Obviously Sustainable (OS) is a social enterprise headquartered in Jakarta, Indonesia. Our mission is to advance a people-centred twin transition; encompassing both climate and digital transformation. Founded during the COVID-19 global pandemic, we witnessed first-hand the fragility of existing systems and the widening deficit in development financing, while simultaneously observing an unprecedented acceleration in technological innovation.
The Skills Mission Accelerator
The Skills Mission Accelerator is our flagship initiative for 2026, dedicated to closing the skills gap and championing equality in a workforce landscape increasingly shaped by the twin transition.
As industries transform, millions of workers face displacement while entirely new roles emerge, which often requiring skills that do not yet exist in the current workforce. Our mission is to build a global skills ecosystem and mobilise a diverse coalition of actors: investors, policymakers, intellectuals, and civil society organisations (CSOs), through evidence-based insights to close the inequality gap in the transition economy.
The Skills Mission Accelerator comprises two pillars: the Global Skills Repository (GSR) and ReSkillU.
Global Skills Repository (GSR)
The GSR is an AI-powered platform that provides multistakeholder actors with job market and skills intelligence on the transition economy. The platform forms independent multistakeholder actors that drives better governance for workforce reskilling.
ReSkillU
ReSkillU is a tailor-made reskilling platform designed to help transitioning industries upskill and reskill their workforce. ReSkillU has been recognised as a London School of Economics and Political Science (LSE) Generate product, winning second place at the LSE Generate Ideation Fellowship 2026.
Contact Us
Get in touch with our team. We welcome inquiries about the Global Skills Repository, partnership opportunities, and the twin transition skills landscape.
Our Office
Obviously Sustainable
Karya Positif Berkelanjutan
SMESCO Indonesia Floor 6
Gatot Subroto Rd. No. 94
South Jakarta, Indonesia 12780
Building the world's first open-source skills intelligence for the transition economy
2026
Throughout 2026
AI System Development, Training & Finalisation
Developing, training, and refining the AI classification engine to enable automated analysis of job roles, skills demand, and transition readiness across sectors.
Build
Throughout 2026
10,000 Job Roles Collected & Analysed
Collecting and classifying 10,000 job roles across ASEAN to establish the foundational dataset for transition economy workforce intelligence.
Data
Throughout 2026
Dashboard Building
Designing the interactive GRS dashboard as a real-time skills intelligence platform with visual insights on job market transitions and reskilling pathways.
Build
Throughout 2026
Flagship Reports in Development
Preparing three flagship reports: Investor Coalitions, Skills Gap Analysis, and Skills-to-Pay Transparency, each targeting key global policy moments in 2027.
Reports
Throughout 2026
Fundraising & Partnerships
Securing financial support and strategic partnerships with investors, development organisations, and multilateral institutions to sustain and scale GRS.
Build
2027
Q1 · Bangkok, Thailand
Official Launch of the GSR
Launching at the Asia-Pacific Forum on Sustainable Development 2027 to policymakers, UN agencies, and regional stakeholders.
Launch
Q3 · ASEAN Business & Investment Summit
Financial Systems & Investor Coalitions Report
Flagship report on investor coalitions mobilising capital for workforce transformation. Research period: August 2026 – June 2027.
Report
September · UNGA, New York
Skills-to-Pay Transparency Report
Correlating transition skills with compensation benchmarks and policy advocacy for government action on pay transparency.
Report
November · COP32, Addis Ababa
Transition Economy Skills Gap Report
Comprehensive analysis of critical workforce shortages across green and digital sectors with reskilling recommendations for policymakers.
Report
By December 2027
20,000 Job Roles: Expanded Coverage
Scaling to 20,000 classified roles, expanding beyond ASEAN to the broader Asia-Pacific and African regions.
Data
← Scroll to explore the full roadmap →
Our Co-Founders
Rezha Bayu Oktavian Arief
Co-Founder
Co-founder & CEO at Obviously Sustainable & Skills Mission Accelerator
A seasoned professional with 12 years of experience spanning technology and public policy in Indonesia and Southeast Asia. He has held roles at leading organisations including Amazon Web Services, Meta, WhatsApp, and several Indonesian Government Ministries.
His expertise covers public policy, policy communications, regulatory advocacy, and stakeholder engagement.
MSc Social Innovation & Entrepreneurship, London School of Economics and Political Science (LSE), UK.
