|Received||:||Apr 22, 2018|
|Accepted||:||Jun 20, 2018|
|Published Online||:||Jun 25, 2018|
|Journal||:||Annals of Clinical Nutrition|
|Publisher||:||MedDocs Publishers LLC|
Cite this article: Tseng AA. Relationship between meat consumption and greenhouse gas emissions. Ann Clin Nutr. 2018; 2: 1007.
Correlation formulas are developed to estimate the dietary and total greenhouse gas emissions (GHGEs) from the nineteen countries of the Group of Twenty (G20) and the world using personal meat consumption as the only input. Based on 47,381 dietary survey samples, quadratic formulas are developed to correlate the meat consumption with GHGEs from human dietary and total activities. The formula reliability is established by comparing formula predictions with peer-reviewed results. These formulas could provide benchmark information for strategy development for reducing GHGEs in order to mitigate the global warming problems. The present study finds that, from 2013 to 2015, the daily dietary GHGE per capita of the nineteen countries varies widely from 4.11kgCO2 e from India to 8.71kgCO2 e from the USA. In 2013, the contribution of the dietary GHGE to the total GHGE changes from 11% for Canada to 63% for India while the world average is 32 %. From 2013 to 2015, the total GHGE changes among the nineteen countries are from a 1.7%-reduction in Russia to a 4.0%-increase in Turkey. Furthermore, the formulas predicate that the global dietary and total GHGEs increase monotonically from 15.9 and 49.5 GtCO2 e in 2015 to 17.7 and 55.4 GtCO2 e in 2025, respectively.
Correlation Formulas are developed to estimate dietary and total greenhouse-gas emissions.
The greenhouse-gas emissions of nineteen major countries in 2015 are evaluted.
The greenhouse-gas emissions of the world from 2015 to 2025 are predicted
Keywords: Food consumption; Greenhouse gas emission; Group of twenty; Human dietary behavior; Meat consumption, World
The food production system is a major contributor to global GHGEs, which are produced at all stages in the system . The Food Climate Research Network estimated that the food system in UK in 2008 accounts for 18.4% of all the GHGEs generated by human activities [2,3] reported that the production of livestock accounts for 18% of the GHGEs; based on a land-use model and also estimated that the cumulative GHGEs in the period from 2010 to 2050 could be up to 20% lower if all people would be vegetarians. Li et al.  studied the food chain systems in China, beginning with agricultural production and ending with consumption and waste disposal. They projected that, based on existing trends, the GHGEs increases from 1.585 Gigaton (Gt) of CO2 e in 2010 to 2.505 GtCO2 e in 2050, which represents a 58% increase within 40 years. However, although the GHGE can grow with rising food demand, the growth can be counterbalanced by eating more plant-based food. This can cause the GH-GEs to fall to 1.118 GtCO2 e by 2050, a 30% reduction compared with the level in 2010.
Despite being one of the major causes of anthropogenic GHGEs, the GHGEs from food consumption or dietary GHGEs seldom get attention and are rarely reported by international organizations or government agencies, although the dietary GHGE data is essential in managing the total GHGEs and in reducing dietary GHGEs. Consequently, one of the purposes of this article is to develop a correlation formula to estimate the dietary GHGEs using meat consumption as the only required input data. The formula reliability is demonstrated by comparing formula predictions with published data by other approaches. The dietary GHGE and its contribution to the total GHGE for the 19 countries of the Group of Twenty (G20) are then studied in order to illustrate the simplicity and versatility of the formula developed.
The correlation formula is further enhanced to predict the global dietary and total GHGEs from 2015 to 2025 by assuming that the global dietary or human behavior follows the current trend without major changes as those recommended by Hawken . It is noteworthy that meat consumption is related to socioeconomic conditions, including living standards, diet, livestock production, and consumer prices and that the meat production has significant environmental and economic consequences for the earth . Also, meat is a major commodity and meat consumption data are frequently provided by many international organizations [7,8].
