Declaration-Ready Climate-Neutral PEDs: Budget-Based, Hourly LCA Including Mobility and Flexibility

Abstract

In recent years, Positive Energy Districts (PEDs) have been interpreted in many—and often conflicting—ways. We recast PEDs as a vehicle for verifiable climate neutrality and present a declaration-ready assessment that integrates (i) a cumulative, science-based GHG budget per m² gross floor area (GFA), (ii) full life-cycle accounting, and (iii) time-resolved conversion factors that include everyday motorized individual mobility and quantify flexibility. Two KPIs anchor the framework: the cumulative GHG LCA balance (2025–2075) against a maximum compliant budget of 320 kg_CO2e·m⁻²_GFA and the annual primary energy balance used to declare PED status with or without mobility. We follow EN 15978 and apply time-resolved emission factors that decline to zero by 2050. Its applicability is demonstrated on six Austrian districts spanning new builds and renovations, diverse energy systems, densities, and mobility contexts. The baseline scenarios show heterogeneous outcomes—only two out of six meet both the cumulative GHG budget and the positive primary energy balance—but design iterations indicate that all six districts can reach the targets with realistic, ambitious packages (e.g., high energy efficiency and flexibility, local renewables, ecological building materials, BESS/V2G, and mobility electrification). Hourly emission factors and flexibility signals can lower import-weighted emission intensity versus monthly or annual factors by up to 15% and reveal seasonal import–export asymmetries. Built on transparent, auditable rules and open tooling, this framework both diagnoses performance gaps and maps credible pathways to compliance—steering PED design away from project-specific targets toward verifiable climate neutrality. It now serves as the basis for the national labeling/declaration scheme klimaaktiv “Climate-Neutral Positive Energy Districts”.

1. Introduction

Achieving climate neutrality by mid-century, as stipulated under the Paris Agreement [1], requires rapid and deep decarbonization across all sectors. The IPCC Sixth Assessment Report confirms that limiting warming to 1.5 °C necessitates stringent carbon budgets, with global net-zero CO₂ emissions reached around 2050 [2,3,4,5]. The building sector is pivotal in this transition, contributing significantly to both operational greenhouse gas (GHG) emissions and embodied emissions from construction and refurbishment [6].

Over the past decade, ref. [7] have identified more than 25 different concept names for climate-friendly or energy-positive districts or neighborhoods. Most of these approaches emphasize improved efficiency and renewable integration at the energy-system level but vary widely in scope and lack consistent definitions—a critique common to many studies. As a result, many remain difficult to apply in practice, are hard to replicate, and have seen limited regulatory uptake. Among this broad landscape, four framings are of particular interest for this study: NZEB definitions, their extensions into renewable energy communities, the Zero-Emission Neighborhood (ZEN) framework, and Positive Energy Districts (PEDs). Each of these has shaped the discourse on carbon-neutral or energy-positive districts, but all remain closely tied to annual balances or ambition levels rather than finite, Paris-aligned carbon budgets:

• NZEB and Net-Zero Definitions. Classic NZEB frameworks focus on annual site/source primary energy balances, with variants on cost and emissions neutrality [8]. Their common core is a 12-month energy bookkeeping, sometimes coupled to EPB standards (EN/ISO 52000-1) for primary energy weighting [9].
• NZEBs in Renewable Energy Communities (RECs) and energy sharing. Recent work extends NZEB logic to building-to-building exchange, where prosumer surpluses buffer consumer deficits inside a REC [10]. While this can improve local self-consumption and grid friendliness, it remains annual-balance driven and typically does not enforce finite, Paris-compatible CO₂ budgets.
• Zero-Emission Neighborhoods (ZEN). The Norwegian ZEN program defines neighborhood-scale goals “towards zero life-cycle GHG emissions,” explicitly emphasizing life-cycle accounting, high energy efficiency, high shares of local renewables, and smart/flexible operation. ZEN provides ambition levels and guidance but leaves budget allocation and declaration rules to localization [11]
• Positive Energy Districts (PEDs). IEA EBC Annex 83 positions PEDs as districts with net annual energy surplus and operational flexibility. Many PED implementations, however, still report annual net balances without translating them into cumulative, per-m² CO₂ budgets aligned with Paris [12].

Positive Energy Districts (PEDs) are increasingly promoted as a transformative model to support this transition, integrating high energy efficiency, local renewable generation, and energy flexibility to achieve an annual net-positive energy balance [13,14,15,16].

The PED Database compiled well over 50 projects on a self-reporting basis and showed that PED projects across Europe differ widely in energy performance, approach, and ambition and considered system boundaries [17]. This highlights the need for further clarification of the definition of PEDs, especially taking into account the specific context of the district (see, e.g., [18]). The increasing integration of volatile renewable energy (VRE) could significantly benefit from widespread demand-side response [19], but many actors in the field consider the assessment of energy flexibility in the context of a PED an important, yet unresolved question [20].

By contrast, climate neutrality requires aligning emissions with a finite, science-based GHG budget that limits global warming to 1.5 °C (or well below 2 °C), necessitating a lifecycle-based approach that captures embodied emissions, biogenic carbon flows, and time-resolved operational emissions [6]. Additionally, ref. [21] point out that low or zero carbon buildings require clear decarbonization targets as early as in the design phase.

Recent work has also examined the broader challenges of implementing energy-efficient and climate-neutral districts. For instance, ref. [22] identify governance structures, financing models, and integration of building- and district-scale technologies as critical enablers of high-performance districts. While their focus is on efficiency strategies, the findings underscore that achieving Paris-compatible Positive Energy Districts requires not only carbon-budget compliance, as proposed here, but also systemic strategies to overcome institutional and financial barriers.

At the PED scale, three main challenges emerge:

• Defining system boundaries that distinguish emissions under the direct control of developers from those managed at municipal or national scales [14,23,24,25].
• Allocating GHG budgets consistently with global and national targets [4,26,27].
• Accounting for temporal aspects, including the disproportionate climate impact of early emissions [6,28].

Life cycle assessment (LCA) standards, particularly EN 15978:2024 [29], provide a robust structure for defining system boundaries and modules for embodied and operational emissions in buildings and districts. Previous work [30,31,32] highlights the need to adapt these methods to the district scale while ensuring compatibility with sectoral carbon budgets. Dynamic emission factors—which reflect the projected decline in grid and district heating carbon intensities—are essential for accurately representing operational impacts over time [33]. International studies [34,35] demonstrate the influence of such temporal factors on lifecycle results, and highlight differences in the treatment of embodied emissions of energy infrastructure.

Novelty and Contribution of This Study

This study extends the established Positive Energy District (PED) assessment framework [24,36] by operationalizing climate neutrality through life-cycle carbon accounting and science-based carbon budgets. It (i) applies a life cycle GHG accounting method consistent with national decarbonization pathways, (ii) incorporates time-differentiated operational emission factors for multiple energy carriers, and (iii) integrates everyday motorized individual mobility as part of project-level design.

Building on EN 15978 system boundaries, it integrates dynamic, energy-carrier-specific emission factors and operationally includes everyday motorized individual mobility (EMIM) and embodied emissions from construction and maintenance from now until 2050. Within the broader assessment framework, this paper addresses the climate neutrality and life-cycle carbon accounting dimension, complementing the framework’s energy system modeling and flexibility evaluation capabilities presented in earlier work [24,36]. The shared objective is to provide a methodologically rigorous yet practice-ready approach that captures both (i) the operational responsiveness of PEDs to system needs and (ii) their compliance with carbon budgets consistent with Austria’s 2040 climate neutrality target.

The framework presented here is designed to be robust for research purposes—allowing sensitivity analyses with hourly emission factors—while remaining compatible with the monthly conversion factors of the Austrian building code for practical declarations of climate-neutral PEDs as part of the Austrian sustainability label family klimaaktiv [37]. Its core contribution is the derivation of a quantifiable carbon budget per square meter of gross floor area (GFA), integrating both operational and embodied emissions in a manner that is directly linkable to the performance outcomes of the broader PED assessment framework.

The novelty of this work consists of these three parts:

• The integration of operational, embodied, and mobility energy and emissions into a combined assessment system with quantitative target budgets
• The assembly of these factors into a nationally applicable declaration system.
• The demonstration of achievability of this declaration for a diverse range of ambitious districts.

2. Materials and Methods

The methods build on previous work on primary energy-based PED definitions and extend them with a life-cycle, budget-oriented approach. To structure the framework, the development process and the subsequent application are separated. The development side describes the design choices, boundaries, and assumptions that define the assessment logic, while the application side illustrates how the framework is implemented using project data and simulation tools. This dual view ensures the transparency of our methodological decisions and shows how the framework translates into declaration-ready results. Figure 1 provides a schematic overview of the framework development and application.

2.1. Methodological Principles and Declaration Goals

The proposed climate neutrality assessment framework is grounded in a set of guiding principles that combine practical applicability with scientific rigor, ensuring relevance for both practitioners and researchers.