Brings 10 years of experience in technology, public policy, and ESG across EU and the Asia-Pacific. He has served as a public policy, policy programmes, and ESG expert for Meta/WhatsApp, Grab, Traveloka, Riot Games, the ASEAN Secretariat, and Indonesian Government Ministries.
His core competencies include public policy and regulatory advocacy, policy communications and programme design, stakeholder engagement, and impact assessment.
LL.M Public International Law, Leiden University, the Netherlands (2018). M.PP Public Policy, School of Government and Public Policy, Indonesia (2021).
InveSkills
InveSkills is a financial systems initiative by the Skills Mission Accelerator that bridges the gap between institutional investors and the workforce dimension of the twin transition.
Twin Transition Skills Intelligence Snapshot
1,000
Transition Roles Tracked
70%
Roles Transforming
22%
Entirely New Roles Emerging
500
Energy Sector Roles in Transition
Roles requiring new skills (transforming + emerging)92%
Companies meeting just transition disclosure indicators39%
Companies disclosing training hours or career development46%
Companies disclosing internal hiring and reskilling rates17%
Declining Skills
Paper-Based Documentation
Legacy System Maintenance
Reactive Maintenance
Manual Calculation Methods
Fossil Fuel Power Systems
➔
Emerging Skills Required
Smart Grid Integration
Blockchain for Energy Trading
IoT Sensor Integration
Digital Twin Modeling
Predictive Maintenance (AI)
The Investor Blind Spot
92% of tracked roles require entirely new or significantly updated skills. Yet only 39% of companies satisfy even one just transition disclosure indicator (IGCC, 2024), and fewer than 20% disclose workforce data at the skills level. Only 17% report internal hiring and reskilling rates (JUST Capital, 2025). While 77% of employers plan to reskill workers by 2030 (WEF Future of Jobs, 2025), investors have almost no visibility into whether companies will reskill, lay off and rehire, outsource, or automate. The gap between stated intent and actual disclosure remains vast.
Sources: IGCC Investor Expectations for Corporate Just Transition (2024) · JUST Capital Just Jobs Performance Tracker (2025) · WEF Future of Jobs Report (2025) · Workforce Disclosure Initiative / Thomson Reuters Foundation (2024)
The Investment-Skills Gap
Today, investors managing trillions in assets under management are increasingly demanding that companies in transitioning industries (energy, food systems, technology) demonstrate credible just transition plans. These demands go beyond decarbonisation targets and climate action roadmaps. Investors want to understand what job roles will be transformed and, critically, what skills workers will need to navigate the transition.
Yet most companies lack the granularity to report at the skills level. They can describe their emissions pathways, but not the human capital pathways that make those transitions possible. This is where InveSkills steps in.
The InveSkills Investor Coalition
InveSkills is a coalition of forward-thinking investors that the Skills Mission Accelerator is building: a group of 50 institutional investors committed to pushing companies for detailed skills information across their climate and digital transformation pathways.
Coalition members will collectively advocate for standardised skills disclosure, asking portfolio companies to report on:
Workforce Transition Planning
Which roles are declining, emerging, or transforming within their operations, and what reskilling programmes are in place to support affected workers.
Skills-Level Reporting
Beyond job titles, what specific competencies are being developed, from smart grid integration to digital twin modeling, and how these align with the company's decarbonisation and digitalisation roadmap.
Just Transition Accountability
How companies ensure that the transition does not leave workers behind, including investment in reskilling, community support, and equitable access to emerging roles.
Why InveSkills Matters
Climate action without workforce planning is incomplete. The InveSkills coalition ensures that investors don't just ask "How are you decarbonising?" but also "How are you preparing your people for what comes next?"
By integrating skills intelligence into investment decision-making, InveSkills creates a powerful feedback loop where capital flows drive not only cleaner industries, but more capable, resilient, and future-ready workforces.
Methodology
The Global Skills Repository (GRS) uses a structured AI-driven pipeline to collect, classify, and analyse twin transition job roles across ASEAN. Below is a comprehensive breakdown of how we move from raw job postings to actionable skills intelligence.
Classification Pipeline
🔎
1. COLLECTION
Job boards, LinkedIn, government portals across 10 ASEAN countries
➔
🤖
2. AI CLASSIFICATION
AI system analyses title, description, sector & location
Aggregated into country, sector & skills visualisations
Transition Status Classification
Every job posting is classified into one of four transition statuses based on how the role relates to the twin transition (climate + digital):
TRANSFORMING (70% of roles)
Existing roles undergoing fundamental changes in required skills due to climate and digital transitions. The role itself persists, but its skill requirements are shifting significantly.
