1 Introduction

1.1 Background

In an increasing energy demanding society, concerns about climate change and energy security are shifting the global energy matrix towards a low-carbon economy. There are some scientists indicate the pace of global warming and climate change is directly proportional to the concentration of carbon dioxide emissions. The motivation to research this topic is inspired by global climate change and CO2 emissions. As the temperature and sea levels remain risen over years in Malaysia, with a surface mean temperature increase of 0.14°C-0.25°C per decade and the annual rise of sea level in coastal Malaysia is approximately 3mm per year. The fast growth in population and economies at a faster pace than in previous generations contributes to more CO2emissions, hastening sea level and temperature rise.

1.2 Research Question

Q1: Do the CO2 emission lead to climate change?

Q2: Does it result from the growth of population and economic activities?

1.3 Non-Technical Explanation

There are 4 variables that might have a correlation with the CO2 emissions. These variables were measured at the categorical levels such as population and GDP; Affected by CO2 emissions is the rising sea level in Malaysia. This data set with interval and ratio variables is fit to use linear regression, by assessing the interaction terms.

There are two regression models that will be developed by including five explanatory variables to understand the emission value of CO2 in Malaysia. One simple model that involved two variables(set Annual CO2 emissions as an independent variable while the surface mean of temperature used to be a dependent variable) is used to explain whether CO2 emissions affect temperature rise, and another multiple-regression model that consists of three variables(Annual CO2 emissions, the number of populations, Annual GDP, in Malaysia) is used to explain whether economic activities and the number of populations affect CO2 emissions in Malaysia.

Linear regression helps us to understand the linear relationship between the dependent and independent variables by creating scatter plots for good visualization to analyze. A simple inear regression is used to show a relationship between two variables. While a multiple linear regression is used to describe data and to explain the relationship between one dependent variable and two or more independent variables. At the center of the multiple linear regression analysis lies the task of fitting a single line through the scatter plot, as can be seen in Model Visualization. If there are any significant outliers, the regression analysis will increase the spread of the estimated coefficient and this reduces the fit of the regression equation. Linear regression eliminates independent variables that do not affect the outcome of interest in the analysis, referring to the value of standard deviation and outliers.

1.4 The below visualized datasets used for our analysis.

1.4.1 Figure 1 Sea Levels of coastal Malaysia 1890-2020

data1<-read.csv("Malaysia_air_pollution.csv")

# Line Chart
ggplot(data1, aes(x = Year, y = Sea_Level, fill = Annual_CO2_emissions)) + 
geom_line(size = 1)

1.4.2 Figure 2 Annual GDP in Malaysia 1890-2020

# Bar Chart
ggplot(data1, aes(x = Year, y = GDP, fill = GDP)) + 
  geom_bar(stat = "identity", position = "dodge")

1.4.3 Figure 3 Annual CO2 emissions in Malaysia 1890-2020

# Point Chart
ggplot(data1, aes(x = Year, y = Annual_CO2_emissions, fill = Annual_CO2_emissions)) +
  geom_point(shape = 21, size = 2)

1.4.4 Figure 4 Population in Malaysia 1890-2020

# Line Chart
ggplot(data1, aes(x = Year, y = Population, fill = Annual_CO2_emissions)) + 
geom_line(size = 1)

1.4.5 Figure 5 Annual Mean of Tamperatures in Malaysia 1890-2020

# Line Chart
ggplot(data1, aes(x = Year, y = Annual_Mean_Tamperature, fill = Annual_CO2_emissions)) + 
geom_line(size = 1)

2 Model Development and Linear regression

2.1 Model 1 Simple linear Regression

Below shows the regression coefficients for the independent variable including:

Annual CO2 emissions 1890-2020

2.1.1 Performing Linear Regression

model1 <- data1 %>% 
  select(Annual_CO2_emissions)
glimpse(model1)

## Rows: 130
## Columns: 1
## $ Annual_CO2_emissions <int> 7328, 18320, 47632, 10992, 47632, 109920, 124576,~

2.1.2 Summary of Coefficients

data1<-read.csv("Malaysia_air_pollution.csv")
colnames(data1)[4]<-"Annual_Mean_Tamperature"

# model 1 (CO2 to temperature)
model1<-lm(Annual_Mean_Tamperature~Annual_CO2_emissions,data=data1)

Using lm function.

equatiomatic::extract_eq(model1, use_coefs = TRUE)

\[ \operatorname{\widehat{Annual\_Mean\_Tamperature}} = 25.2 + 0(\operatorname{Annual\_CO2\_emissions}) \]

The simple linear regression equation as shown above.

summary(model1)

## 
## Call:
## lm(formula = Annual_Mean_Tamperature ~ Annual_CO2_emissions, 
##     data = data1)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.47409 -0.11497  0.01166  0.10385  0.76584 
## 
## Coefficients:
##                       Estimate Std. Error t value Pr(>|t|)    
## (Intercept)          2.520e+01  2.048e-02 1230.26   <2e-16 ***
## Annual_CO2_emissions 4.543e-09  2.265e-10   20.05   <2e-16 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.2 on 128 degrees of freedom
## Multiple R-squared:  0.7585, Adjusted R-squared:  0.7567 
## F-statistic: 402.1 on 1 and 128 DF,  p-value: < 2.2e-16

