Question

Carbon dioxide (CO2) emissions are widely believed to be a driver of global climate change. In this problem set you will use cross-section data to test what drives countries’ “carbon footprints,” that is, their CO2 emissions. Is it population, or is income the bigger culprit?

The data set “CO2 by country 2010 sh S17” contains data on a sample of countries’ CO2 emissions, in kilotons; population, in millions; and gross national income (GNI), in millions of US dollars, for the year 2010.

1. Please propose a linear regression model to estimate the effect of GNI on predicted CO2 emissions. Propose an economic theory to justify this model of CO2 emissions as a function of GNI and explain what the parameters and variables in this model represent.

2.Now estimate this model in Excel, like we did in class, using ordinary least squares. Report and interpret your estimated parameters here. Specifically, what does each parameter estimate tell us?

Country Name	CO2(kt)	POP(millions)	GNI(Millions of US$)
United States	5433056.54	309.33	15170300.00
Afghanistan	8236.08	28.40	15998.78
Albania	4283.06	2.86	11807.46
Algeria	123475.22	37.06	160996.42
Angola	30417.77	19.55	73946.34
Argentina	180511.74	40.37	451417.86
Armenia	4220.72	2.96	9718.57
Australia	373080.58	22.03	1096901.32
Austria	66897.08	8.39	393108.52
Bahamas, The	2464.22	0.36	7702.50
Bahrain	24202.20	1.25	23340.38
Bangladesh	56152.77	151.13	124617.10
Barbados	1503.47	0.28	4321.85
Belarus	62221.66	9.49	54058.30
Belgium	108946.57	10.92	493427.31
Belize	421.71	0.31	1258.61
Benin	5188.81	9.51	6508.31
Bermuda	476.71	0.07	7201.48
Bhutan	476.71	0.72	1497.42
Bolivia	15456.41	10.16	18785.53
Botswana	5232.81	1.97	13197.27
Brazil	419754.16	195.21	2104398.02
Bulgaria	44678.73	7.40	47167.59
Burkina Faso	1683.15	15.54	9202.85
Burundi	308.03	9.23	2014.76
Cabo Verde	355.70	0.49	1591.15
Cambodia	4180.38	14.36	10698.07
Cameroon	7234.99	20.62	23358.49
Canada	499137.37	34.01	1582763.45
Central African Republic	264.02	4.35	1981.47
Chad	469.38	11.72	10302.35
Chile	72258.24	17.15	202873.97
China	8286891.95	1337.71	5904605.99
Colombia	75679.55	46.44	275790.73
Congo, Dem. Rep.	3039.94	62.19	20651.16
Congo, Rep.	2027.85	4.11	9024.21
Costa Rica	7770.37	4.67	35553.17
Cote d'Ivoire	5804.86	18.98	23972.20
Croatia	20883.57	4.42	57968.36
Cuba	38364.15	11.28	63388.65
Cyprus	7708.03	1.10	22311.52
Czech Republic	111751.83	10.47	191444.66
Denmark	46303.21	5.55	325079.06
Dominica	135.68	0.07	483.70
Dominican Republic	20964.24	10.02	51355.94
Ecuador	32636.30	15.00	68517.07
Egypt, Arab Rep.	204776.28	78.08	214525.02
El Salvador	6248.57	6.22	20868.00
Equatorial Guinea	4679.09	0.70	9630.16
Estonia	18338.67	1.33	18239.97
Ethiopia	6494.26	87.10	29825.57
Fiji	1290.78	0.86	3043.13
Finland	61843.96	5.36	251109.55
France	361272.84	65.02	2700865.68
Gabon	2574.23	1.56	12869.57
Gambia, The	473.04	1.68	921.90
Germany	745383.76	81.78	3483764.77
Ghana	8998.82	24.26	31641.07
Greece	86717.22	11.15	293454.05
Grenada	260.36	0.10	731.14
Guatemala	11118.34	14.34	40126.71
Guinea	1235.78	10.88	4302.27
Guyana	1701.49	0.79	2272.06
Haiti	2119.53	9.90	6644.82
Honduras	8107.74	7.62	15110.48
Hong Kong SAR, China	36288.63	7.02	233476.93
Hungary	50582.60	10.00	123537.26
Iceland	1961.85	0.32	11112.24
India	2008822.94	1205.62	1690503.86
Indonesia	433989.45	240.68	689283.20
Iraq	114667.09	30.96	140108.12
Ireland	39999.64	4.56	183661.91
Israel	70655.76	7.62	227769.19
Italy	406307.27	59.28	2121166.42
Jamaica	7157.98	2.69	12736.32
Japan	1170715.42	127.45	5643192.13
Jordan	20821.23	6.05	26218.06
Kazakhstan	248728.94	16.32	128676.48
Kenya	12427.46	40.91	39852.51
Korea, Rep.	567567.26	49.41	1095599.47
Kuwait	93695.52	2.99	126113.71
Lao PDR	1873.84	6.40	6713.29
Latvia	7616.36	2.10	24579.69
Lebanon	20403.19	4.34	37501.05
Lesotho	18.34	2.01	2594.04
Liberia	799.41	3.96	1113.30
Lithuania	13560.57	3.10	35969.96
Luxembourg	10828.65	0.51	34073.20
Macao SAR, China	1030.43	0.53	25370.95
Macedonia, FYR	10872.66	2.10	9207.39
Madagascar	2013.18	21.08	8643.30
Malawi	1239.45	15.01	5290.12
Malaysia	216804.04	28.28	239358.02
Maldives	1074.43	0.33	1822.80
Mali	623.39	13.99	9003.28
Marshall Islands	102.68	0.05	198.24
Mauritania	2214.87	3.61	3444.76
Mauritius	4118.04	1.28	9835.24
Mexico	443674.00	117.89	1042119.87
Micronesia, Fed. Sts.	102.68	0.10	305.08
Moldova	4855.11	3.56	6316.18
Mongolia	11510.71	2.71	5640.28
Montenegro	2581.57	0.62	4086.06
Morocco	50608.27	31.64	88304.87
Mozambique	2882.26	23.97	9834.28
Namibia	3175.62	2.18	10766.65
Nepal	3755.01	26.85	16116.35
Netherlands	182077.55	16.62	841677.04
New Zealand	31550.87	4.37	136188.35
Nicaragua	4547.08	5.82	8699.55
Niger	1411.80	15.89	5674.42
Nigeria	78910.17	159.71	349387.81
Norway	57186.87	4.89	425901.89
Oman	57201.53	2.80	50227.83
Pakistan	161395.67	173.15	183913.43
Palau	216.35	0.02	190.20
Panama	9633.21	3.68	30229.00
Papua New Guinea	3135.29	6.86	9262.47
Paraguay	5075.13	6.46	18618.46
Peru	57579.23	29.26	137317.44
Philippines	81590.75	93.44	265929.44
Poland	317254.17	38.18	458863.46
Portugal	52361.09	10.57	230038.36
Romania	78745.16	20.25	162254.86
Russian Federation	1740776.24	142.39	1477812.94
Rwanda	594.05	10.84	5656.02
Samoa	161.35	0.19	622.45
Sao Tome and Principe	99.01	0.18	200.67
Saudi Arabia	464480.56	27.26	533855.47
Senegal	7058.98	12.95	12799.08
Serbia	45962.18	7.29	38478.03
Seychelles	704.06	0.09	926.08
Sierra Leone	689.40	5.75	2606.60
Singapore	13520.23	5.08	235074.91
Slovenia	15328.06	2.05	47507.02
Solomon Islands	201.69	0.53	508.30
South Africa	460124.16	50.90	357979.72
Spain	269674.85	46.58	1411515.96
Sri Lanka	12709.82	20.65	48950.36
St. Kitts and Nevis	249.36	0.05	663.27
St. Lucia	403.37	0.18	1204.73
St. Vincent and the Grenadines	209.02	0.11	668.95
Sudan	14172.96	35.65	60504.61
Suriname	2383.55	0.52	4330.41
Swaziland	1023.09	1.19	3802.06
Sweden	52515.11	9.38	501832.93
Switzerland	38756.52	7.82	616380.88
Tajikistan	2860.26	7.63	5563.45
Tanzania	6846.29	44.97	22626.29
Thailand	295281.51	66.40	305180.57
Timor-Leste	183.35	1.07	3295.00
Togo	1540.14	6.31	2761.61
Tonga	157.68	0.10	373.17
Trinidad and Tobago	50681.61	1.33	19669.16
Tunisia	25878.02	10.55	42169.55
Turkey	298002.42	72.14	723965.76
Turkmenistan	53054.16	5.04	20254.04
Uganda	3784.34	33.99	15713.33
Ukraine	304804.71	45.87	134410.29
United Arab Emirates	167596.57	8.44	285949.29
United Kingdom	493504.86	62.77	2434464.28
Uruguay	6644.60	3.37	37378.59
Uzbekistan	104443.49	28.56	40491.77
Venezuela, RB	201747.34	29.04	387497.39
Vietnam	150229.66	86.93	111512.78
West Bank and Gaza	2365.22	3.81	9512.20
Yemen, Rep.	21851.65	22.76	29984.28
Zambia	2427.55	13.22	18902.38
Zimbabwe	9427.86	13.08	9263.90