Example: A power plant engineer now needs smart grid integration, IoT monitoring, and renewable energy systems knowledge.
EMERGING (22% of roles)
Entirely new roles created by the twin transition that did not previously exist in the labour market. These represent net new job creation.
Example: EV Battery Management Specialist, Carbon Credit Market Analyst, Digital Twin Engineer.
STABLE (6% of roles)
Roles that continue largely unchanged, with minimal impact from the twin transition on required skills or core functions.
Example: Regulatory compliance officers, general project managers in non-transitioning sectors.
DECLINING (2% of roles)
Roles losing relevance as industries shift away from fossil fuels and manual processes. Workers in these roles face displacement risk.
Example: Diesel generator maintenance technician, traditional fossil fuel extraction specialist.
Twin Transition Relevance Score
Each posting receives a transition score from 0.0 to 1.0 measuring its relevance to the twin transition across two dimensions:
🌿
CLIMATE TRANSITION
Renewable energy roles, carbon reduction technologies, sustainable practices, environmental compliance, circular economy, nature-based solutions, clean hydrogen, and energy efficiency.
💻
DIGITAL TRANSITION
AI and automation, IoT sensor networks, blockchain systems, data analytics, smart grid technologies, digital twin modeling, cybersecurity for critical infrastructure, and Industry 4.0.
Below are 3 actual job postings from our database, one each from Indonesia, Malaysia, and Thailand, chosen to show how different roles receive different scores. Notice how the type and volume of emerging vs declining skills drives the final score.
Smart Grid Systems Architect
🇮🇩 Indonesia · Sector: Digital Energy · Status: Transforming
0.95
SCORE
Why this scores high: This role sits at the heart of the twin transition. Smart grid design, IoT sensors, distributed energy management, and cybersecurity for critical infrastructure are all core twin transition competencies. Nearly every skill listed is either newly emerging or rapidly transforming. The role would not have existed in its current form 10 years ago.
Emerging:AI/ML for grid optimizationBlockchain for energy tradingEV grid integrationRenewable forecasting
Declining:Traditional centralized grid managementManual meter reading
Specialist, Business Process Improvements
🇲🇾 Malaysia · Sector: General · Status: Emerging
0.70
SCORE
Why this scores moderate: Process improvement is a traditional discipline built on Lean and Six Sigma. But this role now also requires sustainable process design, carbon footprint analysis, and IoT-based monitoring. It is a clear overlay of green and digital skills onto a conventional foundation. The mix of old and new keeps it in the mid-range, because the core job still relies heavily on established methods.
Emerging:Sustainable process designDigital twin process modelingESG compliance process
Declining:Manual process documentationPaper-based workflow design
A&A Senior Manager, ICS Quality Risk & Regulatory
🇹🇭 Thailand · Sector: Technology · Status: Stable
0.30
SCORE
Why this scores low: This is a conventional risk and regulatory compliance role. The core work, which includes internal controls, quality assurance, and data analysis, has not been fundamentally reshaped by the twin transition. ESG risk assessment and digital audit tools are emerging add-ons, but they do not yet define what this role is about on a day-to-day basis.
Pattern to notice: The higher-scoring role (0.95) has many emerging skills and very few traditional ones, because the job essentially is the twin transition. The mid-range role (0.70) blends old and new, with traditional expertise being enhanced by green and digital skills. The low-scoring role (0.30) is a fundamentally traditional job where twin transition skills are peripheral add-ons rather than core requirements.
How to read this score
Think of the transition score like a thermometer that measures how much a job is affected by climate action and digital technology. The higher the score, the more the role is connected to these changes.
Score 0.0 – 0.3: Low Relevance
The job has little connection to the green or digital shift. It might exist in a transitioning industry but the day-to-day work hasn't changed much. Example: a general office administrator at an energy company.
Score 0.3 – 0.6: Moderate Relevance
The job is starting to feel the impact. Some new skills are needed, but the core of the role is still traditional. Example: a project manager who now needs to understand carbon reporting alongside their usual duties.