Table 1

options(scipen = 999)
tab.model1 <- tidy(model1, conf.int = TRUE)
kable(tab.model1) %>%
  kable_styling()

term	estimate	std.error	statistic	p.value	conf.low	conf.high
(Intercept)	25.19941	0.0204831	1230.25494	0	25.15888	25.23994
Annual_CO2_emissions	0.00000	0.0000000	20.05281	0	0.00000	0.00000

modelsummary(model1)

	Model 1
(Intercept)	25.199
	(0.020)
Annual_CO2_emissions	0.000
	(0.000)
Num.Obs.	130
R2	0.759
R2 Adj.	0.757
AIC	45.6
BIC	37.0
Log.Lik.	25.793
F	402.115
RMSE	0.20

model1 %>%
  tbl_regression()

Characteristic	Beta	95% CI¹	p-value
Annual_CO2_emissions	0.00	0.00, 0.00	<0.001
¹ CI = Confidence Interval

Based on table 1, CO2 emissions have an impact on temperatures with a high estimated value and coefficient. As shown in the linear regression, our conjecture is confirmed by the data that the temperature keeps rising with the increasing CO2 emissions with an accuracy of 76% of the equation, However, The p-value is the evidence against a null hypothesis. In this case, A p-value less than 0.001 will under normal circumstances mean that there is substantial evidence against the null hypothesis.

2.2 Model 2 Multiple linear Regression

Below shows the regression coefficients for the independent variable including:

GDP = Annual GDP in Malaysia 1890-2020
Population = Malaysia population 1890-2020

2.2.1 Performing Linear Regression

model2 <- data1 %>% 
  select(GDP, Population)
glimpse(model2)

## Rows: 130
## Columns: 2
## $ GDP        <dbl> 844000000, 806488420, 770196441, 745396116, 728624703, 6958~
## $ Population <int> 1533320, 1585304, 1639062, 1694657, 1752135, 1811562, 18730~

2.2.2 Summary of Coefficients

# model2 (GDP, population to CO2)
model2<-lm(Annual_CO2_emissions~GDP+Population,data=data1)
equatiomatic::extract_eq(model2, use_coefs = TRUE)

\[ \operatorname{\widehat{Annual\_CO2\_emissions}} = -6278387.86 + 0(\operatorname{GDP}) + 1.05(\operatorname{Population}) \]

The multiple linear regression equation as shown above.

summary(model2)

## 
## Call:
## lm(formula = Annual_CO2_emissions ~ GDP + Population, data = data1)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -29816986  -4118924    359663   3152055  24121958 
## 
## Coefficients:
##                       Estimate         Std. Error t value             Pr(>|t|)
## (Intercept) -6278387.857179145  1376505.948151349  -4.561          0.000011973
## GDP                0.000344264        0.000009479  36.320 < 0.0000000000000002
## Population         1.050876145        0.201436588   5.217          0.000000733
##                
## (Intercept) ***
## GDP         ***
## Population  ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 7509000 on 125 degrees of freedom
##   (因为不存在，2个观察量被删除了)
## Multiple R-squared:  0.9895, Adjusted R-squared:  0.9894 
## F-statistic:  5903 on 2 and 125 DF,  p-value: < 0.00000000000000022

Table 2

tidy(model2)

term	estimate	std.error	statistic	p.value
(Intercept)	-6.28e+06	1.38e+06	-4.56	1.2e-05
GDP	0.000344	9.48e-06	36.3	2.84e-68
Population	1.05	0.201	5.22	7.33e-07

options(scipen = 999)
tab.model2 <- tidy(model2, conf.int = TRUE)
kable(tab.model2) %>%
  kable_styling()

term	estimate	std.error	statistic	p.value	conf.low	conf.high
(Intercept)	-6278387.8571791	1376505.9481513	-4.561105	0.0000120	-9002663.9659307	-3554111.748428
GDP	0.0003443	0.0000095	36.320330	0.0000000	0.0003255	0.000363
Population	1.0508761	0.2014366	5.216908	0.0000007	0.6522081	1.449544

modelsummary(model2)

	Model 1
(Intercept)	6278387.857
	(1376505.948)
GDP	0.000
	(0.000)
Population	1.051
	(0.201)
Num.Obs.	128
R2	0.990
R2 Adj.	0.989
AIC	4421.1
BIC	4432.5
Log.Lik.	2206.550
F	5902.821
RMSE	7508825.22

model2 %>%
  tbl_regression()

Characteristic	Beta	95% CI¹	p-value
GDP	0.00	0.00, 0.00	<0.001
Population	1.1	0.65, 1.4	<0.001
¹ CI = Confidence Interval

Table 2 indicates that GDP and population have high estimates and coefficients for the effect of CO2, as shown in the linear regression. The generated equation between CO2 emissions and GDP, population with an accuracy of 99%; however, the p-values are still less than 0.001, implying that there is substantial evidence against the original hypothesis.

