The objectiveis to assess whether growth rates are faster at younger or older trees, given that biomass accumulation is correlated with tree widths. We find that tree widths continue rising steadily over 100-500 years, with radiuses growing at an average pace around 2mm per year.
However, growth rates are fastest in early years. As a rule of thumb, they will slow down by 25% after 20-years, 40% after 40-years and 50% after 60-years. This may be an argument for forest management and sustainable harvesting as more reforestation projects are undertaken to combat climate change (note here).
Relatively faster growing species tend to remain relatively faster growing even after longer time-frames. This may be an argument for rigorous species selection, as discussed further in our data-file here.
This data-filecontains our estimates into the economic costs of unmitigated climate change, using the latest disclosures from the IPCC as a framework. We estimate the total costs could reach $1.5trn per annum.
Our numbersinclude losses of labor productivity in hot countries, increased air conditioning demand (offset by lower heating demand), granular data into the cost of natural disasters, which could become increasingly prevalent (chart below), losses in the tourism industry, water scarcity, healthcare costs, et al.
The purpose of this data-file is to disaggregate the energy economics of combusting different fuels, including natural gas, different oil products, NGLs, coal, hydrogen, methanol, ammonia et al.
In each case, we derive the enthalpies of combustion, the contributing share of carbon and hydrogen oxidation, and thus the CO2 emissions per unit of energy. We also derive the fuels’ energy density, expressed in kWh/kg, kWh/mcf and kWh/gallon.
There is recent enthusiasm to lower combustion emissions by blending hydrogen into gas grids. But materially greater decarbonization occurs by replacing coal/biomass with gas, or even replacing oil products with NGLs.
This data-file estimates the GDP generated by manufacturing across 200 countries, each year from 1960-2019.
The dataare derived from international organizations, government statistics and technical papers, and then extensively cleaned.
Our conclusionis that policies have long-term consequences. The largest decline is in former Soviet countries, which have lost c15pp of market share from peak. Conversely, China has risen from 1% to 30% of global manufacturing over the same period.
The US and Europehave declined from c65% of global manufacturing in 1960 to c35%, as absolute value add from manufacturing has effectively stagnated since 2000. The data may be worth considering, as energy policies intensify.
This data-file estimate the costs of blending hydrogen into pre-existing natural gas pipeline networks. Costs are relatively low per mcf of gas, but very high per ton of CO2 abated. Costs also rise exponentially, as more hydrogen is blended into the mix.
Our estimatesare based upon technical papers and TSE’s economic models, and they cover capacity degradation, maintenance costs and upgrading boilers, appliances and turbines.
The purposeof this data-file is to provide helpful data on how land is used globally, and thus to quantify how much land is available for nature based solutions such as reforestation.
The workincludes a breakdown of land use, across 40 different categories, estimates of carbon stocks across these different land types, and more granular detail into agricultural lands, grasslands, degraded lands and reforestable lands.
Estimatesare mostly derived from technical papers. Notes from select technical papers are also included in the data-file.
This data-file contains the outputfrom some enormous data-pulls, evaluating UK grid power generation by source, its volatility, and the relationship to hourly traded power prices. We conclude the grid is growing more expensive and volatile, particularly for ‘downside volatility’.
Different tabs in the data-filecover the total monthly demand of the UK power grid since 2016, broken down by generation source, month-by-month and smoothed over trailing twelve-month timeframes; statistical analysis of hourly power prices, by day and by quarter; and an hourly cross-correlation of wind generation with power prices (chart below).
The data-file is also regularly updated and we are happy to run bespoke analysis on the underlying data-sets for TSE clients.
This data-file tabulates key details of prior investment bubbles, such as their timing, price performance, causes of the boom and causes of the bust. We pick out five factors common to all prior bubbles.
Covered bubblesinclude the Dutch Tulip mania, British South Sea bubble, French Mississippi bubble, British Railway mania, Roaring Twenties boom, Dot Com and the lead-up to the 2008 Financial Crisis. Each is described in detail.
It is interestingto compare the characteristics of these past bubbles to New Energies today. We argue several transition technologies are at risk of becoming bubbles.
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