Kaya Identity Analysis Across Countries

Introduction

Understanding the factors that drive a country’s total CO₂ emissions is critical for designing effective climate policies. The Kaya identity decomposes emissions into four drivers—population, economic activity (GDP per capita), energy efficiency (energy per unit GDP), and emissions intensity (CO₂ per unit energy)—allowing us to quantify each factor’s relative influence on emissions trends. In this project, we apply the Kaya identity regression across multiple countries to answer:

Which drivers most strongly explain cross-country differences in CO₂ output?
How do population, wealth, energy intensity, and emissions efficiency vary in their impact?
Are there notable outliers that deviate from expected efficiency patterns?

By examining a global sample, we aim to uncover where policy interventions (e.g., improving energy efficiency or shifting fuel mixes) may yield the greatest emissions reductions.

Data Preparation

Load libraries

##Install libraries
library(readr)
library(readxl)
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(tidyr)
library(purrr)
library(ggplot2)
library(coefplot)

Importing Country Codes Data

Total primary energy sources

##  [1] "Coal"          "Peat"          "Crude"         "Oil_shale"    
##  [5] "Oil"           "Natural_gas"   "Nuclear"       "Hydro"        
##  [9] "Geothermal"    "Solar_wind"    "Biofuels"      "Heat_non_spec"
## [13] "Electricity"   "Heat"          "Total"         "Check"

Total final consumption (TJ)

##  [1] "Industry"                "Transport"              
##  [3] "Residential"             "Commerical"             
##  [5] "Agriculture"             "Fishing"                
##  [7] "Not_specified"           "Non-energy"             
##  [9] "Total_final_Consumption" "Check"

CO2 from world bank for comparing

Population (person)

GDP