2.3 Model Assessment

2.3.1 Model 1

fit_A <- augment(model1)
fit_A

Annual_Mean_Tamperature	Annual_CO2_emissions	.fitted	.resid	.hat	.sigma	.cooksd	.std.resid
25.1	7328	25.2	-0.0694	0.0105	0.201	0.000646	-0.349
25.1	18320	25.2	-0.0795	0.0105	0.201	0.000847	-0.4
25.1	47632	25.2	-0.13	0.0105	0.2	0.00225	-0.652
25	10992	25.2	-0.189	0.0105	0.2	0.00481	-0.952
25.1	47632	25.2	-0.0996	0.0105	0.201	0.00133	-0.501
25.1	109920	25.2	-0.0799	0.0105	0.201	0.000855	-0.402
25	124576	25.2	-0.16	0.0105	0.2	0.00342	-0.804
25.1	91600	25.2	-0.14	0.0105	0.2	0.00262	-0.703
25.1	128240	25.2	-0.13	0.0105	0.2	0.00226	-0.654
25.1	120912	25.2	-0.14	0.0105	0.2	0.00262	-0.704
25.3	58624	25.2	0.14	0.0105	0.2	0.00264	0.705
25.4	47632	25.2	0.21	0.0105	0.2	0.00593	1.06
25.3	51296	25.2	0.14	0.0105	0.2	0.00264	0.706
25.5	51296	25.2	0.29	0.0105	0.199	0.0113	1.46
25.5	32976	25.2	0.26	0.0105	0.199	0.00908	1.31
25.3	40304	25.2	0.0604	0.0105	0.201	0.000489	0.304
25.1	36640	25.2	-0.0796	0.0105	0.201	0.000848	-0.4
25	87936	25.2	-0.18	0.0105	0.2	0.00433	-0.904
25.4	91600	25.2	0.24	0.0105	0.2	0.00772	1.21
25.4	168544	25.2	0.16	0.0105	0.2	0.00342	0.803
25.2	234496	25.2	0.0195	0.0105	0.201	5.09e-05	0.0981
25.3	18320	25.2	0.0705	0.0105	0.201	0.000666	0.354
25.2	62288	25.2	0.0103	0.0105	0.201	1.42e-05	0.0518
25.2	146560	25.2	0.0199	0.0105	0.201	5.31e-05	0.1
25.2	205184	25.2	-0.0203	0.0105	0.201	5.53e-05	-0.102
25.4	549600	25.2	0.148	0.0104	0.2	0.00292	0.744
25.3	652192	25.2	0.0576	0.0104	0.201	0.000442	0.29
25.3	670512	25.2	0.0675	0.0104	0.201	0.000606	0.34
25.1	769440	25.2	-0.0729	0.0104	0.201	0.000706	-0.367
25.4	1110192	25.2	0.146	0.0104	0.2	0.0028	0.732
25.5	1414304	25.2	0.254	0.0103	0.199	0.00852	1.28
25.2	2004208	25.2	-0.0485	0.0103	0.201	0.000308	-0.244
25.2	2583120	25.2	-0.0111	0.0102	0.201	1.62e-05	-0.056
25.1	2506176	25.2	-0.131	0.0102	0.2	0.00223	-0.657
25.3	2964176	25.2	0.107	0.0101	0.201	0.00149	0.538
25.3	3400192	25.2	0.0851	0.0101	0.201	0.000934	0.428
25.3	3378208	25.2	0.125	0.0101	0.2	0.00202	0.629
25.2	3806896	25.2	0.0333	0.0101	0.201	0.000142	0.167
25.2	4114672	25.2	-0.00811	0.01	0.201	8.4e-06	-0.0407
25.4	3671328	25.2	0.184	0.0101	0.2	0.00435	0.924
25.9	2707696	25.2	0.678	0.0102	0.191	0.0598	3.41
25.1	1795360	25.2	-0.0776	0.0103	0.201	0.00079	-0.39
25.1	1590176	25.2	-0.107	0.0103	0.201	0.0015	-0.536
25.1	1733072	25.2	-0.0773	0.0103	0.201	0.000784	-0.388
25.1	1806352	25.2	-0.0776	0.0103	0.201	0.000791	-0.39
25.2	2048176	25.2	-0.0487	0.0103	0.201	0.000311	-0.245
25.2	2344960	25.2	0.0199	0.0102	0.201	5.18e-05	0.1
25.3	1912608	25.2	0.132	0.0103	0.2	0.00228	0.663
25.3	1703760	25.2	0.0928	0.0103	0.201	0.00113	0.467
25.3	2568464	25.2	0.129	0.0102	0.2	0.00216	0.648
25.3	2183744	25.2	0.0507	0.0102	0.201	0.000335	0.255
25.2	659520	25.2	0.0276	0.0104	0.201	0.000101	0.139
25.3	1319040	25.2	0.0946	0.0103	0.201	0.00118	0.476
25.1	1102864	25.2	-0.0744	0.0104	0.201	0.000733	-0.374
25	611888	25.2	-0.172	0.0104	0.2	0.00394	-0.866
24.9	604560	25.2	-0.332	0.0104	0.199	0.0147	-1.67
25.3	685168	25.2	0.0675	0.0104	0.201	0.000605	0.339
25.4	1190262	25.2	0.225	0.0104	0.2	0.0067	1.13
25.2	1220112	25.2	-0.015	0.0103	0.201	2.96e-05	-0.0752
25.2	3655348	25.2	0.024	0.0101	0.201	7.39e-05	0.121
25	5220367	25.2	-0.263	0.0099	0.199	0.00874	-1.32
24.9	6370886	25.2	-0.378	0.00978	0.198	0.0179	-1.9
24.9	6498899	25.2	-0.319	0.00977	0.199	0.0127	-1.6
24.9	7004164	25.2	-0.301	0.00972	0.199	0.0112	-1.51
24.8	7429726	25.2	-0.423	0.00967	0.197	0.0221	-2.13