The 19 countries in G20 considered including Argentina, Australia, Brazil, Canada, China, France, Germany, India, Indonesia, Italy, Japan, South Korea, Mexico, Russia, Saudi Arabia, South Africa, Turkey, United Kingdom (UK) and United States (US), are not only major economies but also major GHG emitters. Understanding the amount of dietary and total GHGEs from the major emitters is essential in developing an effective strategy for reducing the GHGEs in order to mitigate the global warming challenges. In the present study, the last G20 member, the European Union (EU), is replaced by the whole world and the associated global results are serving as the benchmark for the comparison with that of the 19 countries.
The dietary survey from 47,381 participants and the corresponding carbon-footprint or GHGE data reported by Scarborough et al.  are adopted and analyzed to develop correlation formulas to estimate the dietary and total GHGEs. The survey data are placed into five dietary groups: vegan (2,041 samples), vegetarian (15,751 samples), low-in-meat eater (9,332 samples), medium-in-meat eater (11,971 samples) and high-inmeat eater (8,286 samples). The low-in-meat eaters consume meat less than 50g daily while the high-in-meat eaters consume meat more than 100g/day. Both vegans and vegetarians do not eat any meat. Although vegans avoid all animal products, vegetarians can consume dairy products and eggs.
The dietary data for the five groups considered are summarized in Table 1 in the 2nd and 3rd columns for the daily meat consumption per capita (MCd ) and the daily dietary GHGE per capita (GHGEdd) respectively. Here, the Sampling Interval (SI) is controlled with the statistical significance at the 5% level while the Confidence Interval (CI) is in 95%. As indicated in Table 1, three places are marked with “?” because more data are needed in these places for the correlation analysis to be performed; these are not provided by the source article of Scarborough et al. . Firstly, the upper bound of the Sampling Interval (SI) for the high-in-meat eaters is not specified (? mark in the 2nd row of 2nd column). Secondly and thirdly, we cannot have two GHGE values for the initial condition (IC) or at MCd = 0 in a correlation analysis; as shown in Table 1 (? marks in the 5th & 6th rows of 2nd column), MCd = 0 for both vegan and vegetarian groups.
To determine the appropriate values for the three-additional data required, we perform two cases of correlation analyses with different assumptions. The correlation results for the two cases are then used to judge which assumptions is the most appropriate for predicting GHGEdd.
In Case 1, we assume that the MCd range for the high-in-meat group is from 100 g to 150 g having an SI of 50 g, which is same as that of the medium-in-meat and low-in-meat, as indicated in the 2nd column of Table 1. Two ICs: IC-1 and IC-2 are evaluated, where IC-1 is based on the vegan data, i.e., at MCd = 0, GHGEdd = 2.89 kgCO2 e, while IC-2 is from the vegetarian data, at MCd = 0, GHGEdd = 3.81 kgCO2 e.
The correlation results are shown in Figure 1, where the solid and dotted lines represent the results with IC-1 and IC-2, respectively. The correlation coefficients, R2 , are also displayed in the figure, where R2 =1 means a perfect match of the two variables correlated. As shown, both correlation curves fit the data very well, since the associated R2 s are all higher than 0.96. The correlation with IC-2 (dotted line, vegetarian data) is slightly better, since its R2 is about 3% higher than that of the vegan data (IC-1). However, the correlation curves outside the region of MCd > 125g, do not agree with each other very well, and the associated trend lines appear to diverge from each other, which implies that the assumptions made in Case 1 are not appropriate and the MCd range needs to be reconsidered with one IC. The appropriate assumptions for MCd and IC are analyzed in the next subsection.
The correlation results for Case 2 are depicted in Figure 2, where the dotted and solid lines represent the results based on SI-1 and SI-2 conditions, respectively. As shown in the figure, both correlations fit the data points extremely well, since the corresponding correlation coefficients (R2 ) are higher than 0.99, almost perfect. As shown in Figure 2, the dotted-line correlation curve (based on SI-1) almost monotonically increases with MCe. In general, an increase of MCe can cause a reduction of plant food consumption based on the same amount of food-energy (calories) diets. As a result, the increase rate of GHGE should be gradually reduced to counter the reduction of plant food consumption. Thus, the linear increase of GHGEdd with MCe shown in the dotted line of Figure 2 (using SI-1 condition) does not reasonably reflect the true correlation of MCe to GHGEdd. It is believed that using the SI of 50 g (SI-1) is not large enough to cover all the sampling data for the high-in-meat group, because many of the survey participants consume more meat than the range (more than MCd >150 g) considered in SI-1.