Score 0.6 – 0.8: High Relevance
The job is significantly shaped by the twin transition. Workers need a mix of traditional expertise and new green/digital skills. Example: an electrical engineer who now works with smart grids and IoT sensors instead of conventional power systems.
Score 0.8 – 1.0: Core Twin Transition
The job exists because of the twin transition. It wouldn't have been around 10 years ago. Example: a Digital Twin Engineer who builds virtual models of solar farms, or a Carbon Credit Market Analyst.
In simple terms: Our average score across all 1,000 roles is 0.72, which means the typical job in our database is heavily influenced by climate and digital changes. This makes sense — we specifically search for roles in transitioning industries like renewable energy, green construction, and digital infrastructure. A score of 0.72 tells us that most of these roles require workers to learn significant new skills to stay relevant.
Skills Extraction & Categorisation
For each posting, the AI extracts and categorises skills into three layers:
For each classified role, the system identifies reskilling pathways — suggested training and certification programmes that bridge the gap between declining and emerging skills:
Declining Skill
Diesel Generator Maintenance
Reskilling Pathway
Emerging Skill
Smart Grid Integration
Top Recommended Pathways
● Digital energy systems certification programmes
● Data analytics for energy professionals
● Renewable energy project management
● Cybersecurity for critical infrastructure
● Smart grid and energy storage training
● IoT and smart energy systems training
Data Quality & Limitations
1,000
Qualified roles analysed
100%
AI classification coverage
130+
Unique data sources
Classification engine: AI System · Data pipeline: GRS MCP Server · Geographic scope: 10 ASEAN member states · Last updated: March 2026
Skills Balance
The Mismatch Between Sunsetting Skills and Emerging Demand in ASEAN's Twin Transition
39%
Skills Disrupted
by 2030 (WEF)
~4 yrs
Skill Half-Life
down from 10+ yrs
1 in 8
Workers Have Green Skills
globally (LinkedIn)
1 in 5
Green Jobs Unfillable
by 2030 (LinkedIn)
The Skills Mismatch: Sunsetting vs Emerging
Sunsetting Skills (GRS Data)
Paper-Based Documentation33x
Manual Calculation Methods18x
Traditional Fossil Fuel Systems15x
Traditional Advertising Methods10x
Manual Drafting Techniques8x
Legacy System Maintenance7x
Manual Process Management5x
Traditional IT Infrastructure4x
Reactive Maintenance4x
Coal-Fired Plant Operations3x
⇆
Emerging Skills (GRS Data)
Smart Grid Integration60x
Blockchain for Energy Trading28x
Data Analytics for Energy22x
Renewable Energy Integration19x
IoT Sensors for Monitoring18x
Carbon Footprint Assessment16x
ESG Reporting Automation14x
Energy Storage Systems12x
Climate Risk Modeling11x
Circular Economy Principles9x
ASEAN Skills Gap Indicators
Green talent demand growth (2023-24)+11.6%
Green talent supply growth (2023-24)+5.6%
SE Asia employers expecting climate-driven transformation72%
SE Asia employers worried skills gaps will hamper transformation60%
Workers needing reskilling for sustainability roles (up to 6 months)40%
Workers globally needing reskilling by 203059%
Country Skills Readiness (GRS Signal)
Ratio of emerging to declining skills identified per country, based on 1,000 classified roles
Country
Emerging Skills
Declining Skills
Transforming
Readiness Signal
Singapore
35
2
86
Leading
Malaysia
27
0
43
Strong
Myanmar
19
0
14
Emerging-led
Philippines
18
0
50
Moderate
Indonesia
19
0
55
Moderate
Vietnam
6
0
33
Underprepared
Cambodia
3
0
32
Underprepared
Laos
6
0
29
Nascent
Thailand
3
0
62
At Risk
Brunei
4
3
23
At Risk
The Core Mismatch
ASEAN's twin transition is creating a two-speed skills economy. Green talent demand is growing at 2x the rate of supply (11.6% vs 5.6%), while the average skill half-life has collapsed from 10+ years to just 4. In the GRS dataset, 71% of roles are "transforming," meaning they exist today but the skills required are shifting under workers' feet. Countries like Thailand (96% transforming, only 2% emerging) and Brunei (the only country with more declining than emerging skills) face the most acute mismatch: their workforces are being disrupted faster than new skills pipelines can form.
