective action and commitment from all nations to reduce reliance on fossil fuels, invest in clean energy, and embrace a sustainable model of growth. As countries progress towards achieving the targets set by the UNFCCC and the Paris Agreement, this study serves as a reminder of the pressing need to accelerate the transition to a low-carbon global economy.
