RQ1: Basin Prioritization — Fuzzy BWM + Fuzzy TOPSIS

Multi-criteria prioritization of Turkish hydrological basins for carbon-financed drip irrigation programs. Criterion weights via Fuzzy Best-Worst Method; basin ranking via Fuzzy TOPSIS.

Program Scenario: Surface drip irrigation · N₂O emission reduction · SOC sequestration credited · Sugar beet compatible
Research Design Overview

Step 1 — Pre-Screening

Hard filters applied to all 26 DSİ hydrological basins. Basins eliminated if: drip adoption already >70% (low additionality), irrigated area too small for program scale, or technically incompatible (e.g. rice-dominant). Reduces the pool to analytically comparable candidates.

Step 2 — Criterion Finalization

15 criteria selected through stakeholder interviews, literature review, and expert consultation. Criteria span infrastructure readiness, agronomic compatibility, farmer behavioral readiness, market conditions, and program logistics. 5 criteria use expert rating (1–7 linguistic scale); 10 use secondary data with researcher-based classification (1–5 scale). Each criterion is justified and operationalized with a defined data source.

Step 3 — Fuzzy BWM (Criterion Weights)

Best-Worst Method (Guo & Zhao, 2017) with fuzzy linguistic comparisons. Expert panel (n=5–7) identifies best and worst criteria, then compares others pairwise. Yields criterion weights with explicit consistency check (ξ*). Three weight profiles: one per scenario.

Step 4 — Fuzzy TOPSIS (Basin Ranking)

Technique for Order Preference by Similarity to Ideal Solution. All 9 criteria use a 1–5 linguistic scale (VL/L/M/H/VH → TFN5). Weighted normalized decision matrix compared against Fuzzy Positive Ideal Solution (FPIS) and Fuzzy Negative Ideal Solution (FNIS). Closeness coefficient (CC) determines ranking.

Step 5 — Sensitivity Analysis

Two approaches: (1) Weight perturbation ±10%, ±20% to test ranking stability; (2) Criterion exclusion — toggling off specific criteria tests how sensitive the ranking is to each dimension (e.g., removing tillage-related criteria, lab distance, or social co-benefits). Basins robust across all conditions are identified as priority candidates.

Step 6 — Scenario Comparison

Three scenarios compared side-by-side: A (drip + N₂O + SOC), B (drip + N₂O only), C (SDI + N₂O + SOC). Key differences arise from criterion weights — SOC weight rises from B→A→C; tillage willingness weighted ~2× higher in C (structural commitment). Basins appearing in top tiers across all three scenarios are the strongest program candidates.

Three Program Scenarios

Scenario A — drip + N₂O + SOC

  • Surface drip tape installed seasonally, removed before harvest/tillage
  • Compatible with all row crops incl. sugar beet
  • Carbon methodology: N₂O reduction + SOC sequestration (VM0042)
  • SOC credited — reduced tillage practices monitored
  • Tillage willingness: moderate weight (behavioral commitment)
  • Lower upfront investment; broader crop compatibility

Scenario B — drip + N₂O only

  • Surface drip tape installed seasonally, removed before harvest/tillage
  • Compatible with all row crops incl. sugar beet
  • Carbon methodology: N₂O emission reduction only (VM0042)
  • SOC not credited — tillage is not structurally prevented
  • Tillage willingness: lower weight (behavioral, not structural)
  • Simplest carbon accounting; lowest barrier to entry

Scenario C — SDI + N₂O + SOC

  • Subsurface drip at 30–45 cm depth, permanent installation
  • Incompatible with deep-harvest crops (sugar beet, peanut)
  • Carbon methodology: N₂O + Soil Organic Carbon (VM0042)
  • SOC accumulation enabled by structural no-tillage
  • Tillage willingness: highest weight (structural commitment)
  • Higher investment, longer payback, greatest carbon upside
Criterion List & Operationalization
Note: All scores in the interactive model are preliminary placeholders based on secondary data and interview insights. Final scores will be determined through expert surveys (Fuzzy BWM) and data collection per the defined sources below.
#CriterionTypeMeasurementData Source Wt AWt BWt C
BWM — Criterion Importance

Select the most and least important criteria, then rate all others relative to the best (1 = equally important, 9 = far less important). Click Apply to recompute rankings.

Current Weights
Basin Prioritization Map Scenario A
Ranking:
High priority
Medium
Low priority
Pre-screened out
Fuzzy TOPSIS Ranking
RankBasinProvinces CC Scored⁺d⁻
Criteria Exclusion Test

Toggle criteria on/off to simulate different analytical scenarios. Try removing expert-only criteria, behavioral criteria, or logistics criteria to see which dimensions drive the ranking.

Weight Perturbation
Applies random ±% perturbation to each weight, then re-normalizes
Ranking Comparison: Baseline vs. Current Settings
Basin Baseline Rank Baseline CC Current Rank Current CC Change
How this works: Enter raw values for quantitative criteria — the system classifies them to 1–5 based on defined thresholds. For researcher-classified criteria, enter your 1–5 directly. Expert-rated criteria use 1–7 (you rate based on interview insights; these will later be replaced by actual expert survey responses). Click Apply & View Rankings when done to update the map and rankings.
◈ Quantitative Criteria — Raw Data Entry Auto-classified to 1–5 based on thresholds
Energy: OKSURE 2024 outage minutes/year per province. Water Stress: WRI Aqueduct 4.0 Water Stress indicator (0–5 scale, 2030 BAU). Lab Distance: km from basin centroid to nearest ISO 17025 accredited GC lab.
Thresholds — Energy (min/yr): <200=5, 200–600=4, 600–1200=3, 1200–2400=2, >2400=1  |  Water Stress (0–5): <1=1, 1–2=2, 2–3=3, 3–4=4, >4=5  |  Lab Distance (km): <200=5, 200–350=4, 350–500=3, 500–700=2, >700=1
◉ Researcher-Classified Criteria — 1–5 Score Entry VL=1 · L=2 · M=3 · H=4 · VH=5
★ Expert-Rated Criteria — 1–7 Score Entry VL=1 · L=2 · ML=3 · M=4 · MH=5 · H=6 · VH=7
Placeholder values (4=M) shown. These will be replaced with actual expert survey responses from Fuzzy BWM elicitation.