Global Wind Workforce Outlook 2025-2030
Methodology Chapter 7:
This study adopts a qualitatively led research design, supported by quantitative modelling to examine the structure of technician demand in the wind industry. Qualitative inquiry forms the primary evidentiary base, particularly in relation to workforce planning and workforce preparedness. These insights establish the conceptual framework for the analysis and inform the assumptions embedded in the quantitative model.
To enhance the accuracy and reliability of this model, we have identified a set of key drivers and variables that most strongly influence FTE demand. Below is acategorised overview of the variables we consider most significant. Not all relevant variables are included, due to practical constraints such as the unavailability of structured data. Among the drivers we identified, those related to schedule and work- breakdown-structure elements (e.g., task sequencing, task overlaps,
and the use of parallel crews) are the most difficult to quantify due to limited availability of structured data. For this publication and for our modelling approach, we therefore rely primarily on the turbines-and-capacity outlook, complemented by insights on major project tasks, labour-hour estimates, job roles, and crew configurations gathered through interviews with subject-matter experts.
Workforce Modelling GWO maintains a workforce
The research design integrates three components:
forecast model that estimates the FTE requirements for wind turbine installation and maintenance.
1. quantitative modelling, using GWO’s workforce forecast model to test and refine assumptions about Full-Time Equivalent (FTE) labour demand 2. Qualitative mapping of wind project development structures. 3. Semi-structured stakeholder interviews, guided by hypotheses concerning organisational workforce planning and competence management practices
Table 2: Variables Influencing Technician Demand
Project Characteristics
Project Scope & Work Breakdown
Team Composition
Schedule Parameters
• Number of turbines • Turbine rating (MW) • OEM type / design (affects task breakdown) • Site location (can influence logistics/productivity)
• Task list by phase • Standard labour hours per task (by role)
• Job roles involved • Crew configuration (how many people per role per task) • Standard productivity rates (turbines/year)
• Timeline (start–end) • Valid workdays (accounting for weather, access) • Number of parallel crews or teams • Sequencing or overlapping of tasks
Note: Grey-shaded text denotes factors hypothesised to have potential influence. These factors were not included in the current model due to incomplete data.
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