25.1	7562529	25.2	-0.134	0.00966	0.2	0.0022	-0.672
24.8	3513331	25.2	-0.375	0.0101	0.198	0.0181	-1.89
24.9	3832099	25.2	-0.337	0.0101	0.198	0.0145	-1.69
25.1	3183244	25.2	-0.0939	0.0101	0.201	0.00114	-0.472
25	4201450	25.2	-0.188	0.01	0.2	0.00454	-0.947
24.9	4684919	25.2	-0.341	0.00996	0.198	0.0147	-1.71
25.2	4776549	25.2	0.0289	0.00995	0.201	0.000106	0.145
25.3	5827968	25.2	0.104	0.00984	0.201	0.00136	0.523
24.9	7399409	25.2	-0.283	0.00968	0.199	0.00988	-1.42
24.9	8380229	25.2	-0.287	0.00958	0.199	0.0101	-1.44
24.8	9834393	25.2	-0.474	0.00944	0.196	0.027	-2.38
25	10127602	25.2	-0.215	0.00941	0.2	0.00556	-1.08
25.2	10530226	25.2	-0.0873	0.00937	0.201	0.000909	-0.438
25.3	9137758	25.2	0.0191	0.0095	0.201	4.41e-05	0.0958
25.2	14585889	25.3	-0.0557	0.00902	0.201	0.000356	-0.28
25.4	16662594	25.3	0.0749	0.00885	0.201	0.000632	0.376
25.4	17900625	25.3	0.0693	0.00876	0.201	0.000535	0.348
25.6	17505127	25.3	0.371	0.00879	0.198	0.0154	1.86
25	19044679	25.3	-0.326	0.00867	0.199	0.0117	-1.64
25.2	19445158	25.3	-0.118	0.00865	0.2	0.00153	-0.591
25.2	23897643	25.3	-0.058	0.00836	0.201	0.000357	-0.291
25.6	22620840	25.3	0.318	0.00844	0.199	0.0108	1.6
25.4	23260444	25.3	0.145	0.0084	0.2	0.00224	0.728
25.2	27305937	25.3	-0.0835	0.00818	0.201	0.000724	-0.419
25.6	28032597	25.3	0.293	0.00814	0.199	0.0089	1.47
25.6	30877661	25.3	0.26	0.00801	0.199	0.0069	1.31
25.5	30641940	25.3	0.171	0.00802	0.2	0.00299	0.861
25.4	38048307	25.4	0.0477	0.00779	0.201	0.000225	0.24
25.5	34794433	25.4	0.153	0.00787	0.2	0.00233	0.766
25.5	36329771	25.4	0.176	0.00783	0.2	0.00307	0.881
25.4	40101885	25.4	0.0684	0.00775	0.201	0.000461	0.343
25.8	40877684	25.4	0.385	0.00774	0.198	0.0146	1.93
26.2	42869204	25.4	0.766	0.00771	0.189	0.0574	3.84
25.5	50083870	25.4	0.103	0.00771	0.201	0.00104	0.517
25.7	54269364	25.4	0.214	0.00777	0.2	0.00452	1.07
25.8	65785134	25.5	0.262	0.00816	0.199	0.0071	1.31
25.9	72961016	25.5	0.409	0.00858	0.197	0.0183	2.05
25.7	86718637	25.6	0.147	0.00975	0.2	0.00267	0.737
25.7	90630988	25.6	0.0789	0.0102	0.201	0.000807	0.396
25.7	113933664	25.7	0.013	0.0135	0.201	2.93e-05	0.0655
25.6	113441942	25.7	-0.0748	0.0134	0.201	0.000963	-0.376
25.6	122084586	25.8	-0.184	0.015	0.2	0.00654	-0.927
25.4	114237500	25.7	-0.338	0.0135	0.198	0.0199	-1.7
25.7	108291440	25.7	-0.0114	0.0126	0.201	2.08e-05	-0.0572
25.8	126203049	25.8	0.0273	0.0158	0.201	0.000152	0.137
25.6	133988165	25.8	-0.228	0.0175	0.2	0.0118	-1.15
25.8	135118208	25.8	-0.0432	0.0177	0.201	0.000429	-0.218
25.9	156521332	25.9	-0.0405	0.0232	0.201	0.000497	-0.205
25.8	171102004	26	-0.197	0.0276	0.2	0.0141	-0.997
26	181332800	26	-0.0132	0.031	0.201	7.16e-05	-0.0669
26.3	178785308	26	0.288	0.0301	0.199	0.0333	1.46
25.9	182455363	26	-0.178	0.0313	0.2	0.0133	-0.906
25.9	201985181	26.1	-0.227	0.0386	0.2	0.0269	-1.16
26.2	200712944	26.1	0.0588	0.0381	0.201	0.00178	0.3
26.1	215935154	26.2	-0.13	0.0444	0.2	0.0103	-0.667
26.2	216993527	26.2	-0.0282	0.0449	0.201	0.00049	-0.144
26.2	215213034	26.2	0.0201	0.0441	0.201	0.000243	0.103
26.2	242487438	26.3	-0.0637	0.0569	0.201	0.00324	-0.328
26.3	244832567	26.3	-0.0341	0.0581	0.201	0.000954	-0.176
26.3	231843940	26.3	0.065	0.0517	0.201	0.00304	0.334
26.4	250560642	26.3	0.0202	0.061	0.201	0.000352	0.104
26.4	250561320	26.3	0.0604	0.061	0.201	0.00315	0.311
26.4	272229353	26.4	0.00209	0.073	0.201	4.64e-06	0.0109
26.5	278659255	26.5	0.0131	0.0767	0.201	0.000192	0.0679
26.5	272607434	26.4	0.0807	0.0732	0.201	0.00694	0.419

