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The price of carbon is y = Lx / C

Using this simple equation, Dollar-based climate losses for Mexico, Brazil, Chile, Colombia and Peru are compared with those for China and the US in Dollars per tonne CO2 - not just tonnes CO2

 

Financial metrics applied to economic scenarios are invaluable for assessing the risks associated with climate change at global, national and city levels. Using data derived from the actual loss and damage costs attributable to manmade climate change triggered by fossil fuel burning since 1750, we published the world’s first, scientific carbon pricing methodology in our book ‘Predicting The Price Of Carbon’ by my colleague Richard H. Clarke, Director (Research) at Predict Ability Ltd (PAL).

 

Our carbon price equation is written simply as

y = Lx / C

y is the cumulative carbon price in US $ per tonne CO2

L is the per-event cost of insured and uninsured weather-related losses

x is each event’s attribution factor (the cumulative product Lx is the total loss caused by manmade climate change since 1750)

C is cumulative emissions expressed in tonnes CO2

 

We refine the carbon price y in accordance with a country’s (or region’s or city’s) climate risk. We evaluate this using our climate risk algorithm PALgamma. We determine event attribution factors using our real-time attribution algorithm PALca. This methodology enables carbon prices to be determined for over 100 countries and 520 cities - past, present and future.

 

The snap-shot of y, PAL-1Y, is the expected global average US$ loss per tonne CO2 in any given year. PAL’s Universal Carbon Price PAL-UP is a further extension: it is local not global and incorporates an economic factor representing the cost of emissions appropriate to specific countries. Thus, developing economies with lower historic emissions have a lower carbon price compared to older economies whose historic emissions are greater.

 

As Partners of the Carbon Pricing Leadership Coalition (CPLC) we participated in the recent Webinar, when regional Partners from Mexico, Brazil, Chile, Colombia and Peru reported their progress in meeting the challenges of climate change. Use of PAL’s fully-costed metrics, tailored to their regional, national or city data would attach scientifically determined Dollar values to the CPLC’s already impressive work.

 

As we see in Figure 1, PAL-UP carbon prices are given for Mexico, Brazil, Chile, Colombia and Peru, and are compared with those for China for 2017. The US is already on the PAL-1Y ceiling pathway.

 

Climate loss projections expressed as carbon prices for 2017 for five South American countries and China using PAL’s Universal Carbon Price (PAL-UP). If adopted globally, PAL-UP would be over 90% revenue efficient.

Figure 1. Climate loss projections expressed as carbon prices for 2017 for five South American countries and China using PAL’s Universal Carbon Price (PAL-UP). If adopted globally, PAL-UP would be over 90% revenue efficient.

 

Being scientifically determined, rather than politically influenced, these carbon prices form an impartial framework for a comprehensive range of metrics that matter. For example, we identify and measure climate risk for 520 cities in US$ on our Global Climate Risk Map part of which is shown below in Figure 2.

 

 The Global Climate Risk Map shows Dollar values of climate risk for 520 cities worldwide. This snapshot shows the risks at play in the southern US, Central America and northern South America. The massive eastern US risk is on the horizon.

Figure 2. The Global Climate Risk Map shows Dollar values of climate risk for 520 cities worldwide. This snapshot shows the risks at play in the southern US, Central America and northern South America. The massive eastern US risk is on the horizon.

 

We also show the impact of climate change on the economies of countries and cities in our GDP Growth Grid, which results from over 300 simulations. An example using Mexico is shown in Table 1 below.

 

GDP Growth Grid for Mexico for climate scenarios of Paris and BAU (Business As Usual), for various combinations of climate sensitivity (a measure of the impact of emissions) and decarbonisation costs. The white box shows the range of most likely outcomes based on the last ten years’ data. The average growth is from 1960. Each tile represents a scenario based on the parameters shown at the edge of the grid; a cell is red if the economy peaks before 2100

Table 1. GDP Growth Grid for Mexico for climate scenarios of Paris and BAU (Business As Usual), for various combinations of climate sensitivity (a measure of the impact of emissions) and decarbonisation costs. The white box shows the range of most likely outcomes based on the last ten years’ data. The average growth is from 1960. Each tile represents a scenario based on the parameters shown at the edge of the grid; a cell is red if the economy peaks before 2100

 

The Global Climate Risk Map and the GDP Growth Grid shown here are just two of the many, financial metrics derived from the carbon price y that is based on the climate loss i.e. the actual loss and damage costs attributable to manmade climate change.

 

What climate policy needs is Dollars per tonne of CO2 not just tonnes of CO2. Our equation, y = Lx / C, is the bedrock carbon price and a benchmark giving carbon trading schemes real-world credibility. It scientifically underpins vital metrics that can aid, support and enhance the outstanding efforts of our CPLC Partners worldwide to tackle the urgent issues of climate change mitigation.

 

Bruce Menzies, Chairman, Predict Ability Ltd (PAL)

© Copyright Predict Ability Ltd 2018. All rights reserved.

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