Consequently, as shown in Figure 2, the growth rate of the solid-line curve (based on SI-2) is gradually decreasing as MCe increases; the solid-line curve provides more reasonable prediction of GHGEdd for the range considered. Also, the R2 of the solid-line curve (SI-2 condition) is higher than that of the dotted-line curve (SI-1 condition), which means that Case 2 with SI-2 condition provides more accurate correlation than that of SI-1 condition. Furthermore, by reviewing the raw survey data, very few survey participants consume meat more than 200g (MCe = 241.73g). As a result, the correlation of Case 2 with SI-2 condition should be adopted as the correlation formula to quantify the relationship between GHGEdd and MCe.
As indicated in the analysis presented in the preceding sections, the correlation result of Case 2 with SI-2 condition shown in Figure 2 is adopted to form the formulas to quantify the dietary GHGEdd using MCd as the input. Since MCe [g] = MCd [g] + 41.73 [g], the correlation formulas can be found as:
for MCd > 0 (meat-eater groups),
GHGEdd = - 2.0x10-5 (MCd+41.73)2+ 0.0264 (MCd+41.73)+2.8664, ---------(1a)
for MCd = 0 and MCe > 0 (vegetarian group),
GHGEdd = 3.81, ---------(1b)
and for MCd = 0 and MCe = 0 (vegan group),
GHGEdd = 2.89, ---------(1c)
where GHGEdd is in [kgCO2e]; MCe and MCd are in [g].
To estimate GHGEdd, the input data of MCd is required for 19 countries and the world considered, this section also present the evaluation of the dietary GHGEs.
Except four European counties: France, Germany, Italy, and UK, OECD  provides the annual meat consumption per capita for all other 15 of the 19 countries considered for 2013 and 2015. The meat consumption data are tabulated in kg of retail weight for the “Big-Four” livestock, i.e., beef/veal, pork, poultry, and sheep.
For the four European countries: France, Germany, Italy, and UK, the FAO data  are adopted. Since FAO’s data are based on DW, they need to be converted from DW to RW by multiplying the yield of 0.803 and then converted from RW to MW by a yield of 0.92.
The input data of meat consumption in retail weight in 2014 and 2017 can be found from the data provided by OECD , which were tabulated in kg of retail weight for the “Big-Four” livestock, i.e., beef/veal, pork, poultry, and sheep. The total weight of the annual Meat Consumption (MCa) in kg/capita for the fifteen G20 states and the average of the 28 states of EU are listed in the 2nd column of Table 2 for 2014. In the present study the 28 states of EU (EU28) are considered as a whole and as a single entity. Since the four states in EU28, which overlap with those of G20 members, are already counted in EU28, only 15 (= 19-4) states of the G20 plus EU28 and the world are studied.
On the other hand, in response to the UK dietary survey, the participants are most likely reporting their meat consumption in either Cooked Weight (CW) or RW. According to Scarborough, et al. , the meat consumption data in their survey, which are used to develop Eq. (1a), have not been distinguished between the meat consumed being raw and being cooked. In fact, the survey participants usually report their meat consumptions by using the weight labeled on the meat product purchased from supermarkets or grocery stores, where both the raw and cocked meats are sold. Many of cocked meats, such as roasted meats, barbecue meats, sliced deli meats, and cooked ham are sold in typical supermarkets or grocery stores. Also, the participants can report the CW displayed on menus, when they eat at restaurants or similar places. Furthermore, the participants can measure their cooked food before eating as indicated in many websites [12,13]. Consequently, in this article, it is assumed that one-third of meat consumption data used to develop Eq. (1a) are based on CW and the other two-thirds are based on RW. Thus, for the sake of clarity of the presentation, the weight of meat consumption used in Eq. (1a) is called ‘Mixed Weight (MW)’.
