South Korean Household’s Willingness to Pay for Replacing Coal with Natural Gas? A View from CO2 Emissions Reduction
Bibliographic record
Abstract
Coal is currently a major electric power generation source in South Korea when considering that forty-three percent of electricity that was generated during the first quarter of 2017 came from coal. The amount of CO2 emissions from coal-fired power plant is two times larger than that from natural gas (NG)-fired power plant in the country. In this regard, the Korean government is trying to replace some amount of coal that is used for generation with NG to reduce CO2 emissions. However, the cost of NG-fired generation is about 1.25 times higher than that of coal-fired generation. Thus, the policy-makers demand information about the household willingness to pay (WTP) for the replacement to mitigate CO2 emissions. This paper applies the contingent valuation (CV) approach, and assesses the household’s WTP for replacing one kWh of coal-fired power with that of NG-fired power. For this purpose, a total of 1000 South Korean households were involved in the CV survey employing a dichotomous choice question. In addition to the current electricity price, KRW 121.52 (USD 0.11) per kWh, the respondents were willing to pay KRW 25.35 (USD 0.02) per kWh. The costs of NG-fired and coal-fired generation are KRW 100.13 and 78.05, respectively, per kWh. The difference between the two is KRW 22.08 per kWh, which is smaller than the mean additional WTP (KRW 25.35 per kWh). The household’s additional WTP is bigger than the actual additional cost. It is concluded that the switch of power generation source from coal to NG to reduce CO2 emissions can be supported by South Korean households.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".