Potential Impact Of Meat Replacers On Nutrient Quality And Greenhouse Gas Emissions Of Diets In Four European Countries

SUSTAINABILITY(2020)

引用 20|浏览33
暂无评分
摘要
Meat replacers could play a role in achieving more plant-based diets, but their current consumption is limited. The present modelling study aimed to explore the nutritional and greenhouse gas emissions impacts of meat replacers. Using dietary surveys from Denmark, Czech Republic, Italy and France (similar to 6500 adults), we composed alternative diets in which all the meat in the observed diet (in grams) was substituted by similar use meat replacers (with and without fortification). Starting from the observed diets and meat-replacement diets, diets with improved adherence to food-based dietary guidelines (FBDGs) were modelled using Data Envelopment Analysis. These improved diets were then further optimised for dietary preferences (MaxP, diet similarity index), nutrient quality (MaxH, Nutrient Rich Diet score, NRD15.3) or diet-related greenhouse gas emissions (GHGE) (MaxS, CO(2)equivalents). In all optimised modelled diets, the total amount of meat was lower than in the observed diets, i.e., 30% lower in the MaxP, 50% lower in the MaxH, and 75% lower in the MaxS diets. In the MaxP diet, NRD15.3 was similar to 6% higher, GHGE was similar to 9% lower, and similar to 83% of food intake remained similar. In the MaxH diet, NRD15.3 was similar to 17% higher, GHGE was similar to 15% lower, and similar to 66% of food intake remained similar. In the MaxS diet, NRD15.3 was similar to 9% higher, GHGE was similar to 33% lower, and similar to 65% of food intake remained similar. When using fortified meat replacers, for all modelled diets, the diet similarity was on average 2% lower and the GHGE reduction was on average 3% higher as compared with the same scenarios without fortification. This analysis showed that meat replacers, provided their preference is similar to meat, can provide benefits for GHGE, without necessarily compromising nutrient quality.
更多
查看译文
关键词
diet modelling,environmental impact,greenhouse gas emissions,nutritional quality,preferable,scenario analysis
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要