Research article

A virtual global carbon price is essential to drive rapid decarbonisation

Authors
  • Richard Clarke (Ortec Finance, Bridge House, 181 Queen Victoria Street, London EC4V 4EG)
  • Mark Maslin orcid logo (Department of Geography, University College London, Gower Street, WC1E 6BT)

Abstract

Dealing with climate change is now an infrastructure challenge. Within the next 30 years our energy generation must switch from fossil fuels to renewables. New buildings need to be zero-carbon and existing buildings need to be retrofitted. Our global transportation network will need to be transformed. Delivering the Net Zero World is an engineering challenge. But to do this we need a globally agreed virtual carbon price so that every single infrastructure project can be assessed in terms of its impact on carbon emissions and thus planetary health. We propose a loss-and-damage-based carbon price that is enhanced or reduced by variable, national impact factors. Carbon intensity weighting would further increase the price’s impact. 

Keywords: carbon, climate change, carbon price, net zero, engineering, loss and damage

How to Cite:

Clarke, R. & Maslin, M., (2024) “A virtual global carbon price is essential to drive rapid decarbonisation”, UCL Open Environment 6(1). doi: https://doi.org/10.14324/111.444/ucloe.1983

75 Views

Published on
17 Dec 2024
Peer Reviewed

Introduction

The behaviours of engineers are triangulated by the needs of their employer, their education, training, experience, character and the guidance and rules of their professional bodies. Martin [1] highlights that leading employers and leaders of the engineering community are aware of the need for the profession to change its approach to infrastructure in the face of the challenges of a changing climate. While some employers are far-sighted and holistic, many are not. So, it is incumbent on the professional bodies to be the guardians of public wellbeing, safety and the environment.

Much change has been achieved by the engineering profession in recent decades. Safety engineering has become its own discipline. Energy efficiency, resource utilisation, local pollution abatement and cost reductions have enabled mass access to transport, technology and cheap food. But some of this has been done at the expense of the global environment. A more holistic approach to ‘safety’ in its broadest sense is required, to deal with global issues such as greenhouse gas (GHG) emissions and plastic pollution. Total lifecycle thinking must become the norm for all engineers and project developers [2].

For example, if a power plant were to be built today, and Net Zero 2050 is the target, then it would, in theory, need to emit less than half as much carbon dioxide (CO2) as a plant commissioned 40 years ago. If this cannot be done, or is uneconomic, then, with current approaches, the project must be justified by energy policy or subsidised or both. These approaches cause engineers to deliver unsustainable projects in the face of conflicting influences from international treaties, insurers and pressure from the law and some investor and societal groups. Engineers, and, indeed, all these groups need a common tool to encourage the design and delivery of infrastructure projects that are consistent with net zero ambitions. We propose that a virtual weighted carbon price based on the carbon intensity and consequent climate change damages could be used as one such a tool to help track progress to net zero at the national scale that includes some adjustments to compensate for historical emissions.

Methods

Calculating the carbon intensity weighting

In this section we propose how to calculate the carbon intensity of the energy sources involved in any infrastructure project. Then we set out how this can be incorporated into a virtual carbon price and how a weighted carbon price can be used to track progress towards net zero at the scale of nations. We use this approach because there is a particular problem with carbon pricing as it can be a one-size-fits-all, making carbon price a blunt instrument for encouraging behavioural change. A spectrum of prices based on impact (carbon intensity) would be more effective as well as future-proof [3]. For a carbon price to be credible it must provide a sustained signal of significant magnitude, one that is both verifiable and reasonably predictable. This, we believe, is where our loss-and-damage-based carbon price (Fig. 1) has an advantage.