2.3.1.1 Visualization

ggplot(data1,aes(x=Annual_CO2_emissions,y=Annual_Mean_Tamperature))+
  geom_point()+
  stat_smooth(method="lm",col="red")+
  xlab("CO2 Emissions")+
  ylab("Temperature")+
  ggtitle("Line model visulizing model1")+
  theme(plot.title = element_text(hjust = 0.5))

2.3.2 Model 2

fit_B <- augment(model2)
fit_B

Annual_CO2_emissions	GDP	Population	.fitted	.resid	.hat	.sigma	.cooksd	.std.resid
7328	8.44e+08	1533320	-4.38e+06	4.38e+06	0.023	7.53e+06	0.00274	0.591
18320	8.06e+08	1585304	-4.33e+06	4.35e+06	0.0227	7.53e+06	0.00266	0.586
47632	7.7e+08	1639062	-4.29e+06	4.34e+06	0.0224	7.53e+06	0.0026	0.584
10992	7.45e+08	1694657	-4.24e+06	4.25e+06	0.022	7.53e+06	0.00246	0.573
47632	7.29e+08	1752135	-4.19e+06	4.23e+06	0.0217	7.53e+06	0.0024	0.57
109920	6.96e+08	1811562	-4.14e+06	4.25e+06	0.0214	7.53e+06	0.00238	0.571
124576	6.73e+08	1873003	-4.08e+06	4.2e+06	0.021	7.53e+06	0.00229	0.566
91600	6.58e+08	1936527	-4.02e+06	4.11e+06	0.0207	7.53e+06	0.00215	0.553
128240	6.29e+08	2002203	-3.96e+06	4.09e+06	0.0203	7.53e+06	0.00209	0.55
120912	1.83e+09	2066448	-3.48e+06	3.6e+06	0.0202	7.53e+06	0.00161	0.484
58624	1.83e+09	2129124	-3.41e+06	3.47e+06	0.0199	7.53e+06	0.00147	0.467
47632	2.17e+09	2190086	-3.23e+06	3.28e+06	0.0196	7.53e+06	0.0013	0.441
51296	2.39e+09	2249181	-3.09e+06	3.14e+06	0.0194	7.53e+06	0.00118	0.423
51296	2.12e+09	2306249	-3.13e+06	3.18e+06	0.019	7.53e+06	0.00118	0.427
32976	2.03e+09	2364764	-3.09e+06	3.13e+06	0.0187	7.53e+06	0.00113	0.42
40304	2.19e+09	2424761	-2.98e+06	3.02e+06	0.0185	7.53e+06	0.00103	0.406
36640	2.07e+09	2486279	-2.95e+06	2.99e+06	0.0182	7.53e+06	0.000996	0.402
87936	2.28e+09	2549355	-2.82e+06	2.9e+06	0.0179	7.53e+06	0.000925	0.39
91600	2.7e+09	2614029	-2.6e+06	2.69e+06	0.0177	7.54e+06	0.000785	0.362
168544	2.59e+09	2677183	-2.57e+06	2.74e+06	0.0174	7.53e+06	0.000799	0.368
234496	3.11e+09	2738718	-2.33e+06	2.57e+06	0.0172	7.54e+06	0.000692	0.345
18320	3.31e+09	2798533	-2.2e+06	2.22e+06	0.0169	7.54e+06	0.000509	0.298
62288	3.97e+09	2856522	-1.91e+06	1.97e+06	0.0168	7.54e+06	0.000398	0.265
146560	4.54e+09	2912576	-1.65e+06	1.8e+06	0.0166	7.54e+06	0.000329	0.242
205184	4.94e+09	2969729	-1.46e+06	1.66e+06	0.0164	7.54e+06	0.000277	0.223
549600	4.54e+09	3028000	-1.53e+06	2.08e+06	0.0161	7.54e+06	0.000427	0.28
652192	5.73e+09	3087412	-1.06e+06	1.71e+06	0.0161	7.54e+06	0.000287	0.23
670512	6.11e+09	3147988	-8.67e+05	1.54e+06	0.0159	7.54e+06	0.000229	0.206
769440	6.52e+09	3212623	-6.58e+05	1.43e+06	0.0157	7.54e+06	0.000195	0.192
1110192	6.33e+09	3279664	-6.52e+05	1.76e+06	0.0154	7.54e+06	0.000291	0.236
1414304	7.86e+09	3349182	-5.3e+04	1.47e+06	0.0153	7.54e+06	0.000201	0.197
2004208	8.89e+09	3421253	3.78e+05	1.63e+06	0.0151	7.54e+06	0.000244	0.218
2583120	7.75e+09	3495955	6.5e+04	2.52e+06	0.0147	7.54e+06	0.000569	0.338
2506176	7.9e+09	3573367	1.96e+05	2.31e+06	0.0145	7.54e+06	0.00047	0.31
2964176	6.84e+09	3652493	-8.42e+04	3.05e+06	0.0141	7.53e+06	0.000797	0.409
3400192	8.8e+09	3733371	6.74e+05	2.73e+06	0.014	7.54e+06	0.000634	0.366
3378208	8.46e+09	3816040	6.44e+05	2.73e+06	0.0137	7.53e+06	0.000624	0.367
3806896	1.07e+10	3900540	1.51e+06	2.3e+06	0.0137	7.54e+06	0.000439	0.308
4114672	1.39e+10	3986910	2.7e+06	1.42e+06	0.0137	7.54e+06	0.000167	0.19
3671328	1.39e+10	4074267	2.79e+06	8.86e+05	0.0134	7.54e+06	6.39e-05	0.119
