GHG mitigation strategies on China's diverse dish consumption are key to meet the Paris Agreement targets.

Combatting climate change depends on demand-side mitigation strategies related to food, which is in turn contingent on explicit estimation and management of dish-level emissions. Here, on the basis of a bottom-up integrated emissions framework, we first estimate the greenhouse gas emissions of 540 dishes from 36 cuisines using data from over 800,488 restaurants in China's provincial capital cities. By mining residents' dietary preferences, we then design various dietary change strategies to explicitly link food emissions to the Paris Agreement pledges. The results show that China's food system greenhouse gas emissions were approximately 4.64 GtCO2eq in 2020, accounting for 37% of total emissions, with average per-dish emissions of 8.44 kgCO2eq. Current emission patterns of food consumption in China may not be consistent with the attainment of the 1.5 °C and 2 °C climate targets, but transitioning towards low-emission cuisines and dishes could change that by reducing emissions by 38-69%.

RTC_TongueNet: An improved tongue image segmentation model based on DeepLabV3.

Objective: Tongue segmentation as a basis for automated tongue recognition studies in Chinese medicine, which has defects such as network degradation and inability to obtain global features, which seriously affects the segmentation effect. This article proposes an improved model RTC_TongueNet based on DeepLabV3, which combines the improved residual structure and transformer and integrates the ECA (Efficient Channel Attention Module) attention mechanism of multiscale atrous convolution to improve the effect of tongue image segmentation. Methods: In this paper, we improve the backbone network based on DeepLabV3 by incorporating the transformer structure and an improved residual structure. The residual module is divided into two structures and uses different residual structures under different conditions to speed up the frequency of shallow information mapping to deep network, which can more effectively extract the underlying features of tongue image; introduces ECA attention mechanism after concat operation in ASPP (Atrous Spatial Pyramid Pooling) structure to strengthen information interaction and fusion, effectively extract local and global features, and enable the model to focus more on difficult-to-separate areas such as tongue edge, to obtain better segmentation effect. Results: The RTC_TongueNet network model was compared with FCN (Fully Convolutional Networks), UNet, LRASPP (Lite Reduced ASPP), and DeepLabV3 models on two datasets. On the two datasets, the MIOU (Mean Intersection over Union) and MPA (Mean Pixel Accuracy) values of the classic model DeepLabV3 were higher than those of FCN, UNet, and LRASPP models, and the performance was better. Compared with the DeepLabV3 model, the RTC_TongueNet network model increased MIOU value by 0.9% and MPA value by 0.3% on the first dataset; MIOU increased by 1.0% and MPA increased by 1.1% on the second dataset. RTC_TongueNet model performed best on both datasets. Conclusion: In this study, based on DeepLabV3, we apply the improved residual structure and transformer as a backbone to fully extract image features locally and globally. The ECA attention module is combined to enhance channel attention, strengthen useful information, and weaken the interference of useless information. RTC_TongueNet model can effectively segment tongue images. This study has practical application value and reference value for tongue image segmentation.