Figure 1
Figure 1

The cumulative, climate change related economic impacts of carbon emissions has escalated since the 1980s (green/orange lines) and continued ‘business as usual’ (2.6 °C in 2100) emissions are expected to lead to catastrophic losses, especially in low- and middle-income (LMIC) countries. The PREDICT-CP carbon price (green line) captures the modelled, global GDP impacts of acute physical risk (extreme weather) and chronic physical risk in 154 countries (using aggregates of 1860 city-based polygons; we note that about a third of all disasters occur within the boundaries of cities). These historical and future GDP impacts were calculated using the Ortec Finance PREDICT tool. PREDICT shows that the impact of acute risk under RCP8.5 (4.3 °C of warming by 2100) could cause a difference-to-baseline reduction in global GDP of about 60% by 2100. This is similar to Kotz et al. [4]. The underlying data comes from World Urbanization Prospects (WUP, United Nations, New York), NOAA annual temperature anomalies, historical/projected temperature anomaly trends by country (NASA-GISS) and Munich Re/EM-DAT (disaster and catastrophe frequencies and losses, by location and peril, 1980–2018).

Two things then become apparent. Firstly, to incentivise the movement from ‘dirty’ carbon-intensive fuels to ‘clean’ low-carbon fuels or energy, there may need to be an even stronger price signal, whatever the base price. Secondly, to ensure continuing best practice it will be necessary, from the very start, to link the carbon prices to all energy types and not just fossil fuels.

For every fuel or energy source there is a ratio e, the amount of CO2 emitted divided by the useful energy the source produces. This is called ‘carbon intensity’. For coal, e is about 1 tonne/MWh of electricity; for gas it is about 0.46 tonne/MWh, but even with renewable energy and nuclear sources there is a hidden e of between 0.01 and 0.05 tonne/MWh due to their materials of construction. We use this information to create a carbon intensity weighting (CIW).

By using the CIW method, the carbon price yi for fuel/energy type i is given by

yi=y×CIW=y×ei×f×z.

The ‘CIW’ factor f is defined as

f=ΣEi/Σ(Ei×ei).

A ‘revenue weighting’ factor z is defined as the weighting needed to ensure that the total premium from individual fuel prices yi is consistent with the premium using a global, unadjusted carbon price y.

z=(Σ(Ei×ei))2/(ΣEi×Σ(Ei×ei2)),

where,

Ei = amount of fuel/energy type i used globally (or by country or sector or, perhaps, by company) (GWh)

ei = emission factor for fuel/energy type i (tonne CO2/GWh)

yi = carbon price for a given fuel/energy type i (US$/tonne CO2)

y = global carbon price (US$/tonne CO2), for example, y = SIMPLE-CP × Weff (see main text and Figs. 1 and 2).

Figure 2
Figure 2

GDP - consumption emissions plot: (Dc/Dw × Pw/Pc) v. (GDP/capita)c/(GDP/capita)w at time t, where Dc = country (consumption) cumulative emissions, Dw = world cumulative emissions, Pw = world population, Pc = country population. The effective country weighting, Weff is (W × W*)0.5, where W is the carbon inheritance and W* is the carbon liability. If only GDP/capita data is available, set Weff = W and if country weightings are not required, set Weff = 1. The bubbles are coloured according to the colour key: for example, if a country’s W decreases and W* increases, the bubble will be a shade of red. The data behind this figure comes from sources quoted in Fig. 1 and population, GDP per capita and granular emissions data by territory are compiled and curated by Our World in Data (OWiD, Oxford). The diagram uses, where available, the cumulative consumption emissions from 1750 to 2017; the consumption emissions of nations include emissions associated with imported goods and services. Bubble colours reflect the changes from 2016 to 2017.

Calculating the impact of CO2 decay and climatic response

The peak impact from injecting a mass of CO2 into the atmosphere occurs about 20 years after its release. We calculate the impact of cumulative, global emissions ΣCDR using a two-step approximation.

  1. Decay. The estimated lifetime of a mass of fossil CO2 in the atmosphere is calculated using a fit to the ensemble predictions reported by Archer et al. [5]. From the year of its release, ti, to a future year, tn, the proportion, C*, of the initial release, C, that remains airborne is given by:

    C* = C×(0.22+0 .27e ( t n t i )/350 +0.35 e ( t n t i )/200 +0.16 e ( t n t i )/10 ).