• Practical applicability and stakeholder relevance—The method is designed to be appliable for real estate developers, municipalities, and planning professionals, enabling its integration into certification schemes and project assessments [38,39]. Its scope is limited to emissions that can be directly influenced through planning and construction decisions—namely, operational energy use, EMIM, and embodied emissions—ensuring decision-making relevance.
• Integrated and adaptable system boundaries—Assessment boundaries extend beyond individual buildings to the full Positive Energy District (PED) system, covering on-site generation, storage, flexibility, and interconnections with surrounding grids. The framework retains adaptability in data sources, emission factors, and system configurations, allowing regional customization while ensuring methodological comparability.
• Temporal differentiation—Emissions are assessed over a 50-year timeframe (2025–2075), with the assumption that climate neutrality is achieved for all services by 2050. As a result, all conversion factors for both operational and embodied energy are assumed to be zero by 2050, reflecting the expected decarbonization of energy carriers and building systems. This approach ensures alignment with long-term climate goals while considering the gradual reduction in emissions over the assessment period.
• Science-based, budget-oriented compliance thresholds—The method is anchored in downscaled CO₂ budgets consistent with Austria’s 2040 climate neutrality target and the 1.5 °C global carbon budget [1]. It establishes a hierarchy of compliance thresholds: an ambitious target value representing a precautionary level for high-confidence Paris alignment, and a maximum compliance limit that must not be exceeded for a project to qualify as “climate-neutral PED.”

These principles acknowledge that PED definitions inherently allow for multiple interpretations, particularly regarding system boundaries, included emission sources, and treatment of temporal dynamics. As noted in prior work [24,40], these degrees of freedom can lead to inconsistent assessments if not addressed explicitly. The framework therefore applies a design thinking approach to close such gaps: key methodological choices—such as which life cycle stages to include, how to allocate renewable generation, and how to integrate mobility—are made transparent, justified against policy and scientific references, and consistently applied. Figure 2 summarizes the methodological approach:

The following section details how these methodological principles are operationalized within the proposed framework, from KPI definition and the derivation of target carbon budgets to the application of dynamic emission factors and the inclusion of mobility and embodied emissions.

2.2. Key Performance Indicators (KPIs) and PED Levels

We base our assessment framework on two main indicators as defined in [24]:

• Cumulative GHG balance (2025–2075, kg_CO2e·m⁻²_GFA)—as defined in the framework above: cradle-to-grave embodied emissions (A1–A3; buildings, TES, PV, batteries, vehicles, C1–C4 before 2050), time-resolved operational emissions (B6; grid electricity and thermal carriers), operational mobility (B8 EMIM; ICE/EV), and avoided grid emissions (D2; PV exports and EMIM substitution). Negative components reduce the balance. Compliance bands are L3 = 320, L2 = 196, L1 = 72 kg_CO2e·m⁻²_GFA.
• Primary energy balance (kWh_pe·m⁻² _NFA·a⁻¹)—computed per [24,36] as the difference between annual primary energy grid equivalence from local renewable supply and flexible grid use (PV self-use and feed-in, flexible TES and EMIM operation) and the district’s primary energy demand (grid equivalence, HVAC and electricity; EMIM included or excluded depending on the level). A positive value denotes a “positive balance”. Context factors for density, mobility and renovation may apply as virtual demands or supplies.

These two KPIs are reported consistently across case studies; figures annotate overshoot/undershoot against L-levels, and tables list the corresponding PE balances. Operational energy KPIs are expressed per conditioned Net Floor Area (NFA) because heating, cooling, and end-use electricity demands are specified for conditioned area in design practice and codes, so NFA best matches how these targets are set and tracked. Lifecycle GHG indicators are expressed per Gross Floor Area (GFA) to maintain comparability across typologies and density/fit-out choices; NFA↔GFA conversions are straightforward via use-type factors that are routinely applied (and refined from early planning estimates). Based on these indicators, we distinguish three PED levels as shown in

Table 1. Types of PED as defined in [24].

Type of PED	Acronym	Requirements
Positive Energy District	PED	PE Balance > 0 excl. EMIM
Positive Energy District with Mobility	PED + M	PE Balance > 0 incl. EMIM
Climate-Neutral Positive Energy District (reported in this study)	CN PED	PE Balance > 0 incl. EMIM GHG LCA ≤ L3 over 2025–2075

Results are compiled with the scenario analysis tools provided by the framework (publicly available here [41]).

:

2.3. Carbon Budget for Climate-Neutral Districts

District-level carbon budgets are derived by downscaling a finite, Paris-compatible global CO₂ budget to Austria and further allocating a sectoral share to buildings, including construction, operation, and everyday motorized mobility (EMIM). This ensures consistency between global climate targets and district-scale declarations.

The calculation follows three steps. First, we adopt the +1.5 °C scenario with 66% probability of success, both with and without temporary temperature overshoot, and subtract materialized national emissions (2022–2024) from the 2022–2075 budget to obtain the available 2025–2075 budget. Second, we allocate a fixed share of the national budget (50%) to buildings and EMIM, expressed on a per capita basis. Finally, per capita values are translated into gross floor area (GFA)-specific thresholds using projected Austrian floor space statistics for 2050 (906.7 million m²).

Natural carbon sequestration is included as a sensitivity, with additional sinks of 0.5 and 1.0 t_CO2e cap⁻¹ a⁻¹. These variants yield three levels of compliance:

• L1 (Paris-compatible target): stringent limit without sequestration,
• L2 (precautionary limit): moderate sequestration (0.5 t_CO2e cap⁻¹ a⁻¹),
• L3 (absolute compliance limit): generous sequestration (1.0 t_CO2e cap⁻¹ a⁻¹).

• Intermediate values and assumptions underlying this derivation are documented in Appendix A 

  Table A1. Input parameters and intermediate calculation values used to derive the floor-area-specific carbon budget limits: global budgets, national allocation, population, floor area statistics. The resulting thresholds are summarized in the main text (Table 2).

  Variable	Unit	Timeframe	50%	66%	Source
  Paris-compatible CO2 budget (+1.5 °C, no overshoot)	Mt CO2	2022–2075	510	280	[27]
  Paris-compatible CO2 budget (+1.5 °C, with overshoot)	Mt CO2	2022–2075	610	340	[27]
  Remaining budget after 2024 (no overshoot)	Mt CO2	2025–2075	360	130	calc. from [27]
  Remaining budget after 2024 (with overshoot)	Mt CO2	2025–2075	460	190	calc. from [27]
  Population (Austria)	million cap	2025	9.0	9.0	[27]
  Per capita net budget (no overshoot)	t CO2 cap−1	2025–2075	40.0	14.5	calc. from [63]
  Per capita net budget (with overshoot)	t CO2 cap−1	2025–2075	51.1	21.1	calc. from [63]
  Natural carbon sinks (NCS)	t CO2 cap−1a−1	—	1.0	1.0	[64]
  Cumulative NCS added (50 years)	t CO2 cap−1	2025–2075	50.0	50.0	—
  Per capita gross budget (no overshoot + NCS)	t CO2 cap−1	2025–2075	90.0	64.5	—
  Per capita gross budget (with overshoot + NCS)	t CO2 cap−1	2025–2075	101.1	71.1	—
  Buildings + EMIM sector share of per capita budget (A1–A3, B1–B6 + EMIM, C1–C4)	%	—	50	50	[49] based on [26]
  Estimated national GFA (all uses)	million m2	2040	906.7	906.7	[65]
  Specific GFA per capita (all uses)	m2 GFA cap−1	—	101	101	calc from [65]

  for transparency. The resulting compliance thresholds, which serve as methodological reference points for district assessment, are summarized in

  Table 2. Target and compliance carbon budget limits for climate-neutral Positive Energy Districts (PEDs) in Austria for 2025–2075, expressed per gross floor area (GFA). These values represent the main methodological result of the budget downscaling process, derived from global Paris-compatible CO₂ pathways and Austrian building stock data.

  Limit	Value	Unit	Description and Compliance Note
  Paris-compatible target limit (GHG L1)	72	kgCO2e·m−2GFA	Stringent, science-based carbon budget based on [2,27] ensuring a safe pathway to 1.5 °C. Assumes no contribution from biogenic carbon sequestration. Achieving this limit provides the highest certainty of Paris alignment but is challenging in practice.
  Precautionary compliance limit (GHG L2)	196	kgCO2e·m−2GFA	Upper bound for climate-neutral declaration, assuming 0.5 tCO2e·a−1·cap−1 sequestration. Offers flexibility while retaining a safety buffer; exceeding this value increases risk of overshooting Paris-compatible budgets.
  Absolute compliance limit (GHG L3)	320	kgCO2e·m−2GFA	Maximum permissible budget, assuming 1 tCO2e·a−1·cap−1 global sequestration. Exceeding this value disqualifies a PED from climate-neutral status under this framework.

  .

2.4. LCA System Boundary and Scope of District

This section defines the temporal and functional boundaries for life-cycle assessment (LCA) within this proposed climate-neutrality assessment framework for PEDs. Unless stated otherwise, the assessment horizon is 2025–2075. All operational conversion factors decline to zero by 2050 for carriers on a national decarbonization pathway (e.g., electricity, district heating,); end-of-life and post-2050 processes are assumed carbon-neutral. As illustrated in Figure 3, the system boundaries include both operational and construction emissions, with a focus on the decarbonization of energy carriers and the treatment of biogenic carbon storage.

The assessment adapts life-cycle boundaries based on EN 15978 [29], with notable alterations: Unlike EN 15978, which references the EN 15804 [42] product standard that books stored biogenic CO₂ as an emission, this framework employs a time-dynamic LCA method for detailed declarations (Section 2.5.1). For practicability, a second, simplified method (Section 2.5.2) can also be used, which credits biogenic carbon in building materials in part as carbon storage (negative emissions, 55–100% depending on its origin and turnover period) over the horizon.

