Empirical Study and Characteristics of Survival Carbon Emission of Household in Southwest Agro-Pastoral Area of China
Bibliographic record
Abstract
随着社会经济的发展，家庭生活用能是大气中温室气体CO2重要来源之一。通过介绍世界各国对大气中温室气体排放的历史累计及现状，诠释家庭生存性碳排放的概念。而且对家庭消费的碳排放进行计算和分析，可以反映不同地区因社会经济发展水平不同，其碳排放的本质特征，特别是我国是一个多民族的国家，南北方自然人文环境差别大。本文以我国云南省的兰坪县和香格里拉县为例，结合实地考察和问卷调查，提出家庭生存碳排放评估的意义和指标体系，计算农牧区人均能源消耗碳排放量和食品消费碳排放量。研究结果表明：在能源方面，主要是以生物质燃料和电能，特别是对薪柴的使用高达98.76%；在食物方面，人均食物消费碳排放总量为76.52 kg，以粮食消费占主导，占食物总消费量的81.30%。该区农牧民人均食物消费碳排放只是美国的50.42%，加拿大的56.19%，日本的78.76%，属于基本生存线碳排放。With the development of society and economy, household energy consumption is one of the important resources for CO2 emissions. Through the introduction of the world’s historical emissions and present situation to the atmosphere, this paper explained the concept of household survival carbon emission. And then calculating and analyzing the household consumption of carbon emissions, it could reflect the nature of its carbon emission, due to the difference of area social economic development level. Taking Lanping county and Shangri-la countyofYunnanprovince as examples, based on the investigation and survey, this study has brought forward the index system of evaluating household survival carbon emissions and calculated their per capita carbon emissions amounts of energy consumption and food consumption. The result showed that the main energy consumptions were biomass fuel and electricity, and the use of fuel wood was as high as 98.76% in the energy sector; the per capita carbon emissions of rural food consumption is76.52 kg, dominated by consumption of 81.30%. The average per capita amount of survival carbon emission in this region was the basic life line carbon emission, 50.42% of US, 56.19% of Canada, and 78.76% of Japan, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".