Comprehensive Analysis of the Environmental Benefits of Introducing Technology Innovation in the Energy Sector: Case Study in Chongqing City, China
Bibliographic record
Abstract
Because of the serious damage caused by acid rain, Chongqing city was designated as an Acid Rain Control Zone by the Chinese Central Government. The main factor responsible for the acid rain is the ever-increasing emission of sulfur dioxide (SO2) due to utilization of coal as the primary energy source to meet increasing energy demand. Simultaneously, CO2 emission has dramatically increased with coal utilization. In order to transform the current energy structure, alternative renewable energy technologies must be identified and proved feasible and effective. This research aims to comprehensively analyze the benefits of small-scale hydropower and wind power technologies to reduce SO2 and Greenhouse gases (GHG). For this purpose, we constructed a dynamic comprehensive evaluation model based on an Input-Output (I/O) analysis for the period 2010-2020. The simulation results indicate that the introduction of small scale hydropower and wind power technologies have a positive impact on Chongqing’s socio-economic, environmental and energy development in the first half of the study period. However, the results also show that the scarcity of renewable energy technologies to meet the increasing energy demand as well as the stricter emission constrains affect both economic growth and SO2 and GHG reduction efforts from the latter half of the study period. To address this weakness, the study suggests that additional advanced renewable energy technologies are necessary as well as specific regulations to meet air pollution reduction targets. Last but not least, some feasible policies are proposed by analyzing the potential economic benefit of reducing air pollution and GHG emissions in terms of improved quality of life and environmental conservation. We argue that these benefits could offset the lower GRP growth obtained by the proposed policy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".