  2. Response. The fractional surface temperature response R to a doubling of atmospheric CO2 is initially fast (~40% in 8 years) but then levels off. According to Hansen et al. [6], equilibrium may take over 1000 years to be reached, largely due to the oceans. Roper approximated this (http://roperld.com/science/GlobalWarmingPrediction.htm) using a two-term equation:

    R=0.368×tanh((tnti)/10.5)+0.632/2×(1+tanh((tnti277)/524)).

Combining C*, R and historical emissions data (Our World in Data) in a matrix calculation yields the decay and response adjusted, cumulative emissions data ΣCDR that is needed to determine the cumulative carbon price PREDICT-CP (see Fig. 1). For the years in which tn < ti the matrix contains zeroes. Historically, ΣCDR ≈ 0.368 × ΣC.

Carbon pricing for engineers

An alternative approach to policies or subsidies is to address the loss and damage caused by CO2 specifically. We argue there needs to be an internationally agreed, virtual carbon pricing system that can readily be used by engineers to estimate the economic impact of each tonne of CO2 or any other GHG emitted (Fig. 1). Those costs should be included in the economic assessment of every project [7]. When and where a project takes place are significant factors.

Carbon markets are unpredictable, and other carbon pricing tools are complex to use, or they are encumbered by social discounting considerations [8]. An engineer always needs a practical equation. We propose that a loss-and-damage-based carbon price is used in all projects where carbon or GHG emissions occur. This would include direct and embodied emissions, for example, steel or concrete.

In Fig. 1 the base carbon price (SIMPLE-CP, orange line) represents the carbon price that would compensate for the cumulative, climate attributable economic impact (Gx) of cumulative CO2 emissions (ΣCDR); these are summated global emissions C adjusted for decay and climatic response (see Methods section). G is the economic damage from acute physical risks (extreme weather) and x is the extent to which those losses are climate attributable. Here, the attribution factor is determined using a proxy based on local temperature anomaly.

The simplified carbon price, SIMPLE-CP (US$, 2020) = e(0.04 × (year-1950)) is an approximation to the output of Ortec Finance’s PREDICT physical risk tool, as modified to produce the loss-and-damage carbon price PREDICT-CP (see Fig. 1 for details). For 2025, the SIMPLE-CP = US$20/tonne CO2. The B&T (Burke and Tanutama) term (Fig. 1), accounts for the economic damage from chronic or slow-onset physical risks [9]. The base carbon price is largely independent of future emissions, provided that the transient climate response to cumulative emissions (TCRE) holds at about 1.9 °C/trillion tonnes carbon. This base price is then factored by a time-varying, country weighting factor (Weff, or W for simplicity, see Fig. 2) as the historic emissions and their associated economic development should be considered, to address the need for climate justice [10]. By including W, the United States (US) country price would be $100 in 2025. Additionally, a CIW term can be included to address laggard, high carbon intensity emissions (see Methods section). Thus, the loss and damage carbon price (for year, country, fuel/energy type) = SIMPLE-CP × W × CIW.

As an example, coal emissions in the US in 2030 would attract a carbon price of over $272/tonne CO2 = US$ e0.04 × (2030-1950) × 5.35 × 2.07. The CIW term depends on the future energy mix and geographical or sectorial scope (Clarke [3] showed how CIW could evolve during an energy transition). This price is robustly in line with the proposals of the World Bank Carbon Pricing Leadership Coalition’s High-Level Commission. By mid-century, the impacts of acute and chronic physical risk are about equal. Callaghan and Mankin [11] showed the profound impact that chronic physical risk is already causing. The country weighting factors, W, include the effects of chronic physical risk.

Prioritising infrastructure changes in the Developed World first

The engineering challenge of net zero is even harder when it is realised that not even the richest countries have truly started to decouple their energy use from emissions [12]. The terms carbon inheritance and carbon liability convey the immutable relationship between economic wealth (gross domestic product [GDP]/capita) and energy (kWh/GDP) see Webster and Clarke [13].

We define carbon inheritance (W) as the wealth that nations have attained, largely by using fossil fuels since the beginning of the Industrial Revolution or as data permits. More specifically, this inheritance relates to work and energy but, in practice, nearly all that energy has come from fossil energy. W is expressed as the ratio of (GDP/capita)country/(GDP/capita)world, so the exact definition of GDP is immaterial.

The second term, carbon liability (W*), we define as the cumulative carbon emissions D (= ΣC) of a country divided by its current population (Dc/Pc) and the result is then divided by (Dworld/Pworld). We argue that the current populations represent the net outcome of all the progress, toil, conflict, health and other factors that have led to the emissions and wealth of a country today.

Overall, we find there is a direct relationship (R2 = 0.63) between cumulative wealth and cumulative emissions, as shown in Fig. 2. For each country, the emissions and wealth have been normalised using the global average values as noted above. The size of the bubbles is proportional to the current population of each nation. On the log–log plot there is roughly a 1:1 relationship between scaled emissions and scaled GDP, with a few outliers. The relationship is strongest if consumption, rather than domestic-only emissions are included.

There is a huge difference between the Democratic Republic of the Congo and the US, over two orders of magnitude in fact. This is because the USA has inherited a lot of emissions from its own systems and has a lot of liability as well which is the opposite for the DR Congo. Figure 2 makes a compelling case for action by the industrialised, first-tier economies. When their populations are factored-in, the impact of US, China, Japan, Germany, United Kingdom (UK) and other high-income countries becomes apparent. Whatever else they do, these countries need to fully commit to net zero, and allow engineers to lead the infrastructure revolution, to enable the energy transition. The benefits to these countries and all the others would be transformational. To take a specific example, the UK is blessed with copious quantities of offshore and onshore wind and yet the previous UK Government committed to yet more North Sea oil production and that may not pass the net zero tests, as determined by the UK Government’s own Committee on Climate Change [14]. Rather, the UK should lead on the seasonal energy storage technologies and inter-country grid connectors that are needed to make a renewables-dominated grid dependable. Moreover, there are too many instances in which the UK Government has been taken to court due to non-compliance with legislation it previously enacted, for example, in meeting its 2030 targets or poor home insulation uptake. Currently, the developing economies and India, in particular, look to the UK for leadership as one of the founders of the industrial age.

The underlying data behind Fig. 2 includes population, GDP data and all-forms of emissions data and these can be regularly updated. This leads to the possibility that the diagram could be used as a tool for tracking the progress of nations towards net zero.

For example, if a nation’s bubble moves:

Horizontally right – the economy is growing faster than the global average with low emissions (good, a shade of green).

Right and up – that is, ‘business as usual’ growth (must do better, a shade of yellow).

Stands still – in line with global average (fair, yellow).

Left and down – economy is in trouble (blue, policy action needed).

Up and left, pink as per Brazil or red as per Venezuela (deep trouble, emigration, possible economic collapse).

Right and down – has Sweden started transitioning as its population grows? (good, a deeper shade of green).

Discussion of actions to drive net zero

The need for rapid transition to renewable energy has become central to the discussion of energy security. The Russian invasion of Ukraine led to a huge increase in fossil fuel prices which affected everything from industry, agriculture to the cost of living. In terms of infrastructure, a mixed response is emerging: the European Union is moving away from Russian gas as quickly as possible, having pledged to double the installation of renewable energy this decade [15]; meanwhile, in the US the Biden administration opened the door to selling new oil and gas drilling leases in the Gulf of Mexico and Alaska to help it ensure self-sufficiency in fossil fuels. It has proposed as many as 11 lease sales over the next five years, including 10 in the Gulf of Mexico and one in the Cook Inlet off the Alaskan coast [16]. Drilling, however, off both the Atlantic and Pacific coasts are not included. Meanwhile China, and to a lesser extent India, have leapt at the opportunity to buy cheap Russian oil, due to Western sanctions on Russian exports. Imports of Russian oil rose by 55% from a year earlier to a record level in May 2022, displacing Saudi Arabia as China’s biggest provider [17].

Longer term, the invasion of Ukraine has put energy security back on the top of governments’ agendas. For countries with no or little access to domestic fossil fuel reserves, renewables are set to become very attractive – they are already cheaper to build and maintain than coal fired power stations (International Energy Agency). Hence a diagram such as Fig. 2 will enable us to track how countries are doing not only in decarbonisation but also how secure their energy will be in the future.

