Life Cycle Carbon Footprint (LCA) Comparison: Porsche Panamera (ICE) vs. Tesla Model Y (BEV) — incorporating the role of road design

Dr. Philip Wong

Mr. Kinson Lo

Mr. Guo Xiong Yi

Abstract

This report compares the life‑cycle carbon footprint of a conventional internal combustion engine vehicle (ICEV), represented by the Porsche Panamera, and a battery electric vehicle (BEV) represented by the Tesla Model Y. As owner manuals provide qualitative guidance rather than life‑cycle inventories, quantitative magnitudes are contextualized using the European life-cycle assessment (LCA) study (Pipitone et al., 2021, Sustainability). In addition, the analysis explicitly incorporates a systems perspective from the 2026 review (Wong et al., 2026, Energies), which argues that road design standards and road‑network operations can materially influence transport energy use and associated emissions via mechanisms such as flow stabilization, reduced stop–start conditions, and multimodal prioritization. Results indicate that (i) ICEV life‑cycle greenhouse gas (GHG) emissions are typically use‑phase dominated by fuel combustion, (ii) BEVs exhibit higher embodied emissions in production (notably battery manufacturing) but can achieve lower life‑cycle emissions where the electricity mix is sufficiently low‑carbon and lifetime mileage is adequate, and (iii) road design and operations shift the operational energy intensity of both ICEVs and BEVs, thereby affecting breakeven mileage and the magnitude of any BEV advantage.

1. Goal, scope, and functional unit

1.1 Goal

To compare Porsche Panamera (ICE) and Tesla Model Y (BEV) with respect to life‑cycle carbon footprint (GWP), based on:

• the vehicle manuals (behavioural / maintenance mechanisms),
• the vehicle LCA evidence base (Pipitone et al., 2021), and
• the road‑design / road‑system energy‑efficiency literature (Wong et al., 2026).

1.2 System boundary

A cradle‑to‑grave boundary is retained for the vehicles (production, use, end‑of‑life). However, following Wong et al. (2026), the use-phase is interpreted as vehicle operation within a road transport system, where infrastructure design and network operations affect energy intensity through:

• geometric design and gradients (alignment consistency),
• intersection/control design (signals/roundabouts),
• speed harmonization and managed operations (ITS),
• pavement condition/rolling resistance (asset management),
• multimodal provision (mode shift, trip structure).

Implication: road design is not merely contextual; it can change L/100km (ICE) and kWh/100km (BEV), and thereby life‑cycle CO₂e.

1.3 Functional unit

As mentioned in most LCA papers, the functional unit is distance travelled or total emissions over a specified lifetime mileage (e.g. 150,000 km).

2. Quantitative LCA anchor (Pipitone et al., 2021): what it implies for the Panamera–Model Y comparison

Although Pipitone et al. model representative 2019 European B–C segment vehicles (not Panamera/Model Y specifically), their results provide empirically grounded directionality and sensitivities:

• EU‑average electricity mix, 150,000 km:
  • ICEV ∼28,063 kgCO₂e
  • BEV ∼16,434 kgCO₂e ( ≈58.6% of ICEV)
• Breakeven mileage (EU mix): BEV becomes lower cumulative GWP than ICEV after ∼41,250 km.
• Electricity mix sensitivity: BEV advantage strengthens on low‑carbon grids and can disappear or reverse on coal‑heavy grids.

Interpretive relevance to Panamera vs. Model Y:
The Panamera is typically heavier and more powerful than the paper’s representative ICEV; the Model Y is also larger than the representative BEV. Absolute totals will differ, but the structural LCA logic (production “carbon debt” for BEVs; use-phase dominance for ICEVs; grid dependence for BEVs) remains applicable.

3. Road design as a determinant of life‑cycle carbon footprint (Wong et al., 2026)

Wong et al. (2026) provide a document‑analysis review across major road design manuals (Austroads/TPDM/DMRB) and argue that energy‑relevant outcomes are embedded through direct, indirect, and emergent mechanisms across the road asset life-cycle. For this vehicle comparison, the critical point is that road-network design and operation can reduce or increase operational energy demand by affecting:

3.1 Direct operational mechanisms (high confidence energy link)

• Flow stabilization: coordinated/adaptive signal control, roundabouts, speed harmonization, managed motorway operations reduce idling and stop–start conditions.

• Geometry and gradient management: alignment consistency and moderated gradients reduce braking/acceleration cycles and traction demands.

These mechanisms reduce per‑km energy use for both fuel and electricity powertrains.

3.2 Indirect system mechanisms (energy co‑benefits)

• Safety-driven design (speed consistency, forgiving roads) can reduce harsh acceleration/braking.
• Asset management and pavement condition influence rolling resistance and maintenance disruptions, affecting energy use and emissions over time.
• Multimodal integration (public transport priority, walking/cycling continuity) can reduce total vehicle‑kilometres travelled (VKT), producing systemic carbon benefits beyond vehicle technology choice.