Normally, CW is less than RW due to the moisture and fat being drawn out during the cooking process. There is no one yield value for converting RW to CW for meat, because there are a lot of factors, such as cooking methods, meat quality, cooking temperature, cooking time etc. to account for. The US Department of Agriculture reports an extensive study on the cooking yields for more than two-hundred different processes in cooking meat . The cooking yields reported vary from 0.29 for microwaving pork to 0.96 for baking or roasting pork ham. The major cooking yields are between 0.65 and 0.85, which are also consistent with other private estimations . The average yield of 0.75 is thus selected for the present calculation, in which onethird of the survey data for meat consumption is based on CW. The average yield for converting the OECD or FAO data from RW to MW used in the present calculation becomes 0.92.
In studying the dietary GHGEs in both 2013 and 2015, the annual meat consumption per capita data in RW (MCa ) in 2013 and 2015 reported by OECD  and FAO  are selected. Except four European countries: France, Germany, Italy, and UK, OECD provides data for all other 15 of the 19 countries considered. As discussed in the preceding subsection, the yield value of 0.92 is needed to adjust the OECD data to have the meat in MW, which is required by Eq. (1a). As an example, based on OECD data, the MCa of Argentina is 84.6 kg in RW and the MCa of Argentina becomes 213.2gMW, i.e., 84.6kgRW x 0.92 (yield from RW to MW) x 1000/365, where there are 365 days in the year of 2013. Following the same procedure, the MCd for the other 14 countries and the world can be calculated. Both MCa data and MCd results for the 15 countries mentioned are listed in the 2nd and 3rd column of Table 2, respectively.
For the four European countries: France, Germany, Italy, and UK, the FAO data  are adopted. Since FAO’s data are based on DW, they need to be converted from DW to RW by multiplying the yield of 0.803 and then converted from RW to MW by a yield of 0.92. For example, the annual meat consumption of France is 86.67kg in DW, the MCd of France becomes 175.6gMW, i.e., 86.76kgDW x 0.803 x 0.92 x 1000/365. For the sake of consistence, the FAO data for the 4 European countries shown in the 2nd column of Table 2 are already converted from DW to RW. Moreover, the FAO data are also verified by comparing the global consumption of the FAO data in 2013 with that of the OECD data, because both FAO  and OECD  report the total meat consumption in DW.
Since FAO data are not available for 2015, the 2013 FAO data for the four European countries are modified to be used as the 2015 data. OECD (2016) has the average meat consumption data for the European Union (EU28) in both 2013 and 2015, which changes from 64.9kgRW in 2013 to 68.3kgRW in 2015, an increase of 5.24%. Consequently, the data for the four countries in 2015 are using their 2013 data with an adjustment of a 5.24%-growth. The 2015 MCa data for all 19 countries and the world in [kgRW] are summarized in the 2nd column of Table 3. Both the OECD  and FAO  data count the amount of the meat consumption for the “Big-Four” livestock, i.e., beef/veal, pork, poultry, and sheep.
In this section, the MCd calculated earlier is used to estimate GHGEdd for the 19 countries of G20 in 2013. The GHGEdd are then compared with the daily total GHGE (GHGEtd) to establish a correlation for the predication of future GHGEtd.
Using the MCd data earlier reported in the 3rd column of Table 2 as an input to Eq. (1a), the GHGEdd from the 19 countries of G20 and the world in 2013 can be estimated. For example, by substituting 213.2gMW (the MCd of Argentina) into Eq. (1a), the GHGEdd from Argentina can be obtained to be 8.30 kgCO2 e. Following the same procedure, the GHGEdd from the other 18 countries and the world in 2013 can be found and all results are reported in the 4th column of Table 2.
As shown in Table 2, the GHGEdd varies noticeably from 4.11kgCO2 e from India to 8.61kgCO2 e from Australia in 2013. The global average is 5.90kgCO2 e. The maximum GHGEdd is more than twice larger than the minimum GHGEdd, which implies that there is a reasonably big room for the heavy meateater countries to mitigate their dietary GHGEs by promoting plant-based diets.