As well as an agreed virtual carbon price, professional bodies need to dissuade companies and individuals from the defensive patenting of clean technologies and should instead support licensing agreements to ensure that smart ideas reach the market. This will give a clear signal to incumbents that they need to transition their technologies or move to new markets. As the Carbon Disclosure Project [18] highlights, it is policy and attitude as well as low emissions that makes for a clean, net zero-aligned corporation. On every board and division, there needs to be an executive level officer who is responsible for transition compliance and lifecycle engineering.

Thus, to empower engineers and to kick-start or boost the net zero revolution in the developed markets followed by the rapidly emerging markets, we call for four actions:

  1. Engineering professional bodies across the world need to support engineers so they are empowered to do the job they need to do, to enable economies to rapidly decarbonise their energy, infrastructure, manufacturing and food industries.

  2. Every major company needs a Net Zero Transition Compliance Officer who alongside the Safety Compliance officer ensures every project and decision helps develop the green, low-carbon economy.

  3. Develop the carbon inheritance/carbon liability diagram (Fig. 2) to monitor the movements of countries, to determine if and to what extent they are on track during the energy transition. Ideally, the clock rate on this should be faster than once per year.

  4. Establish a usable yet meaningful globally agreed virtual carbon price, together with carbon auditing tools [19] so that engineers and other actors can include the cost of emitting each tonne of CO2 in determining the economic feasibility of projects. A method is suggested above but, ideally, all engineers in the world need to be using the same tool to check that every infrastructure project complies with the Paris Agreement decarbonisation pathway.

A huge side benefit of all this will be to draw the world’s exceptionally talented individuals into the engineering profession, to work on holistic solutions to today’s and tomorrow’s needs.

Acknowledgements

We would like to thank the reviewer (Hafez Abdo) and the editors (Carmelina Cosmi and Dan Osborn) for all their excellent comments and suggestions which have increased the clarity of the manuscript and streamlined the text.

Open data and materials availability statement

All data generated or analysed during this study are included in this published article.

Declarations and conflicts of interest

Research ethics statement

Not applicable to this article.

Consent for publication statement

The authors declare that research participants’ informed consent to publication of findings – including photos, videos and any personal or identifiable information – was secured prior to publication.

Conflicts of interest statement

The authors declare no conflicts of interest with this work.

Disclaimer

The views expressed in this article are those of the authors and do not necessarily reflect those of their employers.

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Appendix

Ortec Finance’s acute physical risk tool PREDICT has been built to provide an estimate of the GDP impact of extreme weather events for three perils under a range of climate scenarios. This output uses econometric modelling that examines the benefits and drawbacks of different societal responses to climate change, including the impacts of the energy transition and other policy measures. Ortec Finance’s modelling is combined with E3ME, the macroeconomic model developed by Cambridge Econometrics.

PREDICT is comprised of six data arrays and three modules that together calculate the expected frequency of events N for each city-based polygon area (i = 1–1860 in 154 countries) and each peril [h = 1–3, which comprise meteorological (cyclones, etc.), hydrological (flooding, etc.), climatological (heatwaves, droughts, etc)] for each year t. The three modules include:

  • an urbanisation module U (equation A.1) that is influenced by city and regional population size p(t), and change rate dp/dt;

  • an adaptation module A that depends on city and regional population and GDP/capita;

  • a climate module ψ that amplifies the climate-counterfactual trends in extreme weather event frequency as temperature anomalies change, as each climate scenario unfolds; the temperature anomaly Ta used in ψ is multiplied by a country or sub-regional factor LLE that estimates how Ta varies with latitude and longitude (NASA-GISS data); in ψ, for each peril, there is a global parameter δhₕ;

the data arrays are: population data pi,t for each city (UN World Urbanisation Prospects + projections); GDP/capita g (at country level or below); global temperature anomalies Ta(t); Ta correction factors for each city-polygon LLEi,t; fh,i baseline expected frequency of events for each peril and each city-polygon; εh,i calibration factors for each peril and each city-polygon (εh,i values tend to 1 as the model improves; Munich Re and EM-DAT data was used to calibrate the model):

