A Holistic, Dynamic Model to Quantify and Mitigate the Environmental Impacts of Cattle Farming
Advances in Animal Biosciences(2015)
Natural Resources Institute Finland (Luke)
Abstract
There are approximately 200,000 single-family houses heated with oil in Finland and the substitution of oil heating embodies significant carbon dioxide emission reduction potential in the residential sector. We analyze the economics of replacing oil heating with a ground source heat pump (GSHP) or a pellet heating from the consumers’ perspective in alternative policy and oil price scenarios. We differentiated between cases where the oil burner is replaced in the end of its lifetime or before it.The results indicate that the level and uncertainty associated with future crude oil price are major factors in the profitability of replacement. If the international oil market price remains low, an early replacement is not likely to be economical for the consumer in Finland. Yet, with a recovering oil price even an early replacement can be optimal. If oil burner cannot be used anymore, it is in most scenarios economical to replace it with pellet or GSHP if some subsidy for heating renovation is available. Subsidies improve the profitability of both pellet and GSHP investments more than fuel taxation. Consumer discount rate also affects the results.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined