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Researchers used satellite measurements to determine CO2 emissions by country and carbon uptake at the national level. Using a NASA satellite that looks at Earth, CO2 emissions in more than 100 countries worldwide have been tracked. This project provides information on the amount of carbon dioxide released by certain countries. It also measures the carbon dioxide absorbed by natural “sinks” like forests. Accordingly, the results can demonstrate the usefulness of space-based technologies in helping countries meet their climate goals. Hence, these technologies can provide valuable information about the Earth’s climate.

Now the point is that,

What is the importance of NASA’s OCO-2?

The launch of the OCO-2 satellite was in 2014. Three camera-like spectrometers map natural and human-made carbon dioxide levels. So, these spectrometers detect carbon dioxide’s distinctive spectra. Afterward,  measuring how much sunlight a column of air absorbs from the gas, they indirectly estimate the gas.

Moreover, over 60 scientists worldwide took part in an international study that used data from NASA’s Orbiting Carbon Observatory-2 (OCO-2) mission and observations from the ground to figure out how much carbon dioxide in the air will change from 2015 to 2020. Anyhow, Researchers could estimate how much carbon dioxide was released and taken in by using this measurement-based or “top-down” method.

Even though the OCO-2 mission wasn’t meant to figure out how much each nation emitted, the study results are helpful because the first Global Stock take, which will look at how well the world is doing in meeting the goals of the 2015 Paris Agreement, is set for 2023.  All in all the study looked at information on CO2 emissions by country.

NASA Earth Science Division Director Karen St. Germain says: “NASA is focused on delivering Earth science data that addresses real- climate challenges – like helping governments around the world measure the impact of their carbon mitigation efforts,” Moreover, she said: “This is one example of how NASA is developing and enhancing efforts to measure carbon emissions in a way that meets user needs.”

Altogether, here arises the question,

How does bottom-up and top-down approach play a role in measuring carbon emissions?

In order to measure carbon emissions, the conventional approach involves calculating the amount of carbon dioxide released in various sectors, including transportation and agriculture this method, called “bottom-up,” is significant for keeping track of efforts to reduce emissions. But making these carbon inventories takes a lot of time and requires expertise and knowledge of the activities involved.

The study’s authors suggest a “top-down” approach that builds a database of emissions and removals to deal with this problem. This method could benefit countries that need more money to make inventories. The authors’ research includes information from over 50 countries that have not reported their emissions in the last ten years.

So here is the point to know that,

How do ecological changes and fossil fuels lead to the emission of carbon dioxide?

Tracking fossil fuel emissions and carbon in ecosystems, including trees, bushes, and soils, provides a unique perspective. Hence, this information is beneficial for keeping track of changes in carbon dioxide levels caused by changes in land cover. In addition, deforestation is the leading cause of carbon emissions in the Global South. Latin America, Asia, Africa, and Oceania form the Global South. In some regions, land management and reforestation have reduced atmospheric carbon. Therefore, the effects of deforestation on global carbon emissions vary by region.

The authors say that traditional “bottom-up” methods are essential for figuring out how much carbon dioxide an ecosystem puts out and how much it takes in. But these methods can be brutal when there needs to be more data or the overall effects of logging must be fully understood.

Philippe Ciais, study author and research director of France’s Laboratoire des Sciences du Climat et de l’Environnement, says: “Our top-down estimates provide an independent estimate of these emissions and removals, so although they cannot replace the detailed process understanding of traditional bottom-up methods, we can check both approaches for consistency,”

After all, we should know that,

Why is it critical to monitor the carbon balance of unmanaged ecosystems and identify any changes in carbon uptake?

The study presents a multifaceted understanding of the movement of carbon across Earth’s land, oceans, and atmosphere.

In addition to the human activities included in national inventories, unmanaged ecosystems can absorb carbon from the atmosphere. This can help mitigate the effects of global warming, particularly in tropical and boreal forests where human activity is minimal.

Australian university professor and research author Noel Cressie says: “National inventories are intended to track how management policies impact emissions and removals of CO2,” Moreover, he says: “However, the atmosphere doesn’t care whether CO2 is being emitted from deforestation in the Amazon or wildfires in the Canadian Arctic. Both processes will increase the concentration of atmospheric CO2 and drive climate change. Therefore, it is critical to monitor the carbon balance of unmanaged ecosystems and identify any changes in carbon uptake.”

The researchers concluded that their pilot experiment has room for improvement in revealing trends in national emissions.