Nh,i,t=εh,iU(dp/dt, p)A(p, g)fh,iψ(TatLLEi,t|δh). (A.1)

The frequencies Nh,i,t for each city-polygon and peril in year t are converted into GDP impacts G (2020US$) using equation A.2. Each N-term is multiplied by LPEt, the time-variant loss-per-event (US$; additional factors are used for meteorological events); a time variant country factor CFt; and EAR(g), a GDP/capita-dependent Economic Amplification Factor derived from the research of Hallegatte and Hourcade [20]. For developed economies, EAR tends to 1 as GDP/capita increases.

Gc,t=ΣiΣhNh,i,tLPEtCFtEAR(g). (A.2)

 Open peer review from Hafez Abdo

Review

Review information

DOI:: 10.14293/S2199-1006.1.SOR-EARTH.AFABKP.v1.RZPWQH
License:
This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

ScienceOpen disciplines: Earth & Environmental sciences
Keywords: Built environment , carbon , net zero , carbon price , The Environment , Climate , Sustainable development , engineering , climate change , loss and damage

Review text

Please find below my comments on the paper

Title: A virtual global carbon price enabling engineers to drive essential and rapid decarbonization

The abstract states that our energy generation must be switched to RE within the next 30 years. This is not very accurate as eneregy generation via fossil fuel will still be made, but at a reduced level. Also, please remove citation (Clarke and Maslin, 2022) from the abstract.

The Introduction section is rather short and does not offer clear description/discussion of the research problem and does not offer motivations for the study. what is the research query/questions?

Discussion of data lacks coherance. why such data was collected? links between the research query and data is key

Discussion of Methods: [Secondly, to ensure continuing best practice it will be necessary, from the very start, to link the carbon prices to all energy types and not just fossil fuels.] It is not clear how and why apply cabon prices on RE options. Energy generated by RE does not result in emissions.

Carbon Pricing and Engineering: [An alternative approach is to address the loss and damage caused by CO₂ specifically] alternative to what?

you need to explain what you mean by [loss and damage-based carbon price], also to justfiy you suggestion that this approach to be used. how the damage will be estimated? much subjectivity may involve much subjectivity - needs further discussion.

what is really lacking from this study is a discussion of similar and related literature.

In this discussion section [On every board and division, there needs to be an executive level officer who is responsible for transition compliance and lifecycle engineering.] this suggestion is not based on emperical evidence. it is a mere suggestion, and therefore needs to be removed from this section.

the four recommendations need to be reviewed and aligned with the emperical results of the paper

Overall, I found this a rather weak article, however for an online publishing it may be fit but only after being reviweed and strengthened further



Note:
This review refers to round 1 of peer review and may pertain to an earlier version of the document.

 Open peer review from Dan Osborn

Review
This Editor assessment review from the Editor-in-Chief aims to draw together the previous reviews of this revised submission and how the authors have so far responded. Also, it sets out some relatively small, detailed matters for clarification that would further improve the submission after the author's revision to the current version. These points deal mostly with the expressions used to calculate carbon prices and should be straightforward to address.

There have been two previous reviews of version 1 (https://doi.org/10.14324/111.444/000184.v1) from experts in the energy field. One review found the original submission lacked clarity in various respects: the submission needed to be strengthened in various ways, for example, with a clearer introduction and a Discussion section better linked to the main findings rather than containing suggestions for actions in the corporate sector. The other review found the submission made a potentially important contribution but that no reference to some main aspects of EU energy approaches, including those dealing with energy efficiency, and asked that these be included. Both reviewers felt the submission lacked some important contextual material. Given the nature of the submission - a new approach to costing infrastructure projects and a way to monitor progress towards net zero that might be widely adopted if generalised - both reviewers necessarily focused on the high-level strategic elements of the submission.

