Data analysis about data driven decision making

1. Global Carbon(CO2) Emission
Regression Modelling
Group 2
Ajay Anand Shukla – 2314003
Jigyasa Dashora – 2314022
Nitin Kapoor – 2314032
Rahatul Ashafeen - 2314036
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2. Table of content
Objective
Key Analysis & Insights
Data Overview
Assumption
Modelling & Conclusion
Possible Decision
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3. Data Selection Basis: Analysing CO2 Emissions Driving Factors
• Factors Considered:
• Industrialization
• Urbanization
• Technology Progress
• Energy Consumption
• Agriculture
• Principle Applied: Parsimony Principle (Chose Conceivable Explanatory Variables)
• Selection Criteria:
§ Target Variable:
§ CO2 Emission (CO2 Em)
§ Explanatory Variables:
§ Energy Consumption (EC)
§ Gross Domestic Product (GDP)
§ Vehicle Production (VP)
§ Population (P)
Real Life Application:
The analysis can help in potentially assist in policy planning for reducing of CO2 emissions.
Analyze & understand relationship between CO2 emissions and other factors for the years 2017 to 2020.
Objective
Electricity
and
Heat
Production
25%
Agriculture
, Forestry
and Other
Land Use
24%
Buildings
6%
Transportation
14%
Industry
21%
Other
Energy
10%
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4. § Identifying main drivers
§ Evolution over time
§ Impact of economic growth
§ Impact of Energy consumption
§ Effective mitigation strategies
Key Analysis & Insights
Unveiling the complexities of carbon emission dynamics
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5. Initial analysis & insights by JMP with graph builder
Data Overview
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6. Factors with high significant are:
§ Population
§ Vehicle production
Assumption
Observations from initial analysis & insights
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7. Insights:
§ EC and VP ➔ High Impact on Carbon Emission
§ P ➔ Moderate Impact
§ GDP ➔ Negligible Impact
Understanding:
§ High EC and increased VP are associated with higher carbon emissions.
§ GDP is dependence on multiple factors hence negligible corelation.
1st Overall Analysis Conclusion
Understanding Factors Impacting Carbon Emissions
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8. EC GDP VP Pop CO2 Em
EC 1.00 0.88 0.93 0.74 0.99
GDP 0.88 1.00 0.75 0.49 0.82
VP 0.93 0.75 1.00 0.74 0.95
Pop 0.74 0.49 0.74 1.00 0.79
CO2 Em 0.99 0.82 0.95 0.79 1.00
Detailed Analysis Conclusion
Approach and Methodology – Scatter Plot
Observation:
§ Same trend for all the 4 years
§ Highest co-relation value ➔ EC & CO2 Em
§ Lowest co-relation value ➔ GDP & Pop
§ Least co-relating factor for CO2 Em ➔ P
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9. Year Intercept
Energy
Consumption
GDP
Vehicle
Production
Population
2020 0.009 <.0001 0.032* .0003 0.041*
2019 0.006 <.0001 <.0001 0.001 0.007
2018 0.007 <.0001 0.71 0.003 0.0005
2017 0.004 <.0001 0.0002 0.0003 0.004
2nd Over Analysis Conclusion
Finding Factors Affected Carbon Emissions
Year:
!
⏞
𝑌 = 𝑏0 + 𝑏1𝑥1 + 𝑏2𝑥2 + 𝑏3𝑥3 + 𝑏4𝑥4
2017:
!
⏞
𝑌 = -92.89368 + 0.260 EC – 6.872e-5 GDP + 51.826494 VP + 0.3741194 P
2018:
!
⏞
𝒀 = −131.23 + 0.206 EC + 3.528e−6 GDP + 61.88154 VP + 0.6347112 P
2019:
!
⏞
𝒀 = −92.46676 + 0.267 EC − 7.212e−5 GDP + 48.165042 VP + 0.3584881 P
2020:
!
⏞
𝒀 = −87.00767 + 0.256 EC − 6.825e−5 GDP + 59.001293 VP + 0.3595516 P
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10. 2017 2019 2020
2018
Detailed Analysis Conclusion – EC & VP
Approach and Methodology – Fit Regression Model
CO2
Emission
Leverage
Residuals
Energy Consumption Leverage, P<.0001 (2017-20)
CO2
Emission
Leverage
Residuals
Vehicle Production (Mn) Leverage, P=0.0003 (2017); 0.0031 (2018); 0.0013 (2019) & .0003 (2020)
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11. Regression Statistics
Particulars 2017 2018 2019 2020
R Square 0.997 0.994 0.997 0.997
R Square Adj. 0.996 0.992 0.996 0.997
Root Mean Square Foot 122 182 126 116
Observations 25 25 25 25
§ Accuracy of model:
§ Root mean square
§ Effectiveness of model
§ R Square indicate overall
Detailed Analysis Conclusion
Approach and Methodology – Fit Regression Model
Particulars DF
Year 2017 2018 2019 2020
Model 4 4 4 4
Error 20 20 20 20
Cumulative Total 24 24 24 24
Particulars Sum of Squares
Year 2017 2018 2019 2020
Model 1E+08 1E+08 1E+08 1E+08
Error 3E+05 7E+05 3E+05 3E+05
Cumulative Total 1E+08 1E+08 1E+08 1E+08
Particulars Mean Square
Year 2017 2018 2019 2020
Model 25228272 26347032 26885295 26243264
Error 14936 33229 15880 13464
Cumulative Total
Particulars F Square
Year 2017 2018 2019 2020
Model 1689 793 1693 1949
Error Prob > F Prob > F Prob > F Prob > F
Cumulative Total <.0001* <.0001* <.0001* <.0001*
Analysis of Variance
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12. Detailed Analysis Conclusion
2017 2018
2019 2020
Approach and Methodology - Prediction Profiler
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13. § Observation:
Lower GDP per capita often correlates with higher CO2 emissions, potentially fueling economic growth.
§ Research Insight:
CO2 emissions tend to decrease with a country's economic development.
§ Trend:
Rapidly growing middle-to-lower income countries emit more CO2 during initial expansion.
§ Conclusion:
The study reveals an inverse relationship between GDP and CO2 emissions, suggesting that high GDP alone doesn't drive emissions.
3rd Overall Analysis Conclusion
Relationship between GDP & CO2 Em
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14. Observation:
§ At individual level, GDP and CO2 show a significant positive relationship.
§ Accounting other factors (EC, VP &P) relationship becomes negative.
Detailed Analysis Conclusion
2017 2019 2020
2018
CO2
Emission
GDP
Approach and Methodology – Fit Regression Model
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15. Possible Decision
§ Key Insight:
Study identifies energy consumption as a major CO2 emission contributor
§ Decisions:
§ Promote renewable energy sources to reduce emissions
§ Encourage green fleet options to tackle CO2 emissions from vehicles
§ Focus on economic development for more resources to invest in emission-reducing technology
§ Consideration:
While population contributes, there are currently no direct CO2 reduction measures linked to population
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Q&A

17. THANK YOU!
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