NASA scientist and lead author Brendan Byrne works at the Jet Propulsion Laboratory in California, says about CO2 emissions by country: “Sustained, high-quality observations are critical for these top-down estimates,” Moreover, he says: “Continued observations from OCO-2 and surface sites will allow us to track how these emissions and removals change as the Paris Agreement is implemented. So, future international missions that provide an expanded mapping of CO2 concentrations across the globe will allow us to refine these top-down estimates and give more precise estimates of countries’ emissions and removals.”

So, here is

List of the countries along with the annual emission of carbon dioxide:

# Country CO2 Emissions
(tons, 2016)
1 Year
Change
Population
(2016)
Per
capita
Share
of
world
1 China 10,432,751,400 -0.28% 1,414,049,351 7.38 29.18%
2 United States 5,011,686,600 -2.01% 323,015,995 15.52 14.02%
3 India 2,533,638,100 4.71% 1,324,517,249 1.91 7.09%
4 Russia 1,661,899,300 -2.13% 145,275,383 11.44 4.65%
5 Japan 1,239,592,060 -1.21% 127,763,265 9.7 3.47%
6 Germany 775,752,190 1.28% 82,193,768 9.44 2.17%
7 Canada 675,918,610 -1.00% 36,382,944 18.58 1.89%
8 Iran 642,560,030 2.22% 79,563,989 8.08 1.80%
9 South Korea 604,043,830 0.45% 50,983,457 11.85 1.69%
10 Indonesia 530,035,650 6.41% 261,556,381 2.03 1.48%
11 Saudi Arabia 517,079,407 0.92% 32,443,447 15.94 1.45%
12 Brazil 462,994,920 -6.08% 206,163,053 2.25 1.29%
13 Mexico 441,412,750 -2.13% 123,333,376 3.58 1.23%
14 Australia 414,988,700 -0.98% 24,262,712 17.1 1.16%
15 South Africa 390,557,850 -0.49% 56,207,646 6.95 1.09%
16 Turkey 368,122,740 5.25% 79,827,871 4.61 1.03%
17 United Kingdom 367,860,350 -6.38% 66,297,944 5.55 1.03%
18 Italy 358,139,550 0.84% 60,663,060 5.9 1.00%
19 France 331,533,320 2.11% 64,667,596 5.13 0.93%
20 Poland 296,659,670 2.67% 37,989,220 7.81 0.83%
21 Taiwan 276,724,868 1.91% 23,618,200 11.72 0.77%
22 Thailand 271,040,160 1.55% 68,971,308 3.93 0.76%
23 Malaysia 266,251,542 6.54% 30,684,654 8.68 0.74%
24 Spain 251,892,320 -3.12% 46,634,140 5.4 0.70%
25 Ukraine 233,220,080 8.03% 44,713,702 5.22 0.65%
26 Kazakhstan 231,919,540 1.64% 17,830,901 13.01 0.65%
27 Egypt 219,377,350 4.72% 94,447,073 2.32 0.61%
28 United Arab Emirates 218,788,684 4.43% 9,360,980 23.37 0.61%
29 Vietnam 206,042,140 0.09% 93,640,422 2.2 0.58%
30 Argentina 200,708,270 0.16% 43,508,460 4.61 0.56%
31 Pakistan 178,013,820 9.13% 203,631,353 0.87 0.50%
32 Venezuela 175,884,256 -1.90% 29,851,255 5.89 0.49%
33 Netherlands 163,419,285 1.63% 16,981,295 9.62 0.46%
34 Iraq 162,646,160 1.22% 36,610,632 4.44 0.45%
35 Algeria 156,220,560 0.17% 40,551,392 3.85 0.44%
36 Philippines 126,922,662 12.37% 103,663,816 1.22 0.35%
37 Czech Republic (Czechia) 111,825,428 1.39% 10,618,857 10.53 0.31%
38 Uzbekistan 109,347,340 1.60% 31,441,751 3.48 0.31%
39 Kuwait 101,492,225 1.36% 3,956,875 25.65 0.28%
40 Qatar 98,990,085 1.79% 2,654,374 37.29 0.28%
41 Belgium 94,722,813 1.53% 11,354,420 8.34 0.26%
42 Oman 87,835,773 2.09% 4,479,219 19.61 0.25%