For mobility emissions, the framework includes parts of B8 that correspond to EMIM travels to the district and embodied emissions for vehicle construction (Section 2.4.2).

Table 3. System boundaries of Life Cycle Stage Inclusion in the assessment framework.

Life Cycle Stage	Sub-Stage	Module	Considered	Notes/Comments
A—Pre-construction	Pre-construction	A0	No	Outside EN 15978 scope here
A—Product stage	Product stage	A1	Yes	Raw material supply (embodied)
		A2	Yes	Transport to manufacturing
		A3	Yes	Manufacturing emissions
A—Construction process stage	Construction	A4	Yes	Assessed if practical/data available
		A5	Yes	Assessed if practical/data available
B—Use stage	Use (non-energy)	B1	No	Not considered
	Maintenance	B2	Yes	Included; post-2050 assumed carbon-neutral
	Repair	B3	Yes	Same as B2
	Replacement	B4	Yes	Same as B2
	Refurbishment	B5	Yes	Same as B2
	Operational energy use	B6	Yes	Dynamic/Monthly factors (to 0 by 2050)
	Operational water use	B7	No	Water-related GHG excluded
	Other building-related activities	B8	Partly	EMIM B8.1 and B8.2 included (Section 2.4.2) Operational mileage + one-time vehicle embodied (≤2050)
C—End-of-life stage	End-of-life	C1	Yes	Included; post-2050 carbon-neutral
		C2	Yes	Same as C1
		C3	Yes	Same as C1
		C4	Yes	Same as C1
D—Beyond system boundary	Reuse/recycling recovery	D1	Yes
	Exported utilities	D2	Yes	PV grid feed in credited as substitution with negative hourly/monthly factors
Contextual	Off-plot public infrastructure	—	No	Optional reporting only (not for compliance)

gives an overview of the concrete system boundary inclusions and exclusions.

To ensure consistent coverage of embodied and operational impacts, the framework uses building balance boundary BB6 (see Appendix A 

Table A2. Building LCA balancing boundaries defined in [43].

Boundary	Scope Description
BB1	Complete thermal envelope, including internal floor ceilings
BB3	BB1 plus all interior walls, basement structures, unheated buffer zones; excluding open circulation spaces (stairs, balconies, loggias, galleries)
BB5	BB3 plus shared circulation areas and building services
BB6 (used here)	BB5 plus exterior facilities (carports, bicycle shelters, etc.) and auxiliary buildings

for an overview of boundaries as defined by [43]) for A1–A3 (product stage), B2–B5 (use stage), and End-of-life prior to 2050 (C1–C4). BB6 covers the complete building: thermal envelope, interior partitions, basement structures, unheated buffer zones, technical systems, and exterior facilities (carports, bicycle shelters, auxiliary buildings).

2.4.1. Operational Energy Flows, Local PV, and Exported Electricity (Module B6/D2)

Local PV is handled operationally only: on-site generation replaces grid electricity using the framework’s conversion factors (hourly for analysis and optimization; monthly building-code factors for declarations). Surplus feed-in is credited as grid substitution potential (Module D2) using the same operational factors. This means that for declaration GHG and PE accounting purposes, feed in and self-consumption are equivalent. For research and future iterations of declarations, this can be detailed by setting the feed-in grid substitution factor to an appropriate/time-dependent fraction of the grid import factor.

Embodied impacts of PV systems are not counted at project level, as they are allocated to the national energy sector that underlies both the budget derivation and the operational factors used here (which exclude power-plant embodied impacts). This avoids double counting and maintains consistency with sectoral budgets. Crucially, it defuses the paradoxical tension of PEDs being penalized in the GHG balance for their role as energy providers: in our framework, EE from RES is already allocated and budgeted for in the energy sector, giving a clear energy and GHG incentive to include as much RES as is viable.

Waste heat imports are accounted for with zero direct emission intensity, since the associated emissions are allocated to the primary process generating the waste heat. District cooling supplied from a central city network is treated analogously to other imported carriers by applying the emission intensities provided by the utility or national statistics. Where district cooling is produced within the assessed district itself, the corresponding emissions are calculated based on the energy carriers used for its generation, most commonly electricity. See Figure 4 for a schematic overview of the energy flows, including the treatment of waste heat imports and district cooling emissions.

2.4.2. Mobility (Module B8)

Operational mobility emissions (Modules B8.1 for ICE and B8.2 for EV) are accounted for only for EMIM trips whose destination lies within the district. Public transport and active modes (walk/bike/other) are not directly modeled—either immaterial at district scale or outside district control and therefore allocated to the transport sector for GHG budgeting (as per [24]). However, district mobility measures increasing active or public modes of transport can decrease EMIM mileage, and thus, shifts in modal split are also considered under this framework. Trips whose destination lies outside the district are modeled, but considered the responsibility of the target location, omitting it in the district balance to avoid double counting. For EVs we cannot perfectly map charging emissions to discharge purpose; therefore we (i) compute emissions from all EV charging metered inside and outside the district using time-resolved grid factors, (ii) deduct a grid-equivalent credit for trips whose destination is outside the district (treated as an avoided-emission transfer to the destination system), and (iii) treat vehicle-to-building (V2B) as battery operation to the district with its charging emissions accounted for. This yields a net, destination-consistent EV operational footprint for the district, ensures fair effort towards sharing EMIM emissions between all buildings and districts, while retaining physical model accuracy and research trajectories regarding charging infrastructure, where actual expected loads are critical. For ICE vehicles, B8.1 is computed from destination-based trip mileage and carrier-specific g_CO2e·km⁻¹ factors.

Table 4. B8 Mobility emission inclusion matrix.

B8 Emission Cause/Use	Accounted	Modeled	Emission Balance Impact	Treatment/Rationale
Public Transport	✗	✗	None	Allocated to transport sector; outside district control.
Pedestrian, Bike, Other	✗	✗	None	Negligible at district scale. (Included if not)
EMIM ICE Trips to District	✓	✓	+	Included.
EMIM ICE Trips elsewhere	✗	✗	None	Not included here; attributed to destination system.
EMIM EV On-site Charging	(✓)	✓	+	Included using hourly source emission factors (grid, local PV = 0)
EMIM EV Off-site Charging	(✓)	✓	+	Included using hourly grid emission factors.
→ Trips to District	✓	✓	(+)	Implicitly included
→ Trips elsewhere	✗	✓	−	Deduct grid-equivalent credit to remove out-of-district travel from EV charging OE
→ V2B discharge	✓	✓	(+)	Like BESS: Charging included, but lowering building OE at discharge

Notes: “✓”: Accounted/Modelled, “✗”: Not accounted/modelled, “Emission balance impact”: “+” adds emissions; “−” is a credit.

shows the resulting inclusion matrix.

Embodied vehicle impacts are included once at purchase using static average factors (ICE ≈ 5 t_CO2e per car; EV ≈ 9 t_CO2e per car) without replacements or manufacturing decarbonization; this is a conservative snapshot and should be revisited in future work. Sensitivities can vary lifetimes and embodied factors, which should be further discussed; however, the mobility case study (Section 3.4) indicates that EMIM EE only plays a secondary role in district compliance.

2.4.3. Maintenance and End-of-Life: Post-2050 Assumptions (Modules B2–B5; C1–C4; Vehicles)

Building Maintenance (B2–B5) and End-of-life processes (C1–C4) are included. Since all processes taking place after 2050 are assumed to be carbon-neutral, in practice only those with a lifetime of <25 years need to be considered (or preferably avoided). The same convention applies to vehicles.

2.4.4. Public Infrastructure and Contextual Assets

For on-plot infrastructure within BB6 (e.g., internal access roads, on-plot sewers, bike shelters), impacts are included. Off-plot public infrastructure (municipal roads, trunk sewers, transit systems) is outside project control and not included in the core balance; where data are available, such items may be reported contextually but are not part of compliance checks. This keeps project-level accounting aligned with municipal/national responsibilities.

2.5. Treatment of Biogenic Carbon and Carbon Storage in Building Materials

The framework offers two admissible EE accounting options for declarations:

• An optional but preferred dynamic LCA method, for time-explicit treatment where results materially depend on biogenic storage and timing effects (e.g., rotation periods, storage duration, end-of-life timing);
• A default simplified LCA method provided for practicality and comparability, where biogenic carbon contained in bio-based building materials is partly credited as negative emissions (carbon storage) over the assessment horizon, based on carbon origin and use but independent of emission time.

• The framework tooling accommodates both simplified and dynamic biogenic carbon methods, with the latter recommended where data availability allows.

2.5.1. Dynamic LCA Method

The dynamic method evaluates the climate impact of emissions and removals as a function of when they occur over the assessment horizon (here: 2025–2075). In contrast to static accounting, time-dependent characterization captures that (i) early emissions induce higher radiative forcing (RF) and contribute more to peak warming, whereas (ii) delayed emissions exert a smaller effect. Carbon storage in bio-based building materials is therefore credited according to its duration and timing [44].

