Notes

1. Unless otherwise specified, all values in this chapter are expressed in US dollars measured in constant 2018 prices. 2. The SEEA-CF recognizes geothermal, hydro, solar, wave and tidal, and wind energy resources. 3. Other approaches are also possible. One that has been applied to the valuation of hydroelectric resources in Canada (Bernard, Bridges, and Scott 1982;

Gillen and Wen 2000; Zuker and Jenkins 1984), Iceland (Hreinsson 2008a, 2008b), and Cameroon (Wandji and Bhattacharyya 2018) is the least-cost alternative method. In this method, resource rent is calculated as the difference in cost between using a given resource (say, hydroelectric resources) in a given production process (electricity generation) and using the next least expensive alternative (say, coal-fired thermal generation).The method is complex and data intensive. As Young and Loomis (2014, 213) note, “The analyst who undertakes to estimate the alternative cost of electricity generation ‘from scratch’ faces a major task.”Another approach is the appropriation method, in which resource rents are assumed to be equal to the payments (for example, license fees and royalties) that governments demand from resource companies in return for the right to exploit resource assets. For a variety of reasons, the value of these payments does not usually reflect the full value of the underlying resource assets (see SEEA-CF paras. 5.126–5.130). 4. In the cases of solar and wind electricity production, results are presented from whatever year production began until 2017. Results for the Russian

Federation are presented beginning in 1992 and include hydroelectricity assets only, as the country was part of the former Soviet Union prior to 1992 and it did not produce meaningful quantities of solar or wind electricity from 1992 to 2017. Results for Brazil are presented beginning in 1995, the first full year of circulation of the new Brazilian real that was introduced in mid-1994.

Results for Turkey are presented beginning in 2005, the first full year of circulation of the newTurkish lira that was introduced at the end of 2004.Electricity prices denominated in the predemonetization currencies in Brazil and Turkey were unavailable, so results for those periods would not have been comparable with Brazilian or Turkish figures postdemonetization or with other countries predemonetization. Results for Germany in 1990 (prior to unification of the former East Germany and West Germany) were calculated based on 1990 data for the former West Germany and assumptions about the level of renewable energy production in the former East Germany in that year. 5. See International Renewable Energy Agency, Statistics Time Series database,

Abu Dhabi, https://www.irena.org/Statistics/View-Data-by-Topic/Capacity -and-Generation/Statistics-Time-Series. 6. In principle, an asset cannot have a negative value (otherwise, it is a liability rather than an asset), so negative asset values should really be treated as zeroes.

However, they are treated as negatives here to show “how far” renewable energy assets (especially solar and wind assets) are from making positive contributions to national wealth. 7. There is also an argument that the system costs of maintaining reliable electricity supply with a large share of variable renewable generation should be included in the cost formula. This cost, however, arises at much higher levels of solar and wind energy market penetration than observed in most countries

so far. Furthermore, if this externality of renewable energy were to be included, so should be the environmental cost of thermal generation. Both are considered as possible future developments of CWON. 8. Weights are the shares of the value of a country’s asset in the total value of this asset in all sample countries. 9. The capacity factor measures the actual amount of electricity generated as a share of the potential amount that could be generated if a system operated at maximum output over a period. India’s 1990 capacity factor of 0.436 had fallen to 0.303 by 2017. In Japan, the capacity factor fell from 0.293 to 0.206 over the same period. 10. A GWh is a unit of energy approximately equal to 590 barrels of oil. It is enough to meet the electricity needs of about 100 average Canadian homes for a year. 11. It was assumed that hydroelectricity was remunerated at the average annual electricity spot price. In reality, some hydroelectric producers likely received less than the spot price through long-term contracts. In such cases, hydroelectric prices may not have fallen as much over time as estimated here, although they would likely have been lower in the early years of the time period than estimated. 12. Turkish energy prices were rising when measured in lira but declining when measured in US dollars because of a decline in the value of the lira versus the

US dollar. 13. Renewable energy asset wealth may be negative while exploitation of those assets to generate electricity remains profitable in the short run. In the long run, however, private profitability in the face of negative asset values can be maintained only if government subsidies are provided or private producers are willing to accept lower returns on their investments than they could expect elsewhere in the economy. 14. In 2017, Statistics Canada estimated that selected natural resource assets in

Canada were worth the following: land, Can$4,208 billion; fossil fuels,

Can$377 billion; timber, Can$236 billion; and minerals, Can$101 billion (or approximately US$3,237 billion, US$290 billion, US$182 billion, and

US$78 billion, respectively). See Value of Selected Natural Resource Reserves (x 1,000,000), database, Statistics Canada, Ottawa, https://www150.statcan .gc.ca/t1/tbl1/en/cv.action?pid=3810000601. 15. These projections are based on simple extrapolations of the data collected for 1990–2017 and rough assumptions on the evolution of renewable electricity technologies and markets. They are likely to have considerable margins of error. 16. CSP assets are not considered in this assessment because no country has long experience with this technology. 17. This study applies a least-cost long-run power sector expansion planning model for the South Africa Power Pool, which connects electricity markets in several countries in the southern tip of the continent. It was developed by the

World Bank Power System Planning Group (Chattopadhyay et al. 2020). The model uses exogenous demand projections for each country in the pool and allows electricity trade and free entry of new generation plants choosing new capacity only on an economic basis, constrained by standard operational characteristics including transmission and cross-country interconnections capacity.