The purpose of this data-file is to assess how ‘industrial activity’ has changed, in China and in the West (US and Europe), from 1999-2019, as a proxy for the phenomenon called ‘carbon leakage’.
To do this, we have quantified volumes of specific materials (steel, aluminium, copper, plastics, glass, industrial acids) and manufactured goods (cars, refrigerators, textiles, washing machines).
Our broad conclusion is that heavier industrial activity is down 12% in the West over this 20-year period, and up 6.5x in China. At the same time, GDP is up c50% in the West and up 5.5x in China.
The greatest leakage has likely occurred for cars, electronics and machinery. Although other sectors such as solar panels or lithium batteries, where China dominates the supply chain cannot be measured in this data-file, as there is no 1999 baseline.
This data-file profiles some simple details into facilities that produce sulphuric acid, out of the 1,000 such facilities globally.
This is one of the largest commodity chemical marketsin the world, at around 270MTpa, used primarily in making phosphate fertilizers, but also more broadly across a vast number of different industries.
Around two-thirds of the sulphur is sourced from the oil and gas industry, for example, in refineries and natural gas production facilities. This does raise the question for how the world would source sulphuric acid in the unlikely event that oil and gas were phased out as part of the energy transition.
This data-fileaims to break down the costs of decommissioning solar projects. Gross costs are estimated within a range of $0.03-0.20/W, which is around 3-20% of the initial installation costs.
This is better than nuclear, offshore wind and coal decommissioning costs, but worse than natural gas (data are shown in the file).
What might help the economics for solar is the ability to re-use old panels, in markets that are particularly price sensitive. In the best cases, this could allow zero-cost decommissioning of solar assets or possibly even a small profit.
Re-deployingold solar panels could also accelerate the global deployment of solar by c5%. Our notes, conclusions and numbers are built up in the data-file.
The purpose of this data-file is to examine the correlations between different wind farms’ generation rates. Specifically, we obtained and cleaned-up half-hour-by-half-hour power generation data from c20 wind assets around the UK, in Megawatts (MW).
The output from individual wind farms was 67% correlated on average, at any given point in time. This correlation varies with distance, reaching as high as 90% within a 100km x 100km area, and dropping to 50-60% within a 750km x 750km area.
Auto-correlation was also high, as each wind farm’s generation was 80% correlated with its own generation 5-hours earlier or later; and the correlation still held at c25% c24-hours later. Windy and non-windy periods routinely last several 2-10 days.
What implications? This all makes it challenging to back up a wind-powered grid with batteries, but it is advantageous for demand-shifting. Inter-correlations between different solar assets is tabulated here.
The purpose of this data-file is to estimate the cost of land, which matters for renewables and reforestation projects, but also amidst rising inflation.
Our main conclusion from trying to compile the data-set is that there is no such thing as a “land price” as there is for commodities. Even with individual countries, there are 100x variations. European arable land prices, for example, range from $700 to $700,000 per acre. Prices in the emerging world are even more opaque, ranging from $13/acre in parts of Africa through to $7,000 acre for oil palm plantations in Malaysia, through to tens of thousands of dollars on the outer belts of sprawling new cities.
Nevertheless, the data-file supports a vast availability of low-cost land for reforestation. Ballpark estimates are aggregated in the first tab, alongside our notes. Granular US and European data are summarized in subsequent tabs. For the emerging world, individual land offerings are assessed in the final tab.
This data-file aims to estimate how much inflation is likely to result from policies to decarbonize the global economy. Specifically, we have used our models to flow through the impacts per $100/ton of CO2 abatement costs.
Aggregate price levels might rise by 6% per $100/ton of CO2 abatement costs. New energies costs rise by 6-30%. Mobility and food rise by 15%. And materials costs rise by an average of 40%.
Underlying calculations are summarized here, with links to our other data-files and models.
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