3.3 Implication for vehicle LCA comparisons

A vehicle‑centric LCA often treats “use-phase” as a function of vehicle efficiency and energy supply alone. Wong et al. (2026) imply that infrastructure and operations can shift the use-phase intensity, thus changing:

• the slope of cumulative emissions vs. distance for both vehicles, and
• the breakeven mileage where BEVs surpass ICEVs in cumulative CO₂e.

4. Manual-derived mechanisms (reinterpreted with road design explicitly included)

4.1 Porsche Panamera (ICE): manual levers + road-system amplification

From the Panamera manual, the primary carbon levers are:

• eco‑driving (smooth acceleration, avoid idling),
• tire pressure / load management,
• maintenance to preserve combustion efficiency and prevent malfunction.

Road design interacts with these levers:

• On networks with frequent stops, poor coordination, or congestion, the Panamera experiences more idling and transient operation (higher L/100km).
• Road designs emphasizing flow stability (e.g. coordinated signals, fewer stop-start, appropriate intersection forms) align with the manual’s efficiency advice and can materially lower use‑phase emissions, which dominate ICE life‑cycle GWP.

4.2 Tesla Model Y (BEV): manual levers + road-system amplification

From the Tesla manual, major carbon levers include:

• reduced (speed, HVAC, tires),
• reduced parked energy (“phantom drain”),
• battery‑health practices to avoid premature replacement.

Road design interacts with these levers:

• Stop–start traffic raises via repeated acceleration and HVAC time in congestion, though regenerative braking partially mitigates losses.
• Flow-stabilizing operations (adaptive signals, speed harmonization) reduce time‑based energy draws (HVAC, auxiliaries) and improve real‑world efficiency.
• Better operations may also reduce aggressive driving patterns, supporting battery and tire longevity (indirect life‑cycle carbon effects).

5. Comparative life‑cycle synthesis (carbon footprint), now including road design

5.1 Production (embodied carbon)

• ICE (Panamera): embodied emissions are substantial but generally lower than a BEV with a large traction battery.
• BEV (Model Y): embodied emissions are typically higher, driven by traction battery manufacturing (consistent with Pipitone et al., where battery production is a major contributor within BEV production impacts).

Road design does not materially change production emissions; it mainly influences use-phase and maintenance-related burdens.

5.2 Use-phase (dominant for ICE; grid‑dependent for BEV)

Life‑cycle carbon in use-phase can be conceptualized as:

• ICEV:

gCO₂e/km≈(fuel per km × (upstream + combustion factors))

• BEV:

gCO₂e/km≈(kWh per km) × (grid gCO₂e/kWh)

Road design affects the “fuel per km” and “kWh per km” terms through stop rate, delay, speed variance, and gradient/geometry—precisely the “direct mechanism” pathway emphasized by Wong et al. (2026).

5.3 End‑of‑life

End‑of‑life contributes less to GWP than production/use in most cases, though BEV battery EoL is a distinct component. Road design is not a primary driver here.

6. Implications: how road design changes the Panamera–Model Y carbon ranking

6.1 In well‑designed, well‑operated networks (flow stability, ITS, coordinated signals)

• Both vehicles improve, but the ICEV benefit can be especially pronounced because its use-phase is combustion‑dominated and sensitive to idling/stop–start.

• BEV still often retains an advantage under moderate‑to‑clean grids, but the breakeven mileage can shift (either direction) depending on how much the operational improvements reduce per‑km energy.

6.2 In poorly performing networks (frequent congestion, stop–start, suboptimal signal timing)

• ICEV use-phase emissions typically rise markedly, increasing life‑cycle GWP.

• BEV efficiency also worsens, but regenerative braking and high drivetrain efficiency can reduce the penalty relative to ICEVs; however, the magnitude depends on auxiliaries (HVAC) and traffic delay.

6.3 Interaction with electricity mix (still decisive for BEVs)

Even with excellent road design, Wong et al. (2026) does not negate the LCA insight from Pipitone et al. (2021): grid carbon intensity can dominate BEV use-phase GWP. Road design improves kWh/km, but cannot fully offset a highly carbon‑intensive supply in extreme cases.

7. Battery (charging) energy source and upstream pathway sensitivity

A BEV’s life‑cycle carbon footprint is highly sensitive to driving efficiency (kWh/km ) and the carbon intensity of the energy used to charge the battery, i.e., the upstream pathway delivering electricity (or electricity-equivalent) to the vehicle. This extends the “grid mix matters” conclusion from vehicle LCA literature into a broader energy‑transition framing: the same BEV can be low‑carbon or high‑carbon depending on whether charging electricity comes from coal‑heavy generation, renewables, or other low‑carbon pathways.