2707696	1.39e+10	4162611	2.87e+06	-1.62e+05	0.0131	7.54e+06	2.08e-06	-0.0217
1795360	1.29e+10	4251945	2.62e+06	-8.2e+05	0.0128	7.54e+06	5.22e-05	-0.11
1590176	1.31e+10	4342271	2.8e+06	-1.21e+06	0.0126	7.54e+06	0.000111	-0.162
1733072	1.34e+10	4433590	3.01e+06	-1.28e+06	0.0123	7.54e+06	0.000122	-0.171
1806352	1.31e+10	4526829	3e+06	-1.2e+06	0.0121	7.54e+06	0.000105	-0.161
2048176	1.45e+10	4622029	3.57e+06	-1.53e+06	0.0119	7.54e+06	0.000168	-0.204
2344960	1.61e+10	4719231	4.23e+06	-1.89e+06	0.0118	7.54e+06	0.000254	-0.253
1912608	1.31e+10	4818477	3.29e+06	-1.38e+06	0.0114	7.54e+06	0.000132	-0.185
1703760	1.36e+10	4919810	3.59e+06	-1.88e+06	0.0113	7.54e+06	0.000241	-0.252
2568464	1.11e+10	5019176	2.81e+06	-2.38e+05	0.011	7.54e+06	3.78e-06	-0.0319
2183744	1.1e+10	5116462	2.87e+06	-6.87e+05	0.0109	7.54e+06	3.1e-05	-0.092
659520	1.49e+10	5211554	4.33e+06	-3.67e+06	0.0108	7.53e+06	0.00088	-0.492
1319040	1.49e+10	5304334	4.43e+06	-3.11e+06	0.0107	7.53e+06	0.000623	-0.417
1102864	1.49e+10	5394679	4.53e+06	-3.42e+06	0.0105	7.53e+06	0.000745	-0.458
611888	1.49e+10	5486564	4.62e+06	-4.01e+06	0.0104	7.53e+06	0.00101	-0.537
604560	1.49e+10	5580013	4.72e+06	-4.12e+06	0.0103	7.53e+06	0.00105	-0.551
685168	9.86e+09	5675054	3.08e+06	-2.39e+06	0.0103	7.54e+06	0.000356	-0.321
1190262	1.12e+10	5771714	3.64e+06	-2.45e+06	0.0102	7.54e+06	0.000369	-0.328
1220112	1.48e+10	5870020	4.98e+06	-3.76e+06	0.0101	7.53e+06	0.00086	-0.503
3655348	1.6e+10	5982554	5.51e+06	-1.86e+06	0.00999	7.54e+06	0.000208	-0.249
5220367	1.51e+10	6109902	5.34e+06	-1.22e+05	0.00997	7.54e+06	8.98e-07	-0.0164
6370886	1.58e+10	6271228	5.76e+06	6.11e+05	0.00991	7.54e+06	2.23e-05	0.0818
6498899	1.59e+10	6449609	5.97e+06	5.25e+05	0.00991	7.54e+06	1.65e-05	0.0703
7004164	1.69e+10	6639418	6.52e+06	4.86e+05	0.00991	7.54e+06	1.41e-05	0.065
7429726	1.7e+10	6836639	6.76e+06	6.66e+05	0.01	7.54e+06	2.68e-05	0.0892
7562529	1.8e+10	7038910	7.33e+06	2.33e+05	0.0101	7.54e+06	3.31e-06	0.0313
3513331	1.79e+10	7245680	7.51e+06	-4e+06	0.0103	7.53e+06	0.000995	-0.536
3832099	1.79e+10	7458015	7.73e+06	-3.9e+06	0.0106	7.53e+06	0.000973	-0.523
3183244	1.92e+10	7678381	8.39e+06	-5.2e+06	0.0108	7.52e+06	0.00177	-0.697
4201450	2.06e+10	7910188	9.11e+06	-4.91e+06	0.0111	7.53e+06	0.00161	-0.658
4684919	2.2e+10	8156342	9.86e+06	-5.18e+06	0.0114	7.52e+06	0.00185	-0.693
4776549	2.32e+10	8417821	1.06e+07	-5.79e+06	0.0118	7.52e+06	0.0024	-0.776
5827968	2.43e+10	8692337	1.12e+07	-5.41e+06	0.0124	7.52e+06	0.00219	-0.724
7399409	2.59e+10	8973791	1.21e+07	-4.66e+06	0.013	7.53e+06	0.00171	-0.625
8380229	2.77e+10	9253827	1.3e+07	-4.62e+06	0.0136	7.53e+06	0.00176	-0.619
9834393	2.91e+10	9526558	1.38e+07	-3.93e+06	0.0143	7.53e+06	0.00134	-0.527
10127602	2.96e+10	9790083	1.42e+07	-4.08e+06	0.0152	7.53e+06	0.00154	-0.548
10530226	3.22e+10	10046321	1.54e+07	-4.84e+06	0.0157	7.53e+06	0.00225	-0.65
9137758	3.41e+10	10297983	1.63e+07	-7.14e+06	0.0164	7.51e+06	0.00511	-0.958
14585889	3.62e+10	10549395	1.73e+07	-2.67e+06	0.0171	7.54e+06	0.000745	-0.359
16662594	3.88e+10	10804131	1.84e+07	-1.78e+06	0.0177	7.54e+06	0.000342	-0.239
17900625	4.17e+10	11062434	1.97e+07	-1.82e+06	0.0182	7.54e+06	0.00037	-0.245
17505127	4.78e+10	11324277	2.21e+07	-4.57e+06	0.018	7.53e+06	0.00231	-0.614
19044679	5.14e+10	11592638	2.36e+07	-4.54e+06	0.0185	7.53e+06	0.00235	-0.61