Skill Half-Life Compression (WEF)
1985
10+ yrs
2010
7 yrs
2020
5 yrs
2025
~4 yrs
AI/Tech
~2 yrs
Data Sources
Skills disruption: WEF Future of Jobs Report 2025 (39% of skills disrupted by 2030, 59% of workers needing reskilling). Green talent gap: LinkedIn Global Green Skills Report 2024-25 (demand +11.6%, supply +5.6%, 1 in 5 green jobs unfillable by 2030). ASEAN context: WEF SE Asia regional data (72% employers expect climate transformation, 60% cite skills gaps as barrier). Skill half-life: WEF, Skillable, and Morson Edge research (collapsed from 10+ years to ~4 years; ~2 years in AI/tech fields). GRS micro-data: 1,000 classified twin transition roles across 10 ASEAN countries with skill-level analysis.
Skills Transition Flows
How sunsetting skills map to emerging capabilities across ASEAN's twin transition
Reskilling Pathways: From Declining to Emerging
Declining SkillReskilling PathwayEmerging SkillFlow width = relative frequency in GRS data
Flow Analysis: What the Transitions Reveal
The Paper Trail Extinction
Paper-based documentation (33x) is the single most common declining skill across all 10 ASEAN countries. It feeds into two pathways: Digital Energy Systems and Digital Project Management, ultimately producing demand for Smart Grid Integration (60x) and Blockchain-based systems. This is the widest flow in the diagram, representing the most universal reskilling need.
The IoT Convergence
Three declining skills (Reactive Maintenance, Manual Monitoring, and Legacy Systems) converge into the IoT & Sensor Training pathway (44x), which then fans out to IoT Sensors (79x), Digital Twins (63x), and Predictive AI Maintenance (54x). This represents the largest "many-to-many" transition cluster, where multiple sunsetting roles can access multiple emerging roles through a single reskilling bridge.
The Fossil Fuel Pivot
Workers with fossil fuel systems knowledge (21x declining) and coal plant operations (3x) have a natural pathway through Digital Energy Systems into Smart Grid Integration and Energy Storage. The technical foundation in power systems transfers well, but requires a digital layer. This flow is critical for Indonesia (240K coal workers) and Vietnam (170K coal workers) identified in the Transition Balance.
The Data Lift
Manual Calculation Methods (15x declining) flows directly into Data Analytics Training (21x pathway), which produces demand for Energy Data Analytics (76x emerging). This is the most linear transition: workers who currently do calculations manually need to learn the same logic in digital tools. The reskilling gap is narrower here, making it one of the fastest achievable transitions, estimated at 3-6 months of training.
Transition Volume Summary
274x
Total Declining Skill Mentions (GRS)
235x
Reskilling Pathway Mentions (GRS)
951x
Emerging Skill Mentions (GRS)
Declining (19%)Reskilling (16%)Emerging (65%)
The Reskilling Bottleneck
The Sankey reveals a funnel-shaped transition: 8 major declining skills converge into just 7 reskilling pathways before fanning out to 8 emerging skills. The bottleneck is the training infrastructure. Two pathways, Digital Energy Systems and IoT & Sensor Training, each appear 50 and 45 times respectively in classified roles, acting as the critical bridges for 60% of all reskilling flows. If ASEAN invests in scaling these two training programmes alone, it would address the majority of transition friction for energy sector workers.
Methodology
Flow mapping: Each classified GRS role contains declining skills, emerging skills, and suggested reskilling pathways. Flows are constructed by linking declining skills to pathways cited in the same role profiles, then pathways to the emerging skills they produce. Flow width: Proportional to the frequency (count) of each skill or pathway in the GRS dataset of 1,000 classified roles. Limitations: Flows represent co-occurrence patterns in job postings, not guaranteed reskilling outcomes. Actual transition success depends on training quality, duration, and individual aptitude.