Equation (1a) can be re-correlated MCa (not MCd ) in [kgRW] (the 2nd column of Table 2) with GHGEdd in [kgCO2 e] (the 4th column of Table 2) directly. The re-correlated formula can be found as:
GHGEdd = -1.2706x10-4MCa2 + 0.062258MCa+ 3.9332 ---------(1d)
where MCa is in [kgRW] and GHGEdd is in [kgCO2 e]. The above equation should be adopted for later GHGEdd predictions to eliminate the steps for meat-weight conversion.
The World Resources Institute through its Climate Analysis Indicators Tool  website provides historical GHGE data for many countries. There are two types of CAIT data reported: Total GHGEs Excluding Land-Use Change and Forestry and Total GHGEs Including Land-Use Change and Forestry. As required by the UN Climate Change Secretariat, the GHG inventory sector should cover the emissions and removals of GHGs resulting from direct human-induced land use, land-use change, and forestry activities. Except USA, the CAIT  data including the Land-Use Change and Forestry activities are adopted for the 18 countries and the world, while the US data is obtained from its Environmental Protection Agency . All the national total GHGE data (GHGEtn) for the 19 countries and the world in 2013 are listed in the 5th column of Table 2.
The population data for the 19 countries and the world in 2013 from UN Population Division  are listed in the 6th column of Table 2. With the population data, the GHGEtn can be converted to the personal daily total GHGE (GHGEtd) and are summarized in the 7th column of Table 2.
To estimate the contribution of GHGEdd (in the 4th column of Table 2) to GHGEtd (in the 7th column of Table 2), the Radt ratio (= GHGEdd /GHGEtd) can be obtained and are shown in the 8th column of Table 2. As shown, the contribution of the dietary GHGE to the total GHGEs in 2013 varies from the lowest 11% from Canada to 63% from India depending on the specific country considered, where the corresponding global average is 32%. The wide variation of the Radt ratio among the 19 countries of G20 implies that the food system in each country considered is greatly influenced by not only the dietary behavior of the people in each country but also its socioeconomic conditions . Note that the Radt ratio is going to be used as a weighting function for the predications of the GHGEtd from 2015 to 2025.
In 2006, the world per capita meat production (MCa ) reported by OECD  is 31.5kgRW. By adjusting the weight from RW to MW, the yield of 0.92 is again used. Thus, the daily per capita meat consumption (MCd ) can be found to be 79.4gMW (= 31.5 x 0.92 x 1000/365). Using Eq. (1d) and MCa = 31.5kgMW, the GHGEdd can be found to be 5.77kgCO2 e. Since the world population is 6.6x109 in 2006 , the annual dietary GHGE of the world can be found to be 1.39x1013 (= 5.77 x 6.6x109 x 365) kgCO2 e or 13,900 MtCO2 e. Since the world’s total GHGE in 2006 was 42.779 GtCO2 e (including land-use change and forestry) , the present prediction of the GHGEs from the food consumption is 32.5% (= 100 x 13,900/42,779) of the total human GHGE. In addition, based on the most recent data by CAIT (2016), the total global GHGE in 2013 is 48.257 GtCO2 e. The dietary GHGE can be calculated from Eq. (1d) and is also 32.5% of the total global GHGE. The above calculation is based on that MCa is 34.1kgRW provided by OECD  and the world population is 7,349 million in 2013 .
In 2006, Tukker and Jansen  reported that GHGEs from food consumption accounts for approximately 31% of total GHGEs in the EU-25. Also, according to Garnett  the GHGE associated with the food system rises to up to 30% when additional emissions from fuel use, fertilizer production and agriculturally induced land use change are included. By comparing with the 31% and 30% of the total GHGEs reported by Tukker & Jansen  and Garnett , respectively, the present predication of 32.5% is less than 5% and 10% higher than that reported by Tukker & Jansen and Garnett, respectively.
In a more recent study, Vermeulen et al.  estimated that, the GHGE for food systems releases from 9.8 to 16.9 GtCO2 e, which is consistent with the present estimation of 13.9 GtCO2 e. Furthermore, Fiala  assessed that the emission from meat production is accounting for between 15% and 24% of the total GHGEs. As estimated by Friel et al.  80% of agricultural emissions (from both meat-based and plant-based food production) arise from the meat production sector. Thus, based on the Fiala’s estimation, the GHGEs from both meat and plane-based food consumption contribute between 18.75 (15/0.8) and 30.0 (24/0.8) % of total GHGEs.