Methodologically, dynamic LCA proceeds as follows. First, for each GHG, the atmospheric decay of a pulse emission is represented via an impulse response function (IRF): for CO₂ with the Bern carbon cycle model; for CH₄ and N₂O with first-order exponential decay. Second, instantaneous dynamic characterization factors (DCF_inst) are obtained by integrating the gas-specific radiative efficiency over the decay curve from the time of emission/removal to any time t. Third, cumulative warming impact is computed by summing the DCF_inst contributions over all time steps and flows, and results are expressed as dynamic CO₂ equivalents by normalizing to the absolute GWP of a 1 kg CO₂ pulse over the same horizon. This yields time-differentiated CO₂e values for both positive emissions and negative fluxes (e.g., biogenic uptake/storage) [45,46].

For biogenic carbon, two regrowth timing conventions must be declared transparently because they affect results: (a) “growth before harvest” (uptake occurs prior to material use) or (b) “regrowth after harvest” (uptake occurs during/after the building life). The latter typically yields smaller near-term credits due to delayed removals. Fast-rotation materials (e.g., straw, hemp) provide stronger near-term mitigation than slow-rotation timber; dynamic LCA makes these differences explicit. Compared with static “0/0” or “−1/+1” treatments, dynamic accounting reduces bias from ignoring timing and avoids net-negative artifacts when only early-stage credits are counted. The literature consistently finds the dynamic approach more robust and transparent for bio-based construction [44,47].

2.5.2. Simplified LCA Method

The simplified method accounts for biogenic carbon storage based on the origin of the material and its expected service life without differentiating the timing of emissions. All emissions are considered with equal weighting, regardless of whether they occur in the near or distant future. The following rules apply as described in [48], with mass-weighted averages for composite materials:

• Fast-growing plant-based materials (e.g., straw, hemp, flax; rotation period ~1 year): → 100% of the biogenic CO₂ content is credited.
• Wood from managed forests (typical rotation period ~100 years): Only the proportion representing additional sequestration beyond a no-harvest scenario is credited. This comprises: 25% of the harvested wood volume from calamity events (e.g., bark beetle infestation, storm damage), credited at 100% plus 30% of biomass growth from sustainable management practices. → This results in a total credit of 55% of the biogenic CO₂ content for standard harvested wood.
• Calamity wood: → 100% credit for the biogenic CO₂ content. Project declaration requires origin verification from pest or storm damage.

In all cases, the maximum biogenic CO₂ storage credit is limited to 100% of the Global Warming Potential (GWP-biogenic) associated with the material. This approach implicitly incorporates two climate-relevant effects:

• Carbon uptake following harvest due to replanting in sustainably managed forests, compared to unmanaged forest growth.
• Temporary sequestration of biogenic carbon, which reduces the peak temperature increase when paired with rapid and consistent GHG emission reductions.

2.6. Operational Conversion Factors/Dynamic Emission Factors

Operational conversion factors follow time-dependent trajectories per carrier. For electricity and renewable district heating, factors decline to zero by 2050, consistent with national pathways; fossil carriers (natural gas, oil, coal) remain relatively constant until phase-out in 2050; biomass follows the national profile defined in the scenario set; district cooling factors are included analogously and mapped to the respective supply mix. The framework uses hourly factors for analysis and model comparisons, and monthly building-code factors (linearly down-scaled to 2050) for declarations to maximize practical compatibility.

• Data sources and projections:

• Hourly CO₂ intensity profiles for electricity are derived from Austrian grid mix data and adjusted to annual benchmarks from the Austrian National Energy and Climate Plan for 2030 and 2040.
• A linear trajectory is applied from current values to a CO₂ intensity of 0 g·kWh⁻¹ by 2050, consistent with the 100% renewable scenario [49] adapted in [50], including seasonal balancing via methanation of summer electricity surpluses.
• For biomass and district heating from renewable sources, a separate decarbonization pathway is applied based on sectoral transition strategies, reflecting gradual replacement of fossil inputs with renewable heat sources.
• For natural gas and other fossil fuels, virtually no decarbonization is assumed; in line with their inherent combustion emissions, CO₂ intensities recede by 5% until 2050 and must thereafter be completely phased out or covered by CCS.

• Use of hourly vs. monthly factors:

• For analysis and research purposes, the framework uses hourly conversion factors. Multiple factors sets from literature and modeling studies have been created and compared, enabling investigation of the timing effects of energy demand and generation. Section 3.3 presents a case study on these effects.
• For practical usability, regulatory consistency, and comparability with the Austrian building code, the official application of this proposed climate neutrality assessment framework for PEDs uses monthly CO₂ conversion factors to normalize hourly values to compliant annual levels (Appendix A 

  Table A3. Total (PEtot), non renewable (PEnre) and renewable (PEre) primary energy conversion and GHG emission factors for 2024 of selected energy carriers based on [52,66].

  Energy Carrier	PEtot	PEnre	PEre	GHG
  	kWhPE·kWh−1EE			gCO2e·kWh−1
  Grid Electricity	1.76	0.79	0.97	156
  District heating—boiler plant (renewable)	1.72	0.4	1.32	59
  District heating—boiler plant (non-renewable)	1.48	1.16	0.32	193
  District heating—high-efficiency CHP (default)	0.59	0.41	0.18	67
  District heating—high-efficiency CHP (best case)	0.33	–	0.33	20
  Waste heat (default)	1.00	–	1.00	20
  Waste heat (best case)	0.30	–	0.30	20
  Coal	1.46	1.46	0.00	360
  Heating oil	1.20	1.20	0.00	271
  Natural gas	1.10	1.10	0.00	201
  Biomass (solid)	1.13	0.10	1.03	247
  Biofuels (gaseous, stand-alone operation)	1.40	0.40	1.00	100
  Biofuels (liquid, stand-alone operation)	1.50	0.50	1.00	70
  District cooling Vienna [67]	0.42	0.13	0.29	42

  ). These are linearly downscaled from 2023 to 2050 according to the decarbonization path for each energy carrier (Section 2.5.1).

• Application in operational assessment (Module B6/D2):

Electricity consumption: Hourly or monthly CO₂ intensities are multiplied with consumption values to determine operational emissions.

On-site PV generation: Electricity consumed on-site replaces grid electricity at the corresponding CO₂ intensity.

PV surplus exports: PV electricity exported to the grid is credited with the same CO₂ intensity, representing its grid substitution potential.

Other on-site renewable heat generation (e.g., solar thermal, biomass): Reductions are credited based on the carbon capture potential of the plant-based materials used, such as the production of biochar, rather than simply accounting for the avoided CO₂ emissions from displaced heat sources (e.g., district heating or fossil boilers). This approach emphasizes the importance of incorporating carbon sequestration into the lifecycle carbon balance. By producing biochar, carbon from biomass is locked away in a stable form, thereby removing it from the atmosphere and contributing to long-term carbon storage. This method is vital for aligning the assessment with the broader goals of carbon neutrality and addressing the full carbon cycle, including both emission reductions and carbon sequestration. Additionally, this approach avoids the common pitfall of solely relying on emission avoidance through reduced fossil fuel use, which does not fully capture the potential of biomass as a tool for climate mitigation.

Treatment of embodied emissions of energy system: Dynamic conversion factors in this framework only include operational emissions from energy generation and supply. Embodied emissions from the construction and maintenance of generation assets (e.g., PV systems, district heating infrastructure) are excluded, as these are already covered within the national sectoral carbon budget for the energy system and deducted when deriving the building sector’s carbon budget. In contrast, other methodologies (e.g., IEA lifecycle factors) include both operational and embodied emissions, which would cause double counting here.

Gradual Emission Intensity Reductions 2025 to 2050

The total operational emissions over a 50-year period are calculated by multiplying the simulated annual operational emissions by the integral of the decarbonization pathway, as shown in Figure 5. The annual interpolation of emission reductions is based on established literature sources: a transition to a 100% renewable electricity system by 2030 [51] and the achievement of a fully climate-neutral energy system by 2050 [52]. After this point, no operational emissions are considered (fossil fuels are assumed to be phased out).

2.7. Example Assessments

2.7.1. Selection of Use Cases

Six representative Positive Energy District (PED) projects were chosen to demonstrate the applicability of the proposed climate neutrality PED assessment framework. The selection was guided by three main criteria. First, all projects are located in Austria or in comparable climatic regions, ensuring consistent boundary conditions for energy and emission analyses. Second, sufficient data availability was a prerequisite, covering both life cycle and operational aspects. Third, the cases were selected to reflect a broad diversity of conditions relevant for PED development:

This diversity is expressed in several dimensions: building typologies range from renovation projects to new constructions, from low, rural to high, metropolitan density, and from small-scale interventions to large district developments. Different usage mixes are represented, as well as various energy system configurations, including air and ground source heat pumps and district heating, with and without cooling, and with different ratios of mechanical ventilation and heat recovery. The projects also employ contrasting building materials, spanning from conventional reinforced concrete with EPS insulation to hybrid solutions, wood–concrete compounds, and timber structures with ecological insulation materials such as hemp or straw. Finally, mobility-related conditions differ significantly across the cases, reflecting settings from metropolitan to rural locations, with corresponding variations in annual mileage and electric vehicle penetration levels ranging from 10% to full adoption.

Each of the six investigated projects was characterized by a set of key input parameters relevant for energy and emission balances, as shown in

Table 5. Example district baseline parameters.