19445158	5.18e+10	11871102	2.4e+07	-4.58e+06	0.02	7.53e+06	0.00258	-0.616
23897643	5.82e+10	12162189	2.65e+07	-2.65e+06	0.0199	7.54e+06	0.000858	-0.356
22620840	6.3e+10	12468688	2.85e+07	-5.89e+06	0.0203	7.52e+06	0.00434	-0.792
23260444	6.85e+10	12790313	3.07e+07	-7.49e+06	0.0207	7.51e+06	0.00715	-1.01
27305937	7.41e+10	13122833	3.3e+07	-5.71e+06	0.0211	7.52e+06	0.00425	-0.768
28032597	8.02e+10	13460035	3.55e+07	-7.45e+06	0.0215	7.51e+06	0.00737	-1
30877661	8.59e+10	13798094	3.78e+07	-6.92e+06	0.022	7.51e+06	0.0065	-0.932
30641940	9.1e+10	14134060	3.99e+07	-9.27e+06	0.0226	7.49e+06	0.012	-1.25
38048307	9.66e+10	14471215	4.22e+07	-4.13e+06	0.0232	7.53e+06	0.00245	-0.557
34794433	1.04e+11	14819430	4.51e+07	-1.03e+07	0.0233	7.48e+06	0.0154	-1.39
36329771	1.03e+11	15192300	4.51e+07	-8.82e+06	0.0263	7.5e+06	0.0127	-1.19
40101885	1.04e+11	15598924	4.59e+07	-5.82e+06	0.029	7.52e+06	0.00615	-0.786
40877684	1.1e+11	16043736	4.85e+07	-7.57e+06	0.0304	7.51e+06	0.011	-1.02
42869204	1.2e+11	16522004	5.24e+07	-9.53e+06	0.0308	7.49e+06	0.0176	-1.29
50083870	1.31e+11	17022470	5.67e+07	-6.62e+06	0.0311	7.51e+06	0.00858	-0.896
54269364	1.43e+11	17528961	6.14e+07	-7.1e+06	0.0311	7.51e+06	0.00987	-0.961
65785134	1.58e+11	18029824	6.71e+07	-1.28e+06	0.0301	7.54e+06	0.000309	-0.173
72961016	1.73e+11	18519941	7.27e+07	2.2e+05	0.0292	7.54e+06	8.84e-06	0.0297
86718637	1.92e+11	19002660	7.98e+07	6.93e+06	0.0273	7.51e+06	0.00818	0.936
90630988	2.11e+11	19484901	8.68e+07	3.79e+06	0.0256	7.53e+06	0.0023	0.512
113933664	2.33e+11	19977508	9.49e+07	1.9e+07	0.0238	7.34e+06	0.0533	2.56
113441942	2.59e+11	20487604	1.04e+08	9.03e+06	0.0219	7.49e+06	0.011	1.22
122084586	2.8e+11	21017619	1.12e+08	9.88e+06	0.0216	7.49e+06	0.013	1.33
114237500	2.61e+11	21562790	1.06e+08	8e+06	0.0278	7.5e+06	0.0112	1.08
108291440	2.79e+11	22114647	1.13e+08	-4.72e+06	0.0277	7.53e+06	0.00385	-0.637
126203049	3.05e+11	22661293	1.23e+08	3.67e+06	0.0264	7.53e+06	0.00222	0.495
133988165	3.09e+11	23194252	1.24e+08	9.51e+06	0.0288	7.49e+06	0.0163	1.29
135118208	3.28e+11	23709115	1.32e+08	3.56e+06	0.0288	7.53e+06	0.00229	0.481
156521332	3.5e+11	24208391	1.4e+08	1.69e+07	0.0288	7.38e+06	0.0513	2.28
171102004	3.76e+11	24698821	1.49e+08	2.2e+07	0.029	7.27e+06	0.0879	2.97
181332800	3.98e+11	25190647	1.57e+08	2.41e+07	0.0301	7.21e+06	0.11	3.26
178785308	4.23e+11	25690615	1.66e+08	1.24e+07	0.0319	7.45e+06	0.0312	1.68
182455363	4.53e+11	26201954	1.77e+08	5.25e+06	0.035	7.52e+06	0.00612	0.711
201985181	4.79e+11	26720367	1.87e+08	1.53e+07	0.0388	7.41e+06	0.058	2.08
200712944	4.75e+11	27236003	1.86e+08	1.48e+07	0.0387	7.42e+06	0.0545	2.02
215935154	5.14e+11	27735038	2e+08	1.61e+07	0.045	7.39e+06	0.0757	2.2
216993527	5.45e+11	28208028	2.11e+08	6.01e+06	0.0518	7.52e+06	0.0123	0.821
215213034	5.75e+11	28650962	2.22e+08	-6.57e+06	0.06	7.51e+06	0.0173	-0.902
242487438	6.02e+11	29068189	2.32e+08	1.1e+07	0.0685	7.47e+06	0.0562	1.51
244832567	6.38e+11	29468923	2.44e+08	5.02e+05	0.0826	7.54e+06	0.000146	0.0699
231843940	6.7e+11	29866606	2.56e+08	-2.39e+07	0.0969	7.19e+06	0.402	-3.35
250560642	6.99e+11	30270965	2.66e+08	-1.56e+07	0.111	7.39e+06	0.203	-2.21
250561320	7.39e+11	30684652	2.8e+08	-2.98e+07	0.135	6.97e+06	0.946	-4.27
272229353	7.74e+11	31104655	2.93e+08	-2.06e+07	0.157	7.26e+06	0.558	-2.99