In my view, to make the higher-level amendments both reviewers suggest would entail providing a much more broad-ranging submission that would detract from the current focus, which is on the ways in which countries might move towards net zero through using a new way of pricing carbon in infrastructure projects. In my view, the submission is already complex enough and to address these points would entail a large review of the field and the submission is not a review of this kind - although one may be needed, as both reviewers imply.

The authors have addressed the remarks of the two reviewers in this revised version 2 (https://doi.org/10.14324/ucloepreprints.233.v2) about a lack of clarity in parts of the submission by improving the Introduction and expanding the Discussion section in a manner that provides an envelope for the carbon pricing work. The Introduction and Discussion now acknowledge the operational issues and professional challenges that engineers face worldwide and positions the submission in the high-level energy landscape that has been disrupted by the Russia-Ukraine conflict that has created such uncertainty that a high-level review article would not be practicable at this time. The carbon pricing elements of the submission now flow better as the text has been clarified, figure legends expanded and a lot of the detail placed in an information box (which will need careful positioning in any fully published version). I agree with the reviewers that that the paper juxtaposes operational and professional challenges with a new way of calculating and carbon prices that includes renewable themselves on the same footing as any other infrastructure project. This leads readers to judge whether the new approach to carbon pricing would help address the challenge that engineers face - but then readers of multi-disciplinary papers are best placed to make such a judgement and it might be considered wrong for the authors to go any further than they already have in suggesting actions that might be taken by countries or corporate bodies.

These improvements have enabled me to pick up a few points of detail that need to be corrected or clarified further.

These points are:

1. I cannot find any explanation in the main text as to what SIMPLE-CP and PREDICT-CP is. I can see they are both dealt with in the top left box in Fig 1. in terms of expressions being given to show how they are calculated. Inserting a sentence or two of explanation would help especially as the SIMPLE-CP seems to be defined in part by a term EXP which is not elsewhere explained.

2. I think the use of apparently different meanings of W etc is confusing:

a. Weff is defined in two different ways in the paper. In the legend to Fig 2. it is defined as the “The effective country weighting, Weff is (W x W*)0.5.” -- whereas in the earlier section on Calculating the Carbon Intensity Weighting (of an infrastructure project I think you mean) it is set out within the expression “y = SIMPLE-CP x Weff (or W for simplicity)”. In the second instance, which occurs first in the submission, the implication is that Weff is the equivalent of W. That means that the phrase “Weff is (W x W*)0.5” in the legend to Fig 2. would, as W is a short form of Weff, reads in full as “Weff is (Weff x W*)0.5”. That last expression doesn’t seem to me to make sense as Weff would then be itself multiplied by a factor all raised to an exponent. Please clarify the way the terms Weff and W are being used. If they are being used in different ways perhaps insert a sentence or two making that clear or change the letters being used to ensure clarity.

b. Perhaps this point falls if a. is dealt with suitably. In the section “Prioritizing infrastructure changes in the Developed World first”, the term W appears not as an “effective country weighting” (as in a. above) but as a “carbon inheritance”. If these two sets of words are equivalent and W is the same calculation term in both cases please make that clear in the text. If the use of W in the two instances is different then some different letter will be required in one place or the other or the use of Weff continuously in the relevant section.

c. This part of the text (same section as for b. above) does not seem to follow very well as W* is defined first as a simple ration Dc/Pc and then in the next sentence is redefined as the ration scaled: Here are the words used in the submission: “The second term, carbon liability (W*), we define as the cumulative carbon emissions D (= ΣC) of a country divided by its current population (Dc/Pc). That ratio is then scaled by the world’s cumulative emissions and population to obtain W*.” Perhaps this can be easily edited?

3. The sentence: “The relationship is strongest if consumption, rather than domestic-only emissions are included.” seems out of place and perhaps was meant to be included in the previous paragraph which discusses Fig 2. It would be worth making crystal clear which relationship is used for Fig 2. i.e. whether it is consumption or domestic-only based. This will make a big difference to interpretation so could be important for the reader to be left in no doubt about.