Project	D1	D2	D3	D4	D5	D6	Unit
Location	Innsbruck	Vienna	Salzburg	Vienna	Vienna	Vienna
Gross Floor Area (GFA)	5886	13,069	31,799	9761	180,492	33,506	m2
Net to Gross Floor Area	80	80	80	80	80	80	%
Construction Type	Conv	Eco	Eco	Eco	Conv	Conv
Refurbishment Share	0	18	0	100	100	0	%
Buildings	5	2	16	2	71	3
District Plot area	7341	14,095	28,000	4662	48,131	7323	m2
Floor Space Index (FSI)	0.80	0.93	1.14	2.09	3.75	4.58	–
Context Factor Density [24]	−11.3	−3.7	5.6	26.2	37.9	40.7	kWhPE·m−2NFA·a−1
Context Factor Mobility [24]	14.1	12.9	14.9	14.1	16.7	25.6	kWhPE·m−2NFA·a−1
Context Factor Renovation [24]	0	2.2	0	15.0	15.0	0	kWhPE·m−2NFA·a−1
Residential Space Use	100		96	100	91	52	%
Office/Commercial Space Use			3		9	38	%
Education Space Use		100	1			2	%
Retail Space Use						8	%
Heating System	AS HP	GS HP	DH	GS HP	DH	GS HP
Cooling System	None	GS HP	None	GS HP	None	GS HP *
DHW System	AS HP	GS HP	DH	DH	DH	GS HP
Window Ventilation Mechanical Ventilation	0100	0100	0100	0100	90 10	0100	% %
PV installed capacity	196	345	547	91.1	127	560	kWp
PV specific yield	39.8	32.7	20.0	10.7	0.9	19.5	kWh·m−2NFA·a−1
EV Share	70	70	70	70	70	70	%
EMIM Reduction	27%	20%			10%	10%
Flexible TABS	+3K heat	+1K heat	None	+3K heat −2K cool	±2K	+3K heat −2K cool

* In office and commercial spaces.

. These include the gross floor area (GFA), use type (residential, office, education, retail), the district density in terms of floor space index (FSI), and the share of renovation versus new construction and prevalent construction type (conventional, ecological). For energy demand calculations, climate datasets (TRY datasets) were assigned to each location, and hourly outdoor air temperatures and irradiation were considered. In addition, parameters for user electricity, lighting, domestic hot water, heating, cooling, and ventilation demand were included. Table 3 provides an overview of the six projects and their key parameters. Note the specific PV yield’s inverse proportionality to the district density, which complicates positive energy balances for higher densities. Operationalization for this effect in the form of target amending context factors is described in [24].

2.7.2. Data Collection and Assumptions

For each district, a consistent dataset from project declaration and assessment reports was compiled. To ensure transparency and reproducibility, a detailed catalog of all input fields of the framework (over 250 parameters, including units and default assumptions) is provided in the online data repository [41]. A summary of model categories and parameters can be found in Appendix A 

Table A4. Assessment model parameter overview.

Category	Description of Data	Parameters
District	Gross floor areas, net to gross ratios, renovation shares per usage	24
Weather	Hourly TSD of ambient temperature and N/E/S/W/H Irradiation	6 × 8760
Heights	Average room heights per usage	8
Building Hulls	Hull areas by type and orientation	8
Building Physics	Thermal transmittances, effective heat capacities, heat bridges, air tightness	10
Windows	Total transmittance, shading offsets summer, winter, mobile shading	32
Ventilation	Per usage: Share of mechanical/window ventilation, night ventilation, ACH, electricity demand, heat recovery rates	44
Usage	Concurrencies, DHW demands, auxiliary and plug loads per usage	35
Lighting	Daylight controls and coefficients, illuminations, light power loads per usage	12
Heating	Heating periods, Heating Powers and COPs of up to four heating systems (2 electric, 2 thermal), setpoint temperatures	33
Cooling	Cooling periods, Cooling Powers and COPs of up to 3 active and 1 passive systems (2 electric, 1 thermal, 1 free cooling), setpoint temperatures	37
DHW	Hot water demand profiles, storage volumes, generation efficiencies, distribution losses	17
PV Systems	Installed PV capacity, module and inverter efficiency, Hourly TSD of PV yield	5 + 8760
BESS	Battery capacity, charging/discharging efficiencies, c-rates, losses	11
Flexibility	Heating, Cooling, DHW, PV, BESS utilization ranges	11
Mobility	Usage intensity, concurrency, motorization, Annual mileage, EV penetration rates, charging infrastructure, minimum SOCs, utilization of bi-directional charging	32
Embodied carbon	Construction materials and components, GHG intensities, life cycles, reference areas	37
PE and Emission factors	Primary energy and emission conversion factors, annual weights, context factors	2 × 8760 13

. The data covers building characteristics, energy systems, mobility integration, life cycle inventories, and operational performance and were either taken from project documentation, energy performance certificates, and simulation models or derived from the Austrian LCA database baubook.at.

3. Results

3.1. Comparative Analysis

This section compares six baseline districts against the compliance framework using consistent project descriptors and harmonized KPIs. Lifecycle performance is summarized in

Table 6. Baseline district scenarios comparative compliance (✓= yes/✗= no) summary.

District	Heat Demand kWh·m−2NFA·a−1	GHG LCA kgCO2e·m−2GFA	Δ vs. L3 * kgCO2e·m−2GFA	<L3	PE Balance ** kWhPE·m−2NFA·a−1	>0	CN PED ***
D1	16.0	349.2	+29.2	✗	2.8	✓	✗
D2	21.3	292.6	−27.4	✓	8.2	✓	✓
D3	25.6	278.0	−42.0	✓	−47.7	✗	✗
D4	33.3	211.3	−108.7	✓	2.8	✓	✓
D5	37.9	349.0	+29.0	✗	−34.3	✗	✗
D6	13.8	620.2	+300.2	✗	12.8	✓	✗

* Δ vs. L3 = GHG LCA − 320; positive (red) = overshoot; negative (green) = undershoot/compliance. **, ***: ✓/green = compliance, ✗/red = non-compliance.

and Figure 6, Figure 7 and Figure 8: cumulative GHG per m²_GFA is decomposed into A1–A3 (construction incl. TES + EMIM embodied), B-ops (time-resolved electricity and thermal operation), EMIM operation, and PV substitution credits (D2); we also report the primary energy (PE) balance. Compliance bands are shown at L3 = 320, L2 = 196, L1 = 72 kg_CO2e·m⁻²_GFA.

Results are heterogeneous across use types and system choices. Relative to the L3 budget, the modeled GHG balances show: D1 +29.2, D2 −27.4, D3 −42.0, D4 −108.7, D5 +29.0, D6 +300.2 kg_CO2e·m⁻² (overshoot positive; undershoot negative). PE balance passes in D1, D2, D4, and D6 but not in D3 or D5. District-level drivers include the following:

D1 (greenfield, conventional): Non-compliant at L3 (+29.2). High A1–A3 intensity dominates; EMIM measures reduce operational mobility loads but cannot offset embodied peaks.

D2 (school campus): Benefits from the EMIM context (only commuting staff; high PV self-use + flexible charging), hybrid-eco materials achieving L3 compliance (−27.4) and a positive PE balance.

D3 (urban housing, DH): Meets L3 (−42.0) but fails PE (−47.7 kWh_PE·m⁻²·a). Lack of supply side flexibility on DH limits net-positive operation despite acceptable GHG.

D4 (ecologic renovation + TABS/GSHP + mobility measures): Strongest performer across both metrics (−108.7; positive PE). Renovation and density factors reduce embodied intensity; mobility context halves residual EMIM; TABS/GSHP enable flexible, low-carbon operation.

D5 (conventional renovation, gas→DH): Switch to DH alone is insufficient for L3 (+29.0) and fails PE; Section 3.5 explores compliant variants (non-fossil TES + eco materials + flexibility).

D6 (low heat demand, high embodied/EMIM): Despite a positive PE balance, very high embodied and/or mobility-related loads lead to substantial L3 overshoot (+300.2).

Effects of PV TES Embodied Emission (Non-)Inclusion

As argued in Section 2.4.1, embodied emissions from PV systems are not part of this PED system boundary but instead are nationally budgeted in the energy sector.

Table 7. Embodied emissions of example district PV TES EE in total and relative to the remaining EE and total LCA emissions.

Scenario	PV Yield kWh·m−2a−1	EE A1–A3 w/o PV kgCO2e·m−2GFA	EE A1-3 TES PV kgCO2e·m−2GFA	∆EE [%]	∆LCA [%]
D1	39.8	284.0	38.6	+13.6	+11.0
D2	32.7	232.8	30.6	+13.1	+10.5
D3	20.0	122.1	19.9	+16.3	+7.1
D4	10.7	58.0	10.8	+18.7	+5.1
D5	0.9	163.1	8.1	+5.0	+2.3
D6	19.5	433.0	19.4	+4.3	+3.1

shows the omitted PV EE in contrast to the remaining balance: especially for low-density districts that require a significantly positive PE balance and PV export to offset their negative density context factor, and for refurbishment projects with inherently low EE, PV EE can increase overall LCA emissions by more than 10% (assuming 1.16 t_CO2e·kW_p⁻¹ incl. inverters and cabling).

3.2. Case Study District 3: Redevelopment of Urban Medium Density Residential District: Interplay of Embodied and Operational Emission Reduction Measures

The case study evaluates multiple redevelopment scenarios for an urban medium-density residential district (District 3). Variants differ in construction materials (fossil-intensive vs. ecological), PV system sizing (1×, 2.6×, 2×, 1.7×), mobility assumptions (70% EV, 100% EV, reduced traffic), and heating/DHW supply (district heating vs. ground-source heat pump). The main assessment criteria are the cumulative GHG balance (2025–2075) and the primary energy balance for PED compliance.

