By Andy May

Poverty and access to energy are closely related. Although it probably isn’t possible to show that access to energy is the key reason so many have been lifted out of poverty in recent decades, the data and logic suggests that this so.  In the United States, the average person uses about 300 million BTUs of energy per year according to the EIA.  This is equivalent to the manual labor of 69 healthy people working hard for 6 hours per day.  Worldwide, the average person uses 73 million BTUs, the equivalent of 16 hardworking people.

Prior to the industrial age, which began with the first practical coal- and wood-fired steam engines between 1712 and 1776, slavery, bonded servants and serfs were common, this group made up over 90% of the world’s population in 1800.  For a few people to live well they needed lots of servants and domestic animals to do the manual labor for them.  Now, in the age of electricity, petroleum and nuclear powerplants, most manual labor can be done by machines.  No longer do a few wealthy people live from the labor of others, everyone who has access to energy can live well.  Before the industrial age, nearly everyone was extremely poor as seen in Figure 1, today fewer than 10% are extremely poor.

         Figure 1.  The percent of the population in extreme poverty (income of less than $1.90 per person per day or about $2,774 per year) in 2011 PPP (purchase power parity) dollars is shown in orange with the scale on the right. Total fossil fuel consumed, in Terawatt-hours (one terawatt is 1×10¹² Watts) is shown in blue with a scale on the left.  The data on extreme poverty and fossil fuel consumption was downloaded from ourworldindata.org by Hannah Ritchie and Max Roser. The early data is from (Bourguignon and Morrisson 2002) and the later data from the World Bank.

While over 90% of people lived in extreme poverty for most of 19^th century, extreme poverty has since been completely eradicated in the western world and is disappearing at a rapid and accelerating rate in the third world. Figure 2 shows the number of people living in extreme poverty for several selected areas and the whole world.  The United States, the European Union and Canada are not plotted because no one in those countries is living in extreme poverty.

Figure 2. The number of people in extreme poverty in selected areas and for the whole world. Downloaded from ourworldindata.org, data collected by the World Bank.

 

The only significant increase in the number of extremely poor in Figure 2 is in sub-Saharan Africa and this increase has flatten considerably since 2010.  Most of the remaining very poor live in China, India and Africa.  The very poor typically have no indoor, improved, running water or sanitary facilities, no electricity, what shelter they do have is in poor shape, they have no access to transportation and often no schools.  Their energy is from burning wood, coal, or dung, indoors and without proper ventilation. This is the primary reason over four million people die of indoor air pollution each year according to the World Health
Organization.

In this post, we use the World Bank Organization’s definition of extremely poor, which is an income of less than 2011 PPP$1.90 per day per person.  PPP$ are purchase-parity US dollars, they are corrected for the differences in the price of essential goods from country to country.  This amount seems different than the classical definition of $1/day previously used, but since that was in 1996 dollars and the new definition is in 2011 dollars they are nearly the same.  In 2011 dollars, any family of four is extremely poor if their total income (technically their total consumption, which includes charity) is less than $2,774 per year.
Homi Kharas in a Brookings Institution 2017 white paper entitled “The unprecedented expansion of the global middle class” has estimated that 140 million people are joining the world middle class annually and the number appears to be growing each year.  At the end of 2016, there were 3.2 billion people in the middle class, an astonishing number, this is over 42% of the Earth’s population.  Kharas defines the world middle class as a family of four earning more than $16,060 per year and less than $160,600 per year in 2011 PPP$.

The World Bank calls the group between extremely poor and the middle class (those who live on $1.90/day to $10 per day) “low income.”  This group is growing rapidly also. By way of contrast, the poverty definition in the United States for a family of four is an income below $24,339 per year, not including the value of free food, free medical care and housing assistance.  Taxes in the United States rise rapidly with income, yet the higher taxes are not taken into account when comparing the income of the U.S. defined “poor” to the income of higher earners, this radically distorts the measurement of inequality (see this report by John Early for more details). By world standards, a family with this much income is solidly upper-middle class. But, “poor” people in the United States have cell phones, cars, apartments, running water, sanitation, access to
schools, and disposable income nearly equal to the U.S. low income and middle-class groups.  These are items only the middle class and more affluent have in less developed countries.

The correlation between the reduction of extreme poverty and total fossil fuel consumption is very enticing in Figure 1.  Figure 3 shows the relationship using data from 1820 through 2015.  The data was downloaded from ourworldindata.org.  The early energy data shown in Figure 3 is from Vaclav Smil’s book Energy Transitions: Global and National Perspectives (2^nd edition) (Smil 2017) and the later data is from the BP Statistical Review of World Energy here. The poverty data is from (Bourguignon and Morrisson 2002) and the World Bank.