Link to the LCA evidence: Pipitone et al. (2021) explicitly show that BEV life‑cycle results and breakeven mileage depend strongly on the electricity mix; cleaner electricity reduces BEV use‑phase emissions and strengthens the life‑cycle advantage, while carbon‑intensive electricity weakens it.

7.1 Why “green hydrogen” can materially lower BEV impacts (when it is the energy source)

If the charging electricity (directly or indirectly) is supplied via green hydrogen (hydrogen produced through renewable‑powered electrolysis) then the upstream emissions associated with delivering energy to the battery can be substantially lower, reducing the BEV’s use‑phase gCO₂e/km. The attached hydrogen supply‑chain review (Wong et al., 2025, Energies) emphasizes that:

• Green hydrogen (renewables-powered electrolysis) is positioned as the most sustainable route among hydrogen pathways, in contrast with fossil-derived hydrogen routes (grey/brown; and blue with residual emissions and reliance on carbon capture performance).

• The environmental value of hydrogen therefore depends on how it is produced and on supply‑chain integrity, safety, and traceability (i.e., ensuring the “green” attribute is real and auditable).

Interpretation for BEV analysis:

• If a BEV is charged from a grid increasingly supplied by renewables or from electricity generated using verifiably low‑carbon hydrogen (or hydrogen-derived energy carriers), the BEV’s operational emissions decrease.

• Conversely, if electricity/hydrogen is produced using fossil inputs without effective abatement, BEV charging emissions can remain substantial, eroding life‑cycle benefits.

7.2 Governance/traceability matters for “green” claims

Wong et al. (2025) further argue that hydrogen supply chains require proactive regulation, harmonized standards, and traceability mechanisms (e.g., certification/Guarantee of Origin concepts and data accountability tools) to avoid fragmented regulation and unverifiable carbon attributes. Practically, this means LCA comparisons should not assume “hydrogen is green”; they should specify the pathway (green/blue/grey) and whether the renewable origin is verifiable.

7. Conclusion

For LCA comparisons between ICEVs and BEVs, results should be interpreted within the road-transport system rather than as a vehicle-only problem. Consistent with Pipitone et al. (2021), BEVs often exhibit higher production-phase emissions, yet can deliver lower life-cycle GWP depending on the electricity mix and lifetime mileage. Extending this perspective, Wong et al. (2026) indicate that road design standards and network operations can materially affect operational energy intensity—through flow stabilization, reduced idling, speed harmonization, and improved asset condition—thereby changing use-phase emissions for both powertrains and shifting the breakeven mileage. Consequently, the carbon advantage of BEVs is jointly determined by (i) electricity mix and charging patterns, (ii) lifetime mileage and battery durability, and (iii) road-network design and operational performance, which influence real-world energy consumption for both vehicle types.

Beyond driving efficiency and network operations, the upstream energy pathway for battery charging is a critical determinant of BEV life-cycle emissions. Reflecting the electricity-mix sensitivity highlighted by Pipitone et al. (2021), charging supplied by genuinely low-carbon energy, such as electricity sourced from renewables or from verifiably green hydrogen produced via renewable-powered electrolysis, can substantially reduce impacts, whereas fossil-derived electricity (or hydrogen produced from fossil fuels without robust abatement) can materially weaken the BEV life-cycle advantage (Wong et al., 2025).

References:

1.      Porsche AG. (2020). Porsche Panamera owner’s manual. https://s3cf792cad773e861.jimcontent.com/download/version/1612526643/module/15686325722/name/Porsche%20Panamera%20Owner%27s%20Manual.pdf

2.      Pipitone, E., Caltabellotta, S., & Occhipinti, L. (2021). A life cycle environmental impact comparison between traditional, hybrid, and electric vehicles in the European context. Sustainability, 13(19), 10992.

3.      Tesla, Inc. (2026). Model Y owner’s manual. https://www.tesla.com/ownersmanual/modely/en_us/Owners_Manual.pdf

4.      Wong, P., & Lai, J. (2025a). Energy Transitions in Cities: A Comparative Analysis of Policies and Strategies in Hong Kong, London, and Melbourne. Energies, 18(1), 37. https://doi.org/10.3390/en18010037

5.      Wong, P. Y. L., Lo, K. C. C., Lai, J. H. K., & Wong, T. T. Y. (2025b). Proactive Regulation for Hydrogen Supply Chains: Enhancing Logistics Frameworks in Australia. Energies, 18(12), 3056. https://doi.org/10.3390/en18123056

6.      Wong, P. Y. L., Leung, T. M., Zhang, W., Lo, K. C. C., Guo, X., & Hu, T. (2026). Enhancing Energy Efficiency in Road Transport Systems: A Comparative Study of Australia, Hong Kong and the UK. Energies, 19(1), 266. https://doi.org/10.3390/en19010266