The results in

Table 8. Summary of case study results for District 3—urban medium-density residential redevelopment, reporting GHG LCA emissions (kg_CO2e/m²_GFA), L3 and L2 compliance, primary energy balance and zero PE-Balance and Climate Neutral PED compliance (✓/green = yes, ✗/red = no).

Scenario	Construction	PV Size	Mobility	TES	GHG LCA *	<L3	<L2	Primary Energy Balance kWhPE·m−2NFA·a−1		Climate Neutral PED
Fossil Constr	Fossil	1×	70% EV	DH	378.2	✗	✗	−47.4	✗	✗
Eco Constr	Ecological	1×	70% EV	DH	277.6	✓	✗	−47.4	✗	✗
EC + 2.6 × PV	Ecological	2.6×	70% EV	DH	225.6	✓	✗	+6.9	✓	✓
EC + 2 × PV + Mob	Ecological	2×	90% EV, 80% EMIM	DH	170.1	✓	✓	+5.7	✓	✓
EC + 1.7 × PV GSHP	Ecological	1.7×	70% EV	GS HP	254.7	✓	✗	+0.9	✓	✓

* Unit: kg_CO2e·m⁻²_GFA.

, Figure 9 and Figure 10 show that all scenarios with ecological construction remain within the L3 compliance limit, while only the fossil construction variants exceed it. The combination of 2× PV oversizing with mobility measures (full electrification and reduced traffic) performs particularly well, even reaching the precautionary compliance limit for cumulative GHG emissions. This underlines the necessity of combining renewable supply expansion with systemic demand-side measures.

At the same time, the assessment framework requires not only compliance with the finite GHG budget but also a positive primary energy balance. Here, the analysis reveals significant challenges: while PV oversizing improves operational balances, achieving a net-positive balance is difficult in configurations relying on district heating (DH) even when assuming a renewable CHP-based supply with favorable emission factors. In contrast, scenarios with on-site GSHP systems combined with PV come closest to fulfilling both compliance criteria simultaneously, emphasizing the importance of coupling low-carbon construction materials with renewable-based, highly efficient supply systems.

3.3. Case Study District 4: Effect of Dynamic vs. Static Grid Emission Factors and Flexibility Signals

For District 4 we quantify how the temporal resolution and level of grid-GHG signals affect (i) total operational emissions from B6 electricity and EMIM-EV charging, and (ii) the average effective grid-intensity seen by the project. We compare monthly, building-code style factors (OIB ‘19/’23, currently in effect) with hourly, live-tracking based factors for the same vintages. We first test flexibility via TABS GS HP heating (+3K) and cooling (−2K), then in combination with building battery (BESS) sized = PV installed capacity (kW_p) and EV bidirectional charging (V2B, down to 50% SoC_RES). Loads and controls held constant: Building demand, PV generation, HVAC control set-points (22 °C heating, 25 °C cooling), occupancy, and the EMIM travel demand are fixed across scenarios. Only the GHG emission factors and flexibility signals vary:

• GHG Emission factors:

• Monthly and annual factors (2019 M, 2023 M, 2019 A) Twelve constant monthly values per vintage or a constant annual value (see Appendix A Figure A2).
• Hourly factors (2023 H). 8760 time steps based on historic live tracking; annual means match the corresponding OIB vintage 2023 (see Appendix A Figure A2)

• Flexibility Signals:

• Wind Peaks (for 2020, above 50% nominal Power)
• GHG Emission rolling averages (RA): signal if current GHG EF(t) is y% below Rolling Average (x hour future window) (x ∈ {+24, +48, +72}, y ∈ {5%, 10%})

• Detailed scenario definitions for case study 4 can be found in Appendix A 

  Table A5. Scenario definition for case study District 4.

  Scenario	Grid Emission [66]	Flexibility Signal	Flexibility Option (s)
  19M	2019 M	Wind Peaks > 50% Pnom	TABS
  19A	2019 A	Wind Peaks > 50% Pnom	TABS
  23M (Baseline)	2023 M	Wind Peaks > 50% Pnom	TABS
  23H 24-5	2023 H	RA x = 24, y = 5%	TABS
  23H 48-5	2023 H	RA x = 48, y = 5%	TABS
  23H 48-10	2023 H	RA x = 48, y = 10%	TABS
  23H 72-10	2023 H	RA x = 72, y = 10%	TABS
  23H 48-10 BESS	2023 H	RA x = 48, y = 10%	TABS + BESS
  23H 48-10 BESS V2B	2023 H	RA x = 48, y = 10%	TABS + BESS + V2B

  . The hourly flexibility signals are visualized in Appendix A Figure A2.

This use case highlights why hourly emission accounting is essential. As shown in

Table 9. Operational life cycle Emissions (OE), PE Balance, Imported Electricity (ED), relative differences to the Baseline (and Emission Intensities (AVG = Annual Grid Average, Actual = Actual district import average).

Scenario	OE (B6-D2)	∆BL %	PE Balance kWh·m−2·a−1	ED kWh·m−2·a−1	∆BL %	EIGrid gCO2e·kWh−1	EIdistrict gCO2e·kWh−1	∆EI/EIGrid %
[%]19M	175.4	+14.4	+6.3	43.2	0.0	231	241.0	+4.2
19A	167.3	+9.1	+6.6	43.2	0.0	227	224.6	−1.1
23M (Baseline)	153.3	-	+2.8	43.2	-	175	188.9	+7.4
23H 24-5	141.3	−7.8	+22.2	44.2	+2.3	175	162.1	−8.0
23H 48-5	141.2	−7.9	+22.5	44.2	+2.3	175	161.9	−8.1
23H 48-10	139.4	−9.0	+14.4	43.6	+0.9	175	160.6	−9.0
23H 72-10	139.8	−8.8	+14.7	43.6	+1.0	175	161.1	−8.6
23H 48-10 BESS	137.3	−10.4	+19.3	44.2	+2.3	175	154.2	−13.5
23H 48-10 BESS V2G	137.9	−10.0	+25.6	45.4	+5.1	175	151.3	−15.7

, with monthly factors the import-weighted average EI is higher than the annual grid average (≈241→231 g_CO2e·kWh⁻¹ for 2019 and 189→175 for 2023) because districts typically import in winter (carbon-intensive) and export PV in summer (cleaner). Annual bookkeeping would therefore “hide” the winter penalty; capturing it is critical—but without flexible dispatch comes at the cost of higher emissions—arguably as it should be.

Switching to hourly factors and applying rolling-average GHG signals enables flexible loads to target low-EI hours. In our runs the import-weighted EI drops from ~189 g_CO2e·kWh⁻¹ (2023 monthly) to ~160 g_CO2e·kWh⁻¹ with hourly signaling alone, and to ~150 g_CO2e·kWh⁻¹ when BESS and V2B are added. The grid EI for inflexible demand remains comparatively high (≈200 g_CO2e·kWh⁻¹) because it is drawn during scarcity events when storage is depleted—another effect only visible with hourly accounting.

The results presented in Table 9 and Figure 11 also show that flexibility generally slightly increases total electricity demand (round-trip and conversion losses; more charging activity), but because a larger share becomes flexible and is shifted to low-EI hours, net GHG emissions fall (our KPI), and the average effective EI of imports/charging (second KPI) improves materially. In short: hourly signals + storage/V2B make compliance harder to “paper over” yet provide a real, quantifiable pathway to lower life-cycle emissions.

Appendix A Figure A2 shows how energy demand, supplies and flexibility options are dispatched on an hourly basis for Scenario “23H 48-10 BESS V2G”.

3.4. Case Study District 5: Mobility and Renovation Sensitivity

The case study illustrates the cumulative GHG emissions of an identical district configuration (70 multistory buildings) across different spatial contexts, with mobility-related emissions added. It shows that EMIM can dominate total emissions, even in not-decarbonized districts such as this gas-heated example. We investigate District 5 with a factorial scenario set spanning four drivers of mobility-related impacts and their interaction with energy supply and envelope choices as shown in

Table 10. Case Study District 5 Factor Variations and resulting Scenarios.

Factor	Levels (Examples)	Unit	How Operationalized
Location (EMIM mileage)	34 (Rural-2)/24 (Rural-1)/23/22/12 (Suburban)/11 (Urban)/91 (Metropolitan)	km·cap−1·a−1	Per capita annual motorized mileage profile
EV share	5/30/70/90	% of EMIM	Split km into ICE/EV; apply EIc, EFc(t)
TES	Gas/DH/GSHP + PV	—	Carrier paths; PV substitution and D2 for feed-in
Renovation	None/Conventional/Ecologic	—	A1–A3 inventories; B6 demand

. Figure 12 shows the mileage differentiation of [53] used in this framework as described in [24,54].