 

Figure 3. Total fossil fuel use is on the X axis and the percent extreme poverty of total world population is on the Y axis.  The relationship is strong with an R² of 0.99.  Other sources of energy, such as hydroelectric or nuclear were ignored since they were invented late in this period, which covers 1820 to 2015.  The relationship suggested by the regression is that extreme poverty is reduced by 1% for every 1,628 TWh increase in global fossil fuel use.  The regression is significant at an F of regression of 3.7×10^-21, that is much higher than 99.999. There are 23 values plotted and used in the regression. The data on extreme poverty and fossil fuel consumption were downloaded from ourworldindata.org by Hannah Ritchie and Max Roser.

Figure 3 does not prove that the level of extreme poverty was reduced due to the increased use of fossil fuels, but it shows they are strongly related and that the use of fossil fuels might have been the main reason for the reduction in poverty.  Energy use and poverty are unevenly distributed in the world today.  Figure 4 shows the global distribution of total energy use.

Figure 4. Energy use in the world, data and plot from ourworldindata.org by Hannah Ritchie and Max Roser.

 

As we would expect, Figure 4 shows that the wealthier countries use much more energy than the poorer countries.  When total energy used is plotted against GDP in 2011 constant international dollars, we get the plot in Figure 5.  This is a plot of data from 211 countries and regions.  The data was downloaded from
ourworldindata.org and gathered by the United Nations, IEA and the World Bank.

Figure 5. Plot and regression of GDP per capita in constant international 2011 dollars versus energy use in kg of oil equivalent. There are 212 countries and regions plotted, the points are the average for the period 1990 to 2014. Data downloaded from ourworldindata.org by Hannah Ritchie and Max Roser.  Original data sources are the IEA and the World Bank.

 

Figure 5 plots primary energy used per capita versus GDP per capita.  The points are the average annual totals for both values from 1990 to 2014. The correlation isn’t great, but acceptable, the F of regression is significant at the 1.4×10^-67 level. This suggests the two values plotted are strongly related and that for
every kg of oil equivalent consumed per person we can expect GDP to grow an average of 53 cents per person over the period from 1990 to 2014 in 2011 PPP$.  There are 140 kg of oil in a barrel, which costs about $68 today (roughly $63.50 in 2011$) or 45 cents/kg.  The same values can be viewed in the time
dimension as shown in Figure 6.

Figure 6.  Over the years with data, energy use and GDP show the same trend in some detail. Original data sources are the IEA and the World Bank.

 

The correlation seen in Figure 5 is also visible in the data plotted in Figure 6.  In Figure 7 we compare worldwide energy used per capita to GDP per capita, but each observation is a year, rather than a country or region like in Figure 5. Remember that in Figure 5 we simply averaged the data from 1990 to 2014, in
Figure 7 the years are plotted separately.

Figure 7.  Worldwide energy used in kg of oil equivalent versus GDP in constant international 2011 dollars.  Each observation is one year (1990-2014). Original data sources are the IEA and the World Bank.

The R² has improved over Figure 5 with this yearly view. Even though we have 25 observations in this regression versus 212 in Figure 5, the F statistic of the regression is significant at 1.2×10^-12.  As people move from poverty to the lower and middle classes they use more energy, which is why the energy growth is
concentrated in Asia and the Pacific as seen in Figure 8.

Figure 8.  Energy consumption in the world by region. From ourworldindata.org by Hannah Ritchie and Max Roser. Original data sources are the IEA and the World Bank.

In Figure 9 we see the fossil fuel data for selected countries and regions.  Clearly, while fossil fuel use is declining in the EU, OECD and in the U.S., it is increasing much more rapidly in Africa, China and India.  This is partly because many more people live in Africa, India and China; but also because of the rapid decline in extreme poverty in the third world.  As people come out of poverty they use more energy, they use it for essential things like clean water, sanitation and healthy food; but also, for luxuries like cars, air conditioning,
heating and cell phones.

Figure 9. Fossil fuel use for selected countries and regions. Data downloaded from ourworldindata.org, collected by the IEA.

Per dollar of GDP, low-income people use more energy.  But, when their income rises, they use energy
more efficiently, this can be seen in Figure 10. The figure shows the GDP per energy used ratio for low-income and high-income countries.  This may be because high-income countries have their energy distribution infrastructure in place and low-income countries are still building theirs.  Until the
infrastructure is in place energy delivery is expensive and less reliable.