Answer 1

1) GNI is the total amount of money that is earned by a nation. It includes both GDP and the income received from abroad. GNI can be taken as an indicator of economic growth in the economy

Carbon emission is the emission from bringing fossil fuels and the manufacture of cement. This also includes the carbon dioxide produced during the consumption and production of solid, liquid, and gas fuels and also gas flarings.

Regression analysis can be done to understand the impact of economic growth and increasing population on carbon emissions. the regression equation would be

CO2= a+ GNI+ Population

Where Carbon emissions would be a dependent variable and GNI and population. would be an independent variable. This can be backed by an economic explanation as well. Economic growth and preserving environmental growth together is always a challenge. Economic growth always has pressure on the environment. This high pressure leads to environmental deterioration. When the economy grows, associated with that the industrial sector of the economy increases as well. The carbon dioxide is released more due to these industrial activities. These effects will be even more if the companies haven't employed environmentally friendly techniques that improved environmental quality. A proper balance between economic growth and carbon emissions is needed for the sustainable growth of the economy. Hence a regression model would help to understand the magnitude of the effect of economic growth on carbon emissions.

Along with economic growth increasing population also has an impact on carbon emission. Carbon emissions are caused by both consumption and production of fossil fuels. An increased population increases the number of resources used and also increases the demand for products which will have an effect on the production side and thus lead to more carbon emissions.

2. By using the Least square methods, the data was regressed in excel. The regression equation derived by the analysis is as follows

CO2 = 12853.84 + 0.4421 GNI

CO2 is the dependent variable and GNI is the independent variable. 0.4421 is the slope of the regression line and 12853 is the intercept. The slope of the line shows the changes in carbon emissions due to the change in GNI. The regression equation shows that a 1 unit increase in GNI will lead to a 0.44 unit increase in carbon emissions.