District location and accessibility to public transport strongly influence travel demand, resulting in average annual personal motorized mileage ranging from about 4000 km in metropolitan contexts to 11,831 km in rural areas with high commute and low public transport availability (Region 24). Consequently, cumulative district emissions shown in Figure 13 vary widely—from about 740 kg_CO2e·m⁻²_GFA in the fossil-fuel-based, rural commuter case down to 27 kg_CO2e·m⁻²_GFA in the fully electrified, metropolitan case with high shares of alternative transport modes. Even at intermediate penetration levels (30–70% EV share), overall district mobility emissions remain dominated by fossil-fuel operation, accounting for roughly 90% of total EMIM-related emissions at 30% EV penetration and still around 60% at 70% EV penetration. These emissions can further be reduced by active mobility measures in the district such as bike and car sharing opportunities to reduce the total annual mileage by EMIM. This shows that L3 compliance (320 kg_CO2e·m⁻²_GFA over 2025–2075) is possible in all locations, but complete electrification of vehicles until 2050 (averaging an EV share of 50%+ from 2025 to 2075) is essential. Measures to shift the modal split away from EMIM to public transport, pedestrian and bikes can help also but may not be sufficient by themselves.

3.4.1. Impacts of Refurbishment

Table 11. Sensitivity Analysis of LCA results to TES, renovation and flexibility for District 5. (✓/green = compliance, ✗/red = non-compliance).

Scenario	TES	Renovation	GHG LCA kgCO2e·m−2GFA	L3 Compliance	PE Balance kWhPE·m−2GFAa−1	PED Compliance
Gas SQ	Gas	None	752.7	✗	−188.3	✗
DH SQ	DH	None	196.0	✓	−64.4	✗
DH Conv (Baseline)	DH	Conventional	349.0	✗	−28.5	✗
GS HP Conv	GS HP	Conventional	359.7	✗	−23.7	✗
DH Eco-Reno	DH	Ecological	245.7	✓	−28.5	✗
GS HP Eco-Reno	GS HP	Ecological	244.9	✓	−23.7	✗
GS HP Conv Flex	GS HP	Conventional	292.9	✓	+0.4	✓

reports KPIs, and Figure 14 and Figure 15 visualize composition for seven refurbishment options under the metropolitan mobility case with 70% EV: Unrenovated fossil gas (Gas SQ) violates all limits (~753 kg_CO2e·m⁻²_GFA). Switch to District heating without refurbishment (DH SQ) reaches GHG compliance (~196, L2) but without efficiency measures fails the PED/primary energy target. Conventional renovation is not reliably compliant (DH Conv-Reno ~334; GSHP Conv-Reno ~360), whereas ecologic renovations reduce A1–A3 and are robust (DH Eco-Reno ~246; GSHP Eco-Reno ~245). GSHP Conv-Flex remains within L3 (~293) thanks to higher avoided-grid credits and reduced operation, and—critically—enables a positive primary energy, illustrating that a climate-neutral PED is difficult but feasible when non-fossil TES is combined with flexibility even for an arguable worst case scenario of dense urban areas with little potential for energy efficiency and local renewable energy (Roof PV installations are required on all roofs up to conservative estimates).

3.4.2. Sensitivity to Location and Renovation

Figure 16 plots embodied emissions on the x-axis and operational emissions (including EMIM) on the y-axis for five location types (rural-1, rural-2, suburban, urban, metropolitan). Blue diagonals mark the cumulative-budget limits L3 (320), L2 (196), and L1 (72) kgCO₂e·m⁻²GFA to 2050. Marker fill indicates EV share (5–100%), outlines the thermal energy system (blue = gas, red = district heating, yellow = GSHP), and shapes the construction choice (square = no renovation, triangle = code-compliant renovation, circle = ecological renovation).

In the “no renovation” cases (top row), points cluster far left—reflecting very low embodied emissions—but sit high on the y-axis because the envelope is unchanged. Where district heating is available, several variants fall below L3 on GHG alone; gas heating generally breaches L3, particularly in rural settings where mobility dominates. Raising the EV share pushes points downward across all locations yet seldom achieves L2 or L1 without fabric measures.

With renovation (bottom row), points move right as embodied emissions rise, while y-values drop markedly—most strongly when GS HPs are combined with high EV penetration. In suburban, urban, and metropolitan contexts, many renovated variants approach or meet L2, and a few edge toward L1 when ecological materials are used. Rural districts remain the most challenging, because mobility keeps operational totals elevated.

Overall, if the sole objective were cumulative GHG-budget compliance, a “do-nothing fabric” strategy paired with low-carbon district heating could pass L3. However, it fails PED/primary energy goals: without efficiency upgrades, primary energy demand remains high, and the district cannot achieve a positive PE balance. Meeting both the climate-budget and PED criteria requires renovation (preferably but not necessarily ecological), electrified heat (GSHP or very low-carbon CHP DH), and high EV shares to reduce operational and mobility-related emissions simultaneously.

3.5. Case Study District 6: Differences in Construction Practice and Materials

This case study illustrates the application of the simplified biogenic carbon accounting method to the planned green-field development in District 6 and highlights that construction choices dominate D6’s life-cycle GHG outcome (

Table 12. Description of District 6 scenarios and GHG LCA results. (✓/green = compliance, d/yellow = near compliance, ✗/red = non-compliance).

Scenario	EE A1–A3	∆ Baseline	EE Total	GHG LCA	Compliance
Conv-Build (Baseline)	386.5	-	433.0	620.2	✗
Eco-Skeleton	266.7	−31%	313.2	500.3	✗
Mixed-Use CLT	90.1	−77%	136.6	323.8	d (L3, <320)
Max-Eco	−91.0	−124%	−44.5	142.7	✓ (L2, <196)

and Figure 17):

The baseline structural design of the project is based on conventional building practice, with a reinforced concrete framework, thermal insulation using expanded polystyrene (EPS), and mineral wool in areas requiring enhanced fire resistance. The district benefits from high urban density and mixed-use zoning, supported by excellent public transport accessibility. As a result, the underground parking facility can be designed with a single basement level and a reduced parking ratio, further limiting construction-related GHG emissions. The high building compactness enables comparatively low EE during the construction phase.

Despite these advantages, the total CO₂-equivalent emissions from both construction and operation exceed the allocated climate neutrality budget. Even with relatively low operational EI for building use and EMIM, the project surpasses the allowable threshold by more than 75% over the 50-year assessment period.

Scenario “Eco Skeleton” introduces a more ecologically ambitious system, featuring a reduced structural frame with timber elements and mineral wool insulation. Although this scenario represents an improvement over the traditional concrete structure, the CO₂ emissions remain 30% above the maximum compliance limit due to the use of steel-reinforced concrete in the structural elements.

Scenario “Mixed-Use CLT” incorporates a combination of concrete and cross-laminated timber (CLT). This mixed approach, including wood–concrete composite decks and CLT for upper floors, leads to a significant reduction in emissions compared to the first two scenarios, while still maintaining structural integrity and near L3 compliance.

Scenario “Max-Eco” represents the most ecologically advanced scenario, pushing the boundaries of climate-neutral construction. It utilizes CLT, straw insulation, wood-fiber insulation, and clay boards, minimizing the use of concrete to only necessary structural components. This results in the lowest emissions, making it the most sustainable option among the four, though it involves compromises, such as relaxing some fire protection regulations. An alternative pathway to compliance would involve further reducing operational and mobility-related emissions. For example:

• Increasing the installed PV Capacity,
• Raising the share of electric vehicles in the mobility mix,
• Implementing more ambitious ecological construction measures (e.g., low-carbon materials).

4. Discussion

The proposed climate neutrality assessment framework for PEDs aims to bridge the gap between research-grade, high-resolution analysis and practical, policy-aligned application. By defining clear system boundaries, integrating dynamic emission factors for multiple energy carriers, and embedding the assessment in a finite CO₂ budget, it offers a robust yet operationally feasible tool for evaluating district-level climate performance. The framework’s treatment of local PV—crediting both self-consumption and grid substitution—along with its differentiated decarbonization pathways, supports both design optimization and strategic policymaking. The following subsections discuss the methodological implications, applicability and limitations, and policy relevance of this approach.

4.1. Methodological Implications

Positive Energy Districts are framed here as vehicles for verifiable climate neutrality. The assessment boundary is refined to include operational emissions, EMIM, and selected embodied emissions, while transparently excluding categories allocated to other sectors in the national carbon budget (e.g., PV embodied impacts; end-of-life beyond 2050). This departs from conventional LCA allocations (e.g., attributing the embodied impacts of generation assets to the consuming district) and makes explicit how sectoral budgets are coupled. Many proposed PED assessment frameworks, such as those employed by POCITYF [56] or the SYNIKIA [57] project, use a wide range of KPIs covering many important aspects such as quality of life, comfort, planning processes, waste management [58], and the affordability and accessibility of PED measures. In our proposed declaration framework, in contrast, these aspects are deliberately not included in light of existing well-established national declaration systems covering these aspects in great depth: the klimaaktiv labels “Building” and Districts and Neighborhoods”, as well as the TotalQualityBuilding labels all employ methodologically similar points-based approaches, with dozens of assessment aspects across different categories, such as (A) Management, (B) Communication, (C) Urban Development, (D) Building, (E) Supply, (F) Mobility, for the “District and Neighborhoods” declaration [37].

Temporal resolution is central. Hourly emission factors and flexibility (via BESS/V2B/TABS) are shown to significantly reduce operational emissions compared to traditional monthly or annual accounting methods, highlighting the importance of time-resolved emissions in accurate PED assessments. For declarations, monthly factors aligned with Austrian code are used to ensure comparability and administrative simplicity. Carrier-specific decarbonization pathways are differentiated: electricity and district heating follow a declining trajectory to (near) zero by 2050, while gas, biomass, and other fossil carriers retain constant intensities over the horizon. Using static, time-invariant factors would implicitly assume society fails to decarbonize by 2050; the framework instead embeds the policy trajectory, after which the budget is exhausted and historic LCA conventions become less informative.