Based on the comparison presented above, the present prediction is less than 5% to 10% higher than that reported by other studies and the differences are relatively small. Consequently, the present prediction can be considered reasonably reliable and can also be considered an upper-bound estimation.
As mentioned earlier, meat consumption is related to socioeconomic conditions and is characterized by high production costs and associated with higher incomes. Thus, the meat consumption should have significant economic and environmental consequences. In this section, the meat consumption is further correlated with the total GHGEs. Both the dietary and total GHGEs for the 19 countries of G20 in 2015 and for the world from 2015 to 2025 are estimated.
Using Eq. (1d) with the MCa data provided by OECD  or by FAO  (in the 2nd column of Table 3), the GHGEdd for the 19 countries considered can be calculated and shown in the 3rd column of Table 3. As shown, in 2015, the GHGEdd varies noticeably from 4.11kgCO2 e from India to 8.71kgCO2 e from USA. The global average of GHGEdd is 5.91kgCO2 e increasing 0.17% as comparing to that of 2013.
Equation (1d) establishes the correlation between MCa and GHGEdd while Radt (in the 8th column of Table 2) is the ratio of GHGEdd to GHGEtd. Thus, Eq. (1d) can be combined with the Radt ratio to correlate MCa to GHGEtd as:
GHGEtd = (-1.2706x10-4MCa2 + 0.062258MCa +3.9332)/Radt ---------(2)
where MCa is in [kgRW] and GHGEtd is in [kgCO2 e]. If the dietary behavior is not changed very much for the countries considered from 2013 to 2015, the above equation can be used to predict the GHGEtd in 2015 using the 2015 meat consumption data reported earlier (in the 2nd column of Table 3), the 2015 population data by UNPD  (in the 5th column of Table 3) and the Radt values in 2013.
The results based on Eq. (2) for the national total GHGE (GHGEtn) for the 19 countries and the world are summarized in the 6th column of Table 3. The results indicate that, from 2013 to 2015, the GHGEtn changes in these 19 countries are from a 1.7%-reduction of GHGEtn from Russia to a 4.0%-increase of GHGEtn from Turkey, while the whole world emits 2.5% more GHG, growing from 48.257 to 49.453 GtCO2 e.
As shown in Table 3, the average growth of GHGEtn of the 19 G20 countries from 2013 to 2015 is 1.8%, which is about 28% lower than that of global GHGEtn. This 28% difference implies that there is a big room for the global efforts in the reduction of global GHGEtn. Also, based on the CAIT  estimation (including land-use change and forestry), the world emits 47.59 GtCO2 e in 2012 and 48.26 GtCO2 e in 2013. The corresponding annual growth rate of the global GHGEtn is 1.40% which is about 12% lower than that of the average growth rate of the global GHGEtn from 2013 to 2015, i.e., 1.25% (101.25%2 = 102.5%, which reported in the 7th column of Table 3).
OECD  has studied the trend of the meat consumption of many different countries and has predicted the global meat consumption from 2015 to 2025. Based on the meat consumption data from OECD , which are listed in the 2nd column of Table 4, the global dietary (GHGEdd) and total (GHGEtd) GHGEs from 2015 to 2025 can be calculated from Eqs (1d) and (2), respectively. The corresponding results are presented in the 3rd and 4th columns of Table 4. As shown in Table 4, GHGEdd and GHGEtd are monotonically increasing from 5.91 and 18.43 kgCO2 e in 2015 to 5.97 and 18.63 kgCO2 e in 2025, respectively. Since Eqs (1d) and (2) only consider the effects of changing diets but not consider the technology improvement in producing the human food, it is not surprised that GHGEs are monotonically growing as the meat consumptions, which are monotonically increasing. If the food production technology has reasonable improvement, the future GHGEdd and GHGEtd can have reasonable reduction.