Net Transition Balance
ASEAN Twin Transition: Jobs at Risk vs Emerging Roles
~500K
Fossil Fuel Jobs at Risk
by 2050
+5M
Green Jobs Created
by 2050
+4.5M
Net Positive Balance
SE Asia estimate
10:1
Jobs Created per Job Lost
regional ratio
Regional Transition Balance
Jobs at Risk
Jobs Created
~500K lostNET +4.5M~5M created
Country-Level Risk Indicators
Combining ILO/ADB macro estimates with GRS micro-level data (1,000 classified roles)
Country
GRS Roles
Declining
Emerging
At-Risk Workforce
Net Signal
Indonesia
107
0
15
~240K coal
+ net positive
Singapore
125
1
32
Minimal
++ strong positive
Malaysia
89
0
27
~30K oil & gas
+ net positive
Philippines
81
0
18
~25K coal
+ net positive
Thailand
66
0
1
~45K fossil fuel
~ watch zone
Vietnam
42
0
4
~170K coal
! high risk
Cambodia
35
0
2
~5K energy
~ early stage
Laos
36
0
2
~3K mining
~ early stage
Myanmar
37
0
16
~15K gas
+ net positive
Brunei
34
2
1
~20K oil & gas
! high risk
The Asymmetry Problem
While ASEAN's net transition balance is strongly positive (10 jobs created for every 1 lost), the displacement is geographically concentrated: Vietnam's coal regions, Indonesia's Kalimantan and Sumatra mining belts, and Brunei's O&G sector face acute workforce displacement. The workers losing jobs are rarely the same people gaining new ones, creating a "skills mismatch gap" that requires targeted reskilling investment.
Data Sources & Methodology
Macro estimates: ILO "Just Energy Transition in SE Asia" (2022), ADB "Asia in the Global Transition to Net Zero" (2023), IRENA "Socio-Economic Footprint of the Energy Transition: SE Asia" (2nd ed., 2025). Micro data: GRS database of 1,000 classified twin transition roles across 10 ASEAN countries, with skill-level declining/emerging/transforming classification. At-risk workforce: Estimates drawn from ILO coal phase-out study, IEA World Energy Employment (2023), and national labour ministry data. Figures are indicative and include direct and first-tier indirect employment.
What the GRS Data Tells Us
Of the 1,000 roles analysed: 119 (18%) are emerging and only 5 (0.8%) are declining, while 499 (77%) are transforming, meaning they exist today but require significant reskilling. The transforming category represents the largest policy opportunity: these workers are already employed but need upskilling pathways to remain relevant. Countries like Thailand (96% transforming) and Cambodia (91% transforming) signal urgent reskilling infrastructure needs.
Existing roles that are actively being reshaped by the twin transition, requiring new green or digital skills to perform the same core function.
Top Emerging Skills
Smart Grid Integration137x
Blockchain for Energy Trading100x
Data Analytics for Energy Optimization71x
IoT Sensor Integration56x
Digital Twin Modeling49x
Top Reskilling Pathways
Digital Energy Systems Certification49x
IoT and Sensor Technology Training40x
Digital Energy Systems Certification Programs32x
Top Sectors
Energy and Power280
Technology and Digital97
Manufacturing and Industry40
22%
Emerging (225 roles)
Entirely new roles created by the twin transition that did not previously exist in the job market.
Top Emerging Skills
Carbon Footprint Assessment20x
IoT Sensor Integration17x
Renewable Energy Integration12x
Energy Efficiency Optimization12x
Digital Twin Modeling12x
Top Reskilling Pathways
Green Building Certification Programs6x
Environmental Impact Assessment Certification6x
Digital Environmental Monitoring Certification6x
Top Sectors
Technology and Digital71
Energy and Power28
Built Environment15
6%
Stable (55 roles)
Roles that continue largely unchanged, with minimal impact from the twin transition on required skills or functions.
Most In-Demand Skills
Customer Relationship Management5x
Customer Service4x
Business Development3x
Equipment Maintenance3x
Sales3x
Top Sectors
Technology and Digital19
Energy and Power10
3%
Declining (25 roles)
Roles where demand is shrinking as the twin transition renders their core skills or functions obsolete.
Top Declining Skills
Fossil Fuel Extraction Methods2x
Fossil Fuel Pipeline Welding1x
Oil & Gas Specific Fabrication1x
Traditional Hydrocarbon Construction1x
Fossil Fuel Marketing1x
Reskilling Pathways
Renewable Energy Welding Transition1x
Green Hydrogen Infrastructure Welding1x
Transforming (695)
TransformingExisting roles that are actively being reshaped by the twin transition, requiring new green or digital skills to perform the same core function.
Emerging (225)
EmergingEntirely new roles created by the twin transition that did not previously exist in the job market.
Stable (55)
StableRoles that continue largely unchanged, with minimal impact from the twin transition on required skills or functions.
Declining (25)
DecliningRoles where demand is shrinking as the twin transition renders their core skills or functions obsolete.