43 Nigeria 82,634,214 0.70% 185,960,241 0.44 0.23%
44 Chile 81,258,525 5.33% 18,209,068 4.46 0.23%
45 Turkmenistan 79,279,216 0.63% 5,662,368 14 0.22%
46 Romania 78,770,824 1.69% 19,796,285 3.98 0.22%
47 Colombia 77,667,594 -0.84% 48,175,052 1.61 0.22%
48 Bangladesh 74,476,230 4.50% 157,977,153 0.47 0.21%
49 Austria 73,764,112 1.54% 8,747,301 8.43 0.21%
50 Greece 67,840,662 -3.47% 10,615,185 6.39 0.19%
51 Israel 65,201,588 -0.38% 8,108,985 8.04 0.18%
52 Belarus 62,655,669 4.90% 9,445,643 6.63 0.18%
53 North Korea 58,708,734 19.49% 25,307,665 2.32 0.16%
54 Morocco 57,694,464 0.54% 35,126,283 1.64 0.16%
55 Peru 57,692,879 8.16% 30,926,032 1.87 0.16%
56 Libya 52,696,075 1.52% 6,492,162 8.12 0.15%
57 Finland 51,183,960 3.62% 5,497,713 9.31 0.14%
58 Hungary 51,018,899 2.16% 9,752,975 5.23 0.14%
59 Bulgaria 50,872,910 -6.00% 7,151,953 7.11 0.14%
60 Portugal 50,142,844 -2.36% 10,325,538 4.86 0.14%
61 Singapore 48,381,759 2.56% 5,653,634 8.56 0.14%
62 Hong Kong 47,066,386 1.23% 7,243,542 6.5 0.13%
63 Sweden 44,694,415 4.33% 9,836,007 4.54 0.13%
64 Norway 43,456,012 0.85% 5,250,949 8.28 0.12%
65 Serbia 41,168,059 2.27% 8,853,963 4.65 0.12%
66 Ecuador 40,065,690 -4.85% 16,491,116 2.43 0.11%
67 Switzerland 39,666,930 -2.30% 8,379,917 4.73 0.11%
68 Ireland 39,086,565 5.09% 4,695,779 8.32 0.11%
69 Syria 38,054,696 1.78% 17,465,575 2.18 0.11%
70 Denmark 38,007,645 5.23% 5,711,349 6.65 0.11%
71 Slovakia 36,817,242 1.74% 5,442,003 6.77 0.10%
72 Trinidad and Tobago 34,974,263 -5.92% 1,377,560 25.39 0.10%
73 Azerbaijan 33,614,235 -0.41% 9,736,043 3.45 0.09%
74 New Zealand 33,276,202 -0.14% 4,659,265 7.14 0.09%
75 Angola 30,566,933 3.13% 28,842,489 1.06 0.09%
76 Cuba 30,389,116 1.65% 11,335,104 2.68 0.08%
77 Tunisia 29,395,965 0.82% 11,303,945 2.6 0.08%
78 Bosnia and Herzegovina 25,674,120 0.86% 3,386,266 7.58 0.07%
79 Yemen 25,647,990 1.62% 27,168,208 0.94 0.07%
80 Bahrain 24,458,384 2.50% 1,425,792 17.15 0.07%
81 Dominican Republic 23,466,740 2.88% 10,397,741 2.26 0.07%
82 Jordan 22,772,370 1.83% 9,554,286 2.38 0.06%
83 Estonia 22,402,414 1.01% 1,316,510 17.02 0.06%
84 Lebanon 21,863,288 1.95% 6,714,281 3.26 0.06%
85 Bolivia 19,463,728 2.03% 11,031,814 1.76 0.05%
86 Croatia 19,408,194 3.02% 4,208,602 4.61 0.05%
87 Mongolia 18,574,260 18.09% 3,056,364 6.08 0.05%
88 Guatemala 18,539,316 2.42% 16,583,076 1.12 0.05%
89 Sri Lanka 18,454,691 8.55% 21,021,171 0.88 0.05%
90 Myanmar 16,701,776 5.61% 53,045,201 0.31 0.05%
91 Kenya 16,334,919 3.60% 49,051,534 0.33 0.05%
92 Montenegro 16,249,039 2.27% 627,264 25.9 0.05%
93 Slovenia 14,722,601 2.35% 2,074,210 7.1 0.04%
94 Ghana 14,469,986 3.54% 28,481,945 0.51 0.04%
95 Lithuania 13,685,264 2.66% 2,889,557 4.74 0.04%
96 Sudan 13,294,106 4.18% 39,847,439 0.33 0.04%
97 Panama 11,599,764 2.37% 4,037,078 2.87 0.03%
98 Ethiopia 10,438,855 4.03% 103,603,462 0.1 0.03%
99 Luxembourg 10,144,632 3.45% 579,264 17.51 0.03%
100 Zimbabwe 10,062,628 -4.17% 14,030,331 0.72 0.03%

 

Published by: Sky Headlines

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