Local PV is treated on a system basis. Onsite generation offsets grid consumption at the time-varying CO₂ intensity of the avoided imports; exports are credited for grid substitution (D2), recognizing their contribution to system decarbonization. This contrasts with net-zero approaches that ignore export credits, and it materially affects both design optimization and policy interpretation in PED contexts.

Separating measurement effects from control effects is important. Hourly factors expose seasonal import–export asymmetries that annual bookkeeping masks, while dynamic flexibility signals (wind-peak events, GHG rolling averages, price signals, etc.) guide storage, charging, and thermal shifting to lower import-weighted intensities. Declarations remain simple and comparable, while design and operation can be assessed with rigor and temporal fidelity. Crucially, it prevents projects from over-crediting flexibility measures and allows PED compliance to be evaluated transparently against both carbon budgets and energy balance criteria. This clarity improves interpretation but means results are sensitive to the chosen signal and available assets (BESS, TABS, V2B)—an interconnection which would benefit from further research so it can be better designed for. Overall, the approach is auditable and declaration-ready, but data localization and policy-trajectory uncertainty remain the main limits.

A finite-budget perspective tightens alignment with Paris-compatible trajectories. Front-loaded embodied emissions cannot be “repaid” later without crowding out the remaining budget, so low-carbon construction becomes a first-order design variable alongside operational efficiency and mobility measures. Annualized KPIs can conceal this timing problem by spreading construction impacts evenly over service life; the budget lens restores the correct climate signal by tracking accumulation and scarcity over time.

Sensitivity to decarbonization pace and policy timelines. Our baseline assumes electricity and renewable DH factors decline to ~0 g_CO2e·kWh⁻¹ by 2050. To reflect slower or partial decarbonization, we examined alternative factor sets: (i) a delayed trajectory achieving near-zero intensities by 2060; and (ii) residual post-2050 intensities (e.g., 30–60 g_CO2e·kWh⁻¹ electricity; non-zero DH), with end-of-life and maintenance after 2050 treated accordingly. Under these paths, the import-weighted emission intensity rises, and cumulative totals tighten the available headroom; projects that pass under the baseline may require stronger embodied-carbon reductions (materials, lean design), greater PV and flexibility (BESS/V2B/TABS), or stricter EMIM measures (higher EV shares, demand reduction) to remain below L2/L3. Because the framework is parameter-driven, declarations can be re-issued annually with updated factors and budgets as EU/national trajectories and EPBD/ZEB provisions evolve.

4.2. Applicability and Limitations

Applicability. In contrast to other research that found their PED concepts favored certain locations, conditions, and contexts, such as districts with low density [59] or favorable climate conditions [60,61], over others, our framework is applicable to a wide range of PED sizes, usages, and typologies, from small urban districts to large-scale developments, both for renovation and green-field developments in Austrian climates. Its applicability stems from the modular definition of system boundaries and context factors and the flexibility to select conversion factors based on data availability and assessment purpose. In practice, high-resolution operation and mobility inputs are not always available; calibrated defaults allow use at early stages but reduce fidelity.

In contrast to other definitions of PEDs that interpret the positive energy balance without contextual factors [25], the investigated projects show compliance with our assessment targets, even though their energy balance is negative or they have net emissions over the life cycle or both. This indicates their compliance with a net zero emission future, and the strength of the declaration system to map these differences.

Uncertainty and change in underlying assumptions. The calculation of the carbon budget relies on several assumptions about future developments that will need to be revisited as conditions evolve. Key drivers include the total gross floor area, which affects the per-m² allocation, and the decarbonization rates of other sectors such as industry. These parameters are outside the direct control of individual projects and require regular updates at national and international levels. A systematic treatment of uncertainties, for example, through Monte Carlo or scenario analysis, lies beyond the scope of this study but should be pursued in future work to provide clearer uncertainty ranges and to link results more closely with established scenario pathways.

In particular, the exclusion of certain embodied emissions, particularly those for PV systems, relies on the integrity of the national budget allocation methodology, which represents a methodological limitation. Future work should investigate and compare other allocation methods.

Regulatory alignment is strong within the Austrian building code, but deviations from international standards, particularly EN 15978, may affect acceptance in cross-border contexts. The focus on a fixed 2050 endpoint aligns with current climate targets but may require revision in the event of accelerated or delayed policy timelines. In fact, current trends indicate that decarbonization might occur slower than mandated, which is why it is crucial for the assessment framework to annually update the remaining carbon budget based on national and international reports. The later sufficient action is taken, the smaller the remaining budget. This periodic update also includes other system developments such as gains in energy efficiency, or potential carbon budget undershoots of realized renovations, which could theoretically lead to a greater carbon budget for future projects if system decarbonization goes better than planned.

Transferability beyond Austria is methodologically possible but requires localizing decarbonization pathways, carrier factors, mobility contexts, and code constraints (e.g., tall-timber fire rules, façade-PV allowances).

The framework embeds a Paris-aligned trajectory to 2050 and assumes (near-)zero-carbon electricity/DH thereafter; if national timelines diverge, factors and budgets must be re-parametrized. Excluding PV embodied impacts at project level relies on coherent sectoral accounting; where this is not in place, a documented alternative (including PV embodied with a compensating sectoral adjustment) may be warranted.

4.3. Policy Implications

Adoption as declaration: The presented framework underpins a national labeling/declaration system in Austria (“klimaaktiv” [37,62]), providing consistent, comparable verification and a practical bridge between research-grade assessment and regulatory adoption. With localized factors, it can harmonize with emerging EU instruments (EPBD revisions, taxonomy), supporting broader uptake while maintaining the rigor needed for credible climate-neutral PED claims. To maintain the claim of “climate neutral districts”, the remaining carbon budgets must be routinely monitored and adjusted, preferably on an annual basis.

Adoption in practice: The framework enables municipalities and developers to test whether districts are on Paris-compatible paths by combining two KPIs—a finite cumulative GHG budget and a positive primary energy balance—within transparent boundaries that include user-driven mobility. Moving from annual KPIs to a 50-year cumulative budget refocuses decisions on near-term embodied and operational peaks, closing a key blind spot in conventional PED assessments. Authorities can request framework compliance as part of development agreements and contracts, or set concrete, verifiable targets (e.g., max embodied GHG per m², minimum flexible PV/BESS/V2B capacity, DH decarbonization milestones), align procurement and permitting, and offer incentives accordingly. Separating measurement (hourly EF) from control (flexibility signals) also gives a policy lever to reward time-aware operation rather than nominal annual surpluses. This should further be aligned with national adoption plans for Smart Readiness Indicators (SRI).

5. Conclusions

We present a declaration-ready framework for assessing climate-neutral Positive Energy Districts (PEDs) that combines finite CO₂ budgets, explicit system boundaries, and dynamic emission factors. It reconciles research-grade accuracy (hourly CO₂ intensity and differentiated decarbonization paths) with policy usability. Core elements include the following:

• Two KPIs with targets: (1) cumulative GHG budget to 2050 and (2) annual primary energy balance for declaring PED status (with/without mobility).
• LCA scope: unified accounting of construction, operation, and EMIM (including embodied construction, maintenance, end-of-life) over the next 25 years within a single CO₂ budget.
• Alignment with national sector targets: PV embodied impacts remain in the energy sector and are excluded at project level; non-EMIM travel modes (PT, walking, cycling) remain in the transport sector. PV exports are credited with time matching.
• Time-resolved emissions and pathways: hourly (or monthly) grid factors; distinct decarbonization trajectories for electricity and renewables; constant intensities for gas/biomass/other fossil carriers (with DH inheriting its supply mix).

• Design implications. Case study applications show that even highly efficient PED designs can exceed the available CO₂ budget unless embodied emissions are minimized (through low-carbon/biogenic options, lean structures), renewable energy and flexibility contributions are maximized (through flexible dispatch with BESS/V2B/TABS), and operational emissions from user-driven mobility are effectively reduced (by electrification and demand reduction). The main findings of the case studies indicate that:

• A diverse set of districts, ranging from low-density residential to high-density urban contexts, can achieve compliance with the proposed carbon budget (L3) by adopting low-carbon construction materials, maximizing renewable energy generation (PV oversizing), and electrifying mobility (e.g., EVs).
• Operational mobility emissions (EMIM) remain a significant factor for compliance, particularly in rural areas with lower EV penetration and less access to public transport.
• Hourly emission factors and flexibility (via BESS/V2B/TABS) can significantly reduce operational emissions compared to traditional monthly or annual accounting methods, highlighting the importance of time-resolved emissions in accurate PED assessments.

• Limitations. The interpretational claim of “climate neutral” hinges on systemwide decarbonization trajectories and assumptions that remain a source of uncertainty and may require periodic re-adjustment moving forward. Transferability of results depends on localized data. Results obtained in early planning may be subject to change as planning progresses, potentially requiring separate declarations.

• Next steps.

• Link to dynamic LCA databases to strengthen embodied-emission estimates.
• Adapt to diverse regulatory contexts and add uncertainty analysis for pathways.
• Automate compliance checks and embed the workflow in planning regulations and municipal decision processes.