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CLC/TS 50600-5-1 · Energy Management & Environmental Sustainability

CLC/TS 50600-5-1 is the European standard defining a Data Centre Maturity Model (DCMM) for energy management and environmental sustainability. Published by CENELEC and updated in 2023, it provides a structured framework for data centre operators to assess their current practices, identify improvement opportunities, and progress toward higher levels of efficiency, in alignment with the EU Green Deal and the EU Code of Conduct for Data Centre Energy Efficiency.

Key Assessment Domains

⚡Energy Performance & Efficiency

📊Monitoring & Measurement Capability

⚙️Operational Processes & Documentation

♻️Continuous Improvement & Sustainability

Five Maturity Levels

Level	Name	Key Characteristics
1	Initial	Data collection minimal or absent. No formal KPIs. Entirely reactive management — issues addressed only after they occur. No documented processes for energy or sustainability.
2	Managed	Basic KPIs (e.g. PUE) measured. Partial monitoring in place. Mostly manual reporting. Some awareness of energy patterns, but no structured improvement plan. Aligned with EU CoC baseline.
3	Defined	Standardised KPI sets (PUE, ERF, WUE) actively used. Processes formally documented. Data collected on a regular schedule. Responsibilities clearly assigned. Targets set and reviewed periodically.
4	Quantitatively Managed	Automated data collection via DCIM/SCADA. Decisions driven by KPI dashboards and trend analysis. Benchmarking performed. Waste heat reuse in place. Carbon & water metrics (CUE, WUE) tracked.
5	Optimizing	Real-time optimisation via AI/ML models and digital twins. Continuous improvement embedded in operations. Full circular economy principles. External waste heat distribution. Full alignment with EU taxonomy & Green Deal.

Note: The DCMM is applicable to all data centres regardless of size or type. Partial progress is recognised at each level. Certification can be obtained through accredited bodies such as TÜV SÜD or TÜV NORD under the TSI.EN50600 scheme.

Assessment Criteria by Level

Level	Key Requirements
1 · Initial	No formal energy management programme. No KPI tracking. No dedicated roles for sustainability. Energy bills reviewed at most annually.
2 · Managed	At least 1 KPI (e.g. PUE) measured regularly. Basic metering infrastructure in place. Annual energy report produced. At least one person responsible for energy management.
3 · Defined	Minimum 3 KPIs measured and reported on a defined schedule. Documented energy management policy. Targets set with review cycles. Data retained for trend analysis (≥12 months).
4 · Quantitatively Managed	Automated KPI collection (DCIM/SCADA/BMS). Real-time dashboards operational. Benchmarking against external data centres. Waste heat reuse documented. Carbon & water KPIs tracked. Formal improvement roadmap in place.
5 · Optimizing	AI/ML-driven optimisation active. All major KPIs automated and integrated. External waste heat distribution to third parties. Full lifecycle & circular economy assessment. Annual third-party sustainability audit. Aligned with EU taxonomy disclosures.

KPI Coverage by Maturity Level

KPI	L1	L2	L3	L4	L5
PUE — Power Usage Effectiveness	✗	✔	✔	✔	✔
COP — Coefficient of Performance	✗	~	✔	✔	✔
RTI — Return Temperature Index	✗	✗	~	✔	✔
RHI — Rack Heat Index	✗	✗	~	✔	✔
RCI — Rack Cooling Index	✗	✗	~	✔	✔
ERF — Energy Reuse Factor	✗	✗	~	✔	✔
WUE — Water Usage Effectiveness	✗	✗	✗	~	✔
CUE — Carbon Usage Effectiveness	✗	✗	✗	~	✔
REF — Renewable Energy Factor	✗	✗	✗	~	✔
PES — Primary Energy Savings	✗	✗	✗	~	✔

✔ Required · ~ Optional/Expected · ✗ Not applicable at this level

KPI	Full Name	Formula	Target Range	Ideal
RTI	Return Temp Index	RTI = (T_return − T_supply) / (T_out − T_in)	95% ≤ RTI ≤ 105%	100%
RHI	Rack Heat Index	RHI = (T_in − T_supply) / (T_return − T_supply)	0.8 ≤ RHI	1.0
RCI_HI	Rack Cooling Index High	RCI_HI = [1 − Σ(T_i − T_max) / (n · (T_max,all − T_max))] × 100	96% ≤ RCI_HI ≤ 104%	100%
RCI_LO	Rack Cooling Index Low	RCI_LO = [1 − Σ(T_min − T_i) / (n · (T_min − T_min,all))] × 100	96% ≤ RCI_LO ≤ 104%	100%
RI	Recirculation Index	RI = [1 − (T̄_rack,inlet − T_supply) / T_supply] × 100	≈ 100%	100%
LI	Leakage Index	LI = [1 − (T̄_rack,outlet − T_return) / T̄_rack,outlet] × 100	≈ 100%	100%
PUE	Power Usage Effectiveness	PUE = E_DC / E_IT	1.0 ≤ PUE ≤ 2.0	1.0
CoP	Coefficient of Performance	CoP = 1 / (PUE − 1)	—	∞
ERF	Energy Reuse Factor	ERF = E_REUSE / E_IT	0.5 ≤ ERF ≤ 1.0	1.0
REF	Renewable Energy Factor	REF = E_RES-TOT / E_IT	0.6 ≤ REF ≤ 1.0	1.0
PES	Primary Energy Savings	PES = E_DC − E_IT	—	0
WUE	Water Usage Effectiveness	WUE = W_site / E_IT	0 ≤ WUE ≤ 1.8	0
REF	Renewable Energy Factor	REF = E_RES / E_IT	0.6 ≤ REF ≤ 1.0	1.0
CUE	Carbon Usage Effectiveness	CUE = PUE × CF_grid	—	0
ITEUsv	IT Equipment Utilization for Servers	ITEUsv = Active CPU Utilization / Total Capacity	60–100%	100%
ITEEsc	IT Equipment Energy Efficiency for Servers	ITEEsc = ITEUsv / PUE	—	Max