Identifying Water–Energy–Carbon Links in Urban Water Sectors: A Case Study of Incheon Metropolitan City, Republic of Korea
Bibliographic record
Abstract
Water and energy are essential resources for human life, and carbon emissions (CEs) occur in tandem with their use. Thus, water, energy, and carbon are closely inter-related. Approximately 4% of the global energy is used in urban water sectors (UWSs), which encompass various processes such as water intake, treatment, and distribution and wastewater collection and treatment, all of which consume significant energy and emit CO2. Several countries are actively working toward achieving carbon neutrality by 2050–2060. Therefore, increasing energy efficiency and reducing CEs through comprehensive evaluations of UWSs is essential. This study aimed to quantify energy consumption and CEs in UWSs and proposed a methodology for analyzing water–energy–carbon (WEC) links at the city level. By applying it to Incheon Metropolitan City (IMC), we first identified the UWSs and established a WEC database. Based on this database, the WEC consumption and emissions were analyzed by process or administrative district, and visualizations using Sankey diagrams and Geographic Information System Mapping were created to enhance their understandability. In 2021, the UWSs in IMC consumed 308,496,107 kWh of energy, representing 32.7% of the public electricity consumption of IMC, with an average energy intensity of 0.46 and 0.38 kWh/m³ for water supply systems (WSSs) and sewerage systems (SSs), respectively. Their carbon emissions totaled 315,765,358 kg CO2, accounting for 2.7% of IMC’s total carbon emissions, with an average carbon intensity of 0.21 and 0.58 kg CO2/m³ for WSSs and SSs, respectively. The proposed methodology was used to comprehensively evaluate WEC consumption and emissions in IMC. It is expected to enable relevant stakeholders to develop measures, such as water reuse and increasing renewable energy usage in water treatment and wastewater treatment plants, to build sustainable UWSs.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".