Figure 10.  Energy efficiency expressed as GDP/energy used for both high-income and low-income countries. Data downloaded from ourworldindata.org, collected by the IEA.

China is well along in their energy infrastructure build-out and as can be seen in Figure 9 the rapid growth in their fossil fuel use has leveled off after a very rapid increase from 2002 to 2013.  Coal is the most popular fuel for generating reliable electricity by far.  Coal, regardless of what you may hear from the blathering news media, still produces 40% of the worlds electricity, see Figure 11.

Figure 11.  Coal is the largest primary fuel for making electricity in the world today and for the foreseeable future.

While worldwide coal use dropped from 2013 to 2016, it increased in 2017. It is expected to remain the dominant primary energy source for electricity through 2030 by ExxonMobil and the IEA.

Figure 12 shows that during the build-out of the Chinese energy infrastructure coal use skyrocketed in the country.

Figure 12. Coal consumption in selected countries. Data downloaded from ourworldindata.org.

 

Africa, a continent with limited energy infrastructure, but over a billion mostly poor people, shows very little change from year-to-year.  India’s coal use is beginning to pick up and looks like China 25 years ago.  India has huge coal reserves, the fifth largest in the world and over a billion people, mostly very poor.  The country is advancing economically very fast and since access to reliable electricity is vital to a robust economy, it
may follow the path of China.  India’s commitment to reduce CO₂ emissions and coal use in the Paris agreement is as a percent of GDP (energy efficiency) so if they follow the normal course of infrastructure development, like China did, they meet their commitment, but their CO₂ emissions increase.

Electricity is somewhat wasteful because only 28% of the primary energy used to generate it is delivered in a useable form to the consumer (EIA 2012), but electricity is none-the-less essential for water and sanitation facilities, hospitals and communications.  For these reasons and many more, access to electricity is vital for economic growth and reducing poverty as seen in Figures 13 and 14.

 

Figure 13.  Plots of per capita GDP and percent of the population with access to electricity by year for the world. Data downloaded from ourworldindata.org.

 

Figure 14. The correlation between the percent of the population with access to electricity and per capita GDP for the period 1990 to 2014. Data downloaded from ourworldindata.org.

Like fossil fuel use and total energy use, use and access to electricity is very closely correlated with prosperity. Due to the importance of electricity for creating and delivering clean water, proper sanitation and
health care, it is also vital for a population’s health.

 

Conclusions and Discussion

The correlation between access to energy, especially electricity, and economic prosperity is very strong.  This strong correlation suggests, but does not prove, that available, affordable energy is essential for reducing
poverty.  It also suggests the opposite, that withholding energy from a population will lead to more poverty.  Other factors can cause poverty, government corruption, runaway crime, and war are examples; but surely access to affordable energy is a critical component of economic prosperity.

In 1990 there were about 2 billion people living in extreme poverty, this has now been reduced to less than 700 million.  Currently over 200,000 people move out of poverty and into the lower or middle class every day.  Income inequality in the world is lower today than at any time in the history of humankind.  Even in the United States, inequality has been lowered substantially; regardless of what is seen in the news media.  Phil Gramm and Robert Ekelund have even speculated that it was the rising equality between working people and welfare recipients in the U.S. that led, in part, to the election of Donald Trump as President, from their June 24, 2018 op-ed in the Wall Street Journal:

 

“The new analysis [(Early 2018)] was published in April by the Cato Institute’s John F. Early, a former assistant commissioner of the Bureau of Labor Statistics, and it provides the most comprehensive accounting to date of how taxes and government payments affect income distribution in the U.S. His study includes the roughly $1 trillion of annual government spending not currently counted in the U.S. Census Bureau’s income-distribution tables. That includes Medicaid, food stamps, the earned-income tax credit, and 85 other federal payments and services, along with similar state and local income supplements. The study also subtracts federal, state and local taxes from individuals’ measured income, an adjustment not contained in the census data.

The most surprising finding is the astonishing degree of equality among the bottom 60% of American earners, generated in part by the explosion of social-welfare spending and the economic and wage stagnation during the Obama era. Hardworking middle-income and lower-middle-income families must have recognized that their efforts left them little better off than the growing number of recipients of government transfers. The perceived injustice of this equality helped drive the political shift among blue-collar workers, many of whom supported the pro-growth candidacy of Donald Trump in 2016 despite having voted for Mr. Obama in the two previous presidential elections.”