4. I note a reference is made in the legend to Fig 1. to 1860 cities. Could you please confirm that the country comparisons are just that and not city-to-city comparisons or an aggregate of city comparisons. If Fig 1. And Fig 2. have been prepared in that proxy city-based way please make that is clear in the text with an explanation as to why cities have been used as a proxy for countries.

5. Can anything more be said about the any of the proprietary software used to generate data for the study? Readers may be interested in it if they want to take up the approach or test reproducibility or sensitivity.

Declarations and potential conflicts of interest.
Professor Dan Osborn, Editor-in-Chief of UCL Open Environment, is affiliated to the same University as the corresponding Author (University College London, UK) but however does not possess any research or partnership conflicting interests with the corresponding Author. It is deemed that sufficient external peer review has been sought for this submission to ensure effective and proper peer review standards, in line with the journals peer review policies (https://journals.uclpress.co.uk/ucloe/site/journal-policies/).

Note:
This review refers to round 2 of peer review.

 Open peer review from Carmelina Cosmi

Review

Review information

DOI:: 10.14293/S2199-1006.1.SOR-EARTH.ASDW1U.v1.RJWFBM
License:
This work has been published open access under Creative Commons Attribution License CC BY 4.0 , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com .

ScienceOpen disciplines: Earth & Environmental sciences
Keywords: Built environment , carbon , net zero , carbon price , The Environment , Climate , Sustainable development , engineering , climate change , loss and damage

Review text

Global carbon pricing has been recognized as a cost-effective way to reduce carbon emissions and support the achievement of the Net Zero goal. The proposed method of assigning a price based on carbon intensity weighting from an LCA perspective is attractive and could be useful in accelerating the technology transition. Therefore, the article could contribute to the scientific debate by providing a method for evaluating technologies and driving decarbonization based on differentiated carbon pricing.  However, in its current form, it has many shortcomings that limit its usefulness and clarity as explained in the following.

The abstract should better focus on the objective of the study and briefly explain the application and main conclusions, so as to provide the reader with the essential context, highlight the objectives and key findings.

The introduction is vague and does not focus the topic of the paper. Carbon pricing/carbon tax is a widely discussed topic and a thorough evaluation of the existing literature is necessary to frame the study and improve its innovative contribution to the scientific debate. Some opinions not substantiated by recent studies and legislative measures are also reported.  For example, the authors report that "Energy efficiency, resource utilization, local pollution abatement, and cost reduction have enabled mass access to affordable transportation, technology, and food. But this has come at the expense of the global environment."  Related to this statement, it should be noted that according to the "energy efficiency first" principle, energy efficiency is considered by the EU to be the "first fuel" to achieve climate mitigation (e.g. European Green Deal Package, Commission Recommendation (EU) 2021/1749, Energy Efficiency Directive 2018/2002), and as outlined by the recent IEA Energy Efficiency 2022 report. This concept should be appropriately emphasized in the context of the document, taking into account the significant contribution to the reduction of CO2 and other emissions from anthropogenic activities.

In addition, as underlined by the authors, a holistic approach is certainly useful in addressing complex issues such as climate change mitigation and "planetary health". This is what energy modelers have been doing by for many decades, developing complex models based on optimizing resource use and performing in-depth scenario analyses widely used for policy assessment at different spatial scales (e.g., the E3M Lab http://www.e3mlab.eu/e3mlab/; IEA-ETSAP https://iea-etsap.org/index.php). Therefore, in light of these studies, the authors should review the possible implications of their analysis, which can certainly support technology assessment in a complex modeling environment.

The section on Data should be moved after Methods and more extensive comments are needed to better understand how these data were processed and further used in the application of the method proposed by the authors.  In fact, the presentation of the application of the method is unclear and should be reworded to highlight its usefulness and the main results obtained in terms of promoting clean technologies and fuels that can accelerate the energy transition as well as policy indications.

The conclusions are also vague, non-explanatory and not supported by the results and/or scientific evidence.

Taking into account the above comments, the authors should improve the manuscript accordingly, including a thorough analysis of the state of the art, highlighting the scientific content and the importance of the study in the context of climate change mitigation and energy system transformation.



Note:
This review refers to round 1 of peer review and may pertain to an earlier version of the document.