2.3.2.1 Visualization

GDP<-data1$GDP
Population<-data1$Population
CO2_emissions<-data1$Annual_CO2_emissions
s3d<-scatterplot3d(GDP,Population,CO2_emissions,
                   pch=16,
                   main="3D Scatter Plot visualizing model2")
s3d$plane3d(model2,lty.box='solid',col='blue',draw_polygon = T)

2.3.3 Criteria and Outcomes

The variables are statistically significant as the p-value of each variable is less than the significant level (<0.05).
The high value of the F statistic (1: 402.1, 2: 5921) with a low p-value (<2.2e-16), indicates the potential relationship between the independent variables and the outcome.
The R-squared value (1:0.7567, 2:0.9894 ), is a corrected goodness-of-fit (model accuracy) measure for linear models. It identifies the percentage of variance in the target field that is explained by the input or inputs, both showing an approximately high value(76% and 99%).

Through the modelling analysis, both models produce statistically acceptable results, indicating that increasing GDP and population would bring more CO2 emissions. The increase in CO2 emissions directly affects temperature increase and results in climate change while indirectly leading to sea-level rise.

3 Limitation

The high-positive correlation between the variables is known from the data modelling analysis, but the data set is so limited that the p-value and confidence interval (CO2 emissions and temperature, GDP and CO2 emissions) are very low. Although we cannot directly explain the relationship between CO2 and the four variables, the reality is that as the CO2 emissions in Malaysia increased, the temperature also increased. In addition, the increase in population and economic activity does generate more CO2 emissions in the absence of external governance of carbon dioxide emissions. Also, the excess carbon dioxide emissions could lead to temperature increase. Strictly speaking, this study can only confirm the normal association of the data and cannot be used as an official document for commercial, academic, and governmental purposes, since there are still a lot of variables would affects CO2 emissions and climate change. i.e. energy factors, greenhouse gas emissions. Facts under data analysis have proved that the decentralized collection, quality and quantity of data directly affects the Accuracy and Rigidity of the results.

4 Conclusion

Excess carbon dioxide emissions in the atmosphere have irreversible consequences for Malaysia’s environment, the most notable of which is temperature rise. GDP growth and CO2 emissions are strongly correlated in the last 3 decades, and the positive relationship indicates that CO2 emissions rise as a result of economic growth, highlighting the significance and urgency of environmental governance. To protect our natural environment and the earth, individuals and groups should toward a low-carbon economy and CO2 emissions reduction.

5 References

Abbasi, Muhammad Ali, Parveen, Shabana, Khan, Saleem, & Kamal, Muhammad Abdul. (2020). Urbanization and energy consumption effects on carbon dioxide emissions: Evidence from Asian-8 countries using panel data analysis. Environmental Science and Pollution Research International, 27(15), 18029-18043.
Deviren, Seyma Akkaya, & Deviren, Bayram. (2016). The relationship between carbon dioxide emission and economic growth: Hierarchical structure methods. Physica A, 451, 429-439.
Dechezlepretre, A. (2020). THE ECONOMIC COST OF AIR POLLUTION: EVIDENCE FROM EUROPE. From https://www.oecd.org/officialdocuments/publicdisplaydocumentpdf/?cote=ECO/WKP(2019)54&docLanguage=En
Lindsey, R. (2021). Retrieved 7 April 2022, from https://drupal-www.climate.woc.noaa.gov/news-features/understanding-climate/climate-change-atmospheric-carbon-dioxide
Zhang, Lingyun, Li, Zecheng, Kirikkaleli, Dervis, Adebayo, Tomiwa Sunday, Adeshola, Ibrahim, & Akinsola, Gbenga Daniel. (2021). Modeling CO2 emissions in Malaysia: An application of Maki cointegration and wavelet coherence tests. Environmental Science and Pollution Research International, 28(20), 26030-26044.

Climate Change and Source of CO2 Emissions in Malaysia

Jonie Xiaojie Liu 32350333

30-May-2022