Using the population data predicted by UNPD , the GHGEdn and GHGEtn from 2015 to 2025 can be estimated from the results of GHGEdd and GHGEtd just calculated. The corresponding results are listed in the 6th and 7th columns of Table 4. Again as indicated in Table 4, the estimated results of GHGEdn and GHGEtn are monotonically increasing from 15.85 and 49.45 GtCO2 e in 2015 to 17.75 and 55.38 GtCO2 e in 2025. The GHGEdn and GHGEtn increase with an average annual growth rate of 1.16%. Again, if the green-energy related technology can be greatly improved and the human activities in energy saving can be implemented, the future GHGEdn and GHGEtn can be greatly lower as estimated by Hawken . Also, having a sizable change of the human dietary behavior by switching to more plant-based food can help to mitigate GHGEs. Otherwise, if the current trend continues, it would be inevitable to exceed the 2C limit imposed by the Paris Climate Agreement in a near future.
Correlation formulas are developed based on a dietary survey with 47,381 participants to estimate the Greenhouse Gas Emissions (GHGEs) from human food consumption and total activities for the 19 countries of G20 and the world. The correlation results show that the daily dietary GHGE per capita in 2013 varies widely from 4.11kgCO2 e in India to 8.61kgCO2 e in Australia, while the global average is 5.90kgCO2 e. The maximum dietary GHGE is more than twice higher than the minimum one, which implies that there is a reasonably large room for the heavy meat-eater countries to mitigate their dietary GHGEs by switching to more plant-based diets.
In 2015, the total GHGE among the 19 countries considered varies from 357 MtCO2 e in Turkey to 11,550 MtCO2 e in China. The wide variation of the GHGEs implies that the food system in each country considered is greatly influenced by not only the dietary behavior of the people but also its socioeconomic conditions. As a result, for the development of an effective strategy or green technology to reduce the GHGEs, the impact of the socioeconomic and technological conditions on the dietary and total GHGEs should be essential and worthwhile for further study.
The ratio of dietary GHGE to the total GHGE in 2013 is calculated. The ratio among the 19 countries varies from the lowest 11% in Canada to 63% in India, while the world average is 32%, which is consistent with other published results obtained by using different approaches. This demonstrates that the correlation formulas developed are not only simple, but also reliable.
The growths of the total GHGEs from 2013 to 2015 are estimated. The results indicate that, from 2013 to 2015, the total GHGE changes in these 19 countries are from a 1.7%-reduction in Russia to a 4.0%-increase in Turkey. The whole world is found to emit 2.5% more greenhouse gases, increasing from 48,257 in 2013 to 49,453 MtCO2 e in 2015, with an annual growth rate of 1.25%, which represents a 12% of emission reduction as compared with the annual rate from 2012 to 2013.
Furthermore, if the current trend is persistent, the present study predicates that the global dietary and total GHGEs increase monotonically from 2015 to 2025 with an annual growth rate varying between 0.99% and 1.32%. It is believed that the new green technologies to every sectors of industry have to be developed and the managing plan and technology to reduce human energy consumption has to be implemented or improved. Otherwise, it should be very difficult for not exceeding the 2C limit imposed by the Paris Climate Agreement. Certainly, switching the dietary behavior to have more plant-based diets can also help in mitigating the GHGEs.
More dietary surveys are recommended to be conducted to provide data with more dietary groups with smaller sample interval, i.e., more than the five groups and smaller than 50-g sample interval considered; so that more accurate and reliable correlations can be developed.
Mathematically, the national GHGE is a geographical and temporal variable, depending on the specific country and year considered. As suggested by the form of Eq. (2), the geographical effect is managed by the ratio Radt, while the temporal effect is handled by the changes of MCa . The current approach in developing Eq. (2) is similar to the simplest first-order forward scheme of the finite difference method, where the effects of geographical and temporal are also separately treated and each effect is taken care by a set of finite difference approximations. To improve the reliability of the correlation developed, the ratio, Radt, should be updated every few years. This ratio may need to be further improved to include time-effect predictability by compiling more data on both technological improvements and socioeconomic changes in each nation considered, so that the prediction can be more accurate and reliable.
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