Due to the steep increase in the tax rate with rising income and the large size of government payments to the bottom quintile of the U.S. population, the study by John Early found that while the bottom quintile earned only 2.5% of all earned income, they have virtually the same share of spendable income as the second quintile.  Middle income workers earned almost six times what the bottom quintile earned, but only had 20% more spendable income.  They conclude this basic unfairness to the working class was noticed by the public and played a large role in electing the President.

Judge William Alsup, a United States District Judge in California, recently dismissed a lawsuit by several California communities against a number of oil and gas companies.  The California communities were seeking damages against a potential future rise in sea level that they claim is due to carbon dioxide emissions from burning oil and gas.  In that judge’s opinion (text here) he states the thesis of this post reasonably well:

“With respect to balancing the social utility against the gravity of the anticipated harm, it is true that carbon dioxide released from fossil fuels has caused (and will continue to cause) global warming.  But against that negative, we must weigh this positive: our industrial revolution and the development of our modern world has literally been fueled by oil and coal.  Without those fuels, virtually all of our monumental progress would have been impossible.  All of us have benefited. Having reaped the benefit of that historic progress, would it really be fair to now ignore our own responsibility in the use of fossil fuels and place the blame for global warming on those who supplied what we demanded?  Is it really fair, in light of those benefits, to say that the sale of fossil fuels was unreasonable?”

The judge makes the point that we cannot condemn fossil fuels, especially coal, or outlaw their use, without also considering the consequences of the actions.  Restricting or eliminating fossil fuels, or even increasing their cost, will almost certainly slow the reduction of poverty in the world or even increase the level of poverty.  Is this fair?

 

Works Cited

Bourguignon, François, and Christian Morrisson. 2002. “Inequality among World Citizens: 1820-1992.” The American Economic Review 92 (4). https://ift.tt/1tniCY9.

Early, John. 2018. Reassessing the Facts about Inequality, Poverty, and Redistribution. CATO Institute, CATO Institute. https://ift.tt/2Kl1O5P.

EIA. 2012. “Electricity’s share of U.S. delivered energy has risen significantly since 1950.” Today in Energy,
March 2. https://ift.tt/2IsWGI2.

EIA. 2017. “What is U.S. electricity generation by energy source?” https://ift.tt/2mDFipT.

Smil, Vaclav. 2017. Energy Transitions, Global and National Perspectives. Santa Barbara: Praeger.
https://ift.tt/1Li211V? l=en&lr=&id=X2doDQAAQBAJ&oi=fnd&pg=PP1&dq=Energy+Transitions:+Global+and+National+Perspectives+(2nd+edition)+&ots=sdzvQtrjOU&sig=CleGMY22BPMcJrOAbgbJJ1Wp-Vo#v=onepage&q=Energy%20Transitions%3A%20Global%20and%20National%20Pe.

 

 

 

 

 

 

 

 

 

 

 

Guest essay by Tom Quirk

When the King of Siam wished to rid himself of a troublesome courtier, he would send a white elephant as a first sign of oncoming ruin. Our federal government has served up a troublesome elephant of a plan, the NEG (National Energy Guarantee) that might ruin our country.

The information used in this analysis is sourced from the Department of the Environment and Energy, Australian Energy Statistics, and gives the sources of electricity generation for the calendar year 2017. The generation plant data comes from the AEMO.

This note will explore what could happen were one of the key conditions of the NEG was actually met – the condition that CO2 emissions from electricity generation should move to average 0.4 tonnes of CO2 per megawatt hour of electrical energy.

South Australia has already achieved this NEG goal but at great cost to consumers whether domestic or business.

The table below lays out the mix of generators. Coal fired generation has ceased, wind farm energy takes priority in the market and the inter-connectors to the Victorian power system keeps South Australia from having too many blackouts by supplying some 15% of demand.

Renewables meet 37% of demand while Victoria supplies 15% and the NEG target is achieved with an average of 0.38 tonnes CO2 per MWh.

South Australia Electricity Generation 2017
	Generation GWh %	Plant MW	Utilis- ation	tonnes CO2/MWh
Non-renewable fuels
Black coal
Brown coal
Natural gas	7,699	47%	2,853	31%	0.48*
Oil products	110	1%			1
Total non-renewable	7,809	48%
Renewable fuels
Biomass	92	1%			0
Wind	4,803	29%	1,810	30%	0
Hydro	3	0%			0
Large-scale solar PV	5	0%			0
Small-scale solar PV	1,091	7%			0
Geothermal		0%			0
Total renewable	5,995	37%
Total generated in SA	13,804	85%			0.27
Interconnects to SA	2,600	15%			0.95
Total consumption SA	16,404	100%			0.38

* Weighted average for CCGT, OCGT and thermal gas plants

So turning to the other states, New South Wales, Queensland, Tasmania and Victoria that are in the National Energy Market, the table below is for the same period as the South Australian analysis above.

