Societal Dimension of Energy Consumption – Exploring Environmental Inequality in China
Bibliographic record
Abstract
From the social-ecological nexus perspective, environmental inequality is embedded in its root of social problem arising from income inequality. “The urgent global challenges of sustainability and equity must be addressed together” (IPCC2011). This paper intends to explore the link between house income inequality and environmental vulnerability in Rural of China. In the process of rural to urban dominated economy transformation, social structures are changing, and ecosystems are facing stress. Given China's dynamic economic and environmental situation, we aim to provide an assessment in the inequality of energy use and environmental effects in two different systems of urban and rural region in China. The paper deals with three questions: 1. how has household expenditure linked with the energy use directly and indirectly; 2. how has China challenged by inequalities between rural and urban household on the residential energy consumption; 3. how high is the emission estimated in the rural residential energy use? The analysis results indicate obviously that almost half rural family still use no-commercial energy and thus coal is the main commercial energy sources; the per capital CO2 emissions of rural region is much higher than urban region, which is driven by low energy efficiency and less advanced public infrastructure. It address the equity issues that policy should focus on energy affordability and promoting a transition away from biomass to other modern energy sources in rural China. In the paper, the input-output table is employed for accounting the indirect residential energy use and emissions, which is associated with the eight sectors of household expenditure. The data sources are from various household serveries and energy statistics in the period of 1990 to 2016.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".