In these states renewables generate 15% of supply with half coming from hydro plants. Coal and gas provide 85% of supply and the CO2 emissions average of 0.89 tonnes CO2 per MWh is well above the NEG target of 0.4 tonnes CO2 per MWh.

NSW, Qld, Tas and Vic Electricity Generation 2017
	Generation GWh %	Plant MW	Utilis- ation	tonnes CO2/MWh
Non-renewable fuels
Black coal	109,890	54%	18,286	69%	1
Brown coal **	38,313	19%	5,285	83%	1.5
Natural gas	22,372	11%	8,759	29%	0.48*
Oil products	2,137	1%			1
Total non-renewable	172,712	85%
Renewable fuels
Biomass	3,351	2%			0
Wind	6,337	3%	2,656	27%	0
Hydro	13,711	7%	7,939	20%	0
Large-scale solar PV	708	0%			0
Small-scale solar PV	5,852	3%			0
Geothermal	1	0%			0
Total renewable	29,960	15%
Total generation	202,672	100%			0.89

* Weighted average for CCGT, OCGT and thermal gas plants

** Hazelwood operated for 3 months of 2017

So what might happen if this same energy were to be generated under the NEG target of 0.4 tonnes CO2 per MWh?

The final table shows what changes to generator plant might be made up to 2030 to reach the NEG emission target while generating the same energy as in 2017.

NSW, Qld, Tas and Vic Electricity Generation 2030 with NEG target
	Generation GWh %	Plant MW	Utilis- ation	tonnes CO2/MWh
Non-renewable fuels
Black coal	17,938	9%	3,000	68%	1
Brown coal	7,300	3%	1,000	83%	1.5
Natural gas	120,000	59%	40,000	34%	0.48
Oil products	2,137	1%			1
Total non-renewable	147,374	73%
Renewable fuels
Biomass	3,545	2%			0
Wind	24,000	12%	10,058	27%	0
Hydro	20,000	10%	10,000	23%	0
Large-scale solar PV	761	0%			0
Small-scale solar PV	6,991	3%			0
Geothermal	1	0%			0
Total renewable	55,297	27%
Total generation	202,672	100%			0.43

A comparison of the present and the desired outcome points to the following

• 15,000 MW of black coal burning power stations have been closed. This leaves 3,000 MW of plant that operate with 71% utilisation (in AEMO speak – Capacity Factor). For low cost base load power, these plants need 70% or more utilisation, ideally 87% (IEA figure).

• 1,000 MW of brown coal burning power station (Loy Yang B) remains in Victoria. As a base load power station it will only supply 25% of the steady 4,000 MW demand during the early morning hours in Victoria.

• Natural gas usage has increased six-fold with a four-fold increase in plant. Generators are no longer simply meeting demand changes in periods of high demand but are having to meet sudden changes from intermittent supply sources. How the extra gas will be sought is a mystery with governments stopping the search for new gas sources. Perhaps LNG will be shipped from Queensland or Western Australia.

• Wind farms have increased from 2,600 to 10,000 MW. The Victorian government wants to add 4,000 MW of wind farm. This is sufficient to destroy the high utilisation necessary for baseload generation. Other states may be just as ambitious. The southern states of New South Wales, South Australia, Tasmania and Victoria all share common weather patterns so there will be periods where correlated wind farm generation will fall towards zero. This has already been seen. No allowance for backup supply has been considered in this analysis but new inter-connections will not be much help balancing coherent wind power variations.

• Hydro has been increased assuming “Snow you too” is built. This scheme, like batteries in South Australia, depends on buying low and selling high where you must buy 20% more energy than you sell. If there is little baseload pricing in the wholesale market this may be an NBN-like venture as the operating surplus will have to meet financing costs.

• Small scale solar photo voltaic systems are a completely uncontrollable source of demand variation. Encouraged by direct state grants this has been a religious indulgence for the better-off.

No attempt has been made to estimate the costs for these changes or the prices consumers would pay. But if South Australia is setting an example then the prices will be amongst the highest in the world. The consequence will be smelters closing and other energy intensive processes moving elsewhere in the world.

The conclusion from this analysis is that the political and policy-making class have taken us and the white elephant into a labyrinth of regulations that will further disrupt electricity supply. Whether we meet the Minotaur or the elephant, like Theseus has a piece of string to help us escape remains to be seen.