The systemic problem is to understand what influence the gold price?

Many articles say that the gold price is manipulated and some analysts predict that the bubble will burst. (1)

We think that understanding how gold can be influenced by different factors is an interesting research topic. The variation of the gold price is a real-world problem which evaluates through the interaction of a group of different elements.

It seems that the gold price is a very complex problem understanding. Of course everybody has his own thinking about the problem according to his own filter.

But this approach is most of the time not valuable because there is not a full view of all the variables and their link. In a context of a growing demand and a constant supply, be able to determine if gold price will continue to increase and if this asset will represent a safe investment for the new decade.

In September 2011, gold price surged a record, $1,274,75 an ounce. According to the Commodities guru George Soros “gold was the ultimate bubble" and was no longer a safe investment.

On the other hand, the research conducts by metal consultant GFMS predicted that gold will hit a new record of $1,300 an ounce. (2)

Who was right? Both of them. 

This example illustrates how complex is the problem.

At the time of this research the price of gold is $1,316,79 an ounce.

Wealthy persons are concerned by preserving their fortune, they also look to maximise their wealth and to keep it safe. Many options are available to investors, despite buillion is a popular asset on a long-term portfolio, nowadays is it gold a safe investment? That is a good question. Also understanding the impact of gold on the economy and how it is link to poverty might be interesting. To analyze an issue, one must first define it.

In order to get a better understanding of the gold price we will model this complex problem. Our goal is to visualize the interconnection of elements and be able to identify feedback loops with the aim to understand the complexity of the problem.

We will analyse different documents from various sources, underline variables and identify their relationships over time.

 

 (1) https://www.moneymetals.com/news/2017/04/28/who-controls-gold-price-001058

 

 (2) https://www.bullionbypost.co.uk/index/gold-investment/is-gold-a-safe-investment/

 

Finance Gold Economy USA FED

C8 Article through the Economy lense

Economy C8

'Efficiencyism’  can be described as a blind belief in the effectiveness of efficiency measures without taking into account circumstances and the wider context.   The graph on the left shows how the frequent use of the term 'efficiency' at the level of local interactons can lead to the emergence of  'efficiencyism' through upward causation, denoted by the arrows pointing upwards.  However, there is also downward causation from the global level depicted by the red arrows which can increase the blind application of efficiency measures at the local level. In other words, efficiency for the sake of efficiency becomes a dominant idea.  The tyrannical influence of 'eficiencyism' affects all of us to varying degrees and unfortunately can often have very negative side effects, such as an increase in unemployment, social injustice and even increase inequality.  Of  course, well thought out efficiency improvements can also bring great  benefits.   I recommend reading an excellent article by Dr. Charles Chandler, who explains the term 'efficiencyism' with some excellent examples and also points to some  of its undesirable effects.

http://www.ageofoe.com/010-efficiencyism-holds-us-back/

Social Systems SystemsThinking Downward Causation Emergence Efficiency Economy

Microeconomic measures can produce counterintuitive 'emergent' effects at the macro or systemic level. The commendable act of saving money by individuals during uncertain economic times has the perverse macroeconomic effect of making a recession  worse: in aggregate there will be less money available for spending, suppressing demand for goods and services. Economists call this effect 'the paradox of thrift'. Similarly, logical efforts by companies in such conditions to reduce their wage bill or their postponement of investment decisions will reduce spending in the economy  and deepen the economic downturn.

What can be done to counteract this harmful dynamic? The missing spending can be replaced by government spending: governments have it within their power to effectively counter economic downturns!

Economy Emergent Phenomena Fiscal Spending Paradox Of Thrift

​In a recent report, the World Economic Forum considered that the use of robots in economic activity will cause far more job losses in the near future than there will be new ones created. Every economic sector will be affected. The CLD tries to illustrate the dynamic effects of replacing human workers with robots. This  dynamic  indicates that if there is no replacement of the  income forgone by the laid off workers, then the economy will soon grind to a halt. To avoid disaster, there must be enough money in circulation, not parked in off-shore investments, to permit the purchase of all the goods and services produced by robots. The challenge for the government is to make sure that this is  case.  

Robots Economy Social Employment

When people talk about a government deficit, they forget that this is only one side of the ledger. On the other is a corresponding non-government SURPLUS. The money the government spends is not lost but shows up in the private sector as income. When one talks only of the deficit then one can understand that many think it should be reduced or even converted into a surplus, but reducing the government deficit reduces private sector income and a government surplus forces a deficit on the private sector with a potentially devastating effect on private sector wealth and economic activity.  Unless the economy is overheating, government deficits are usually healthy. For countries that run traditionally a trade deficit, such as the US they are necessary to maintain economic activity. Consider this fact: for almost all of past 40 years the US and the UK have run deficits without any harmful effects!
This video by professor Stephanie Kelton contains evidence that supports the modle.

https://www.youtube.com/watch?v=g6rlprwQB5E

Government Deficit Economy Private Sector Wealth

Environment Sustainability Society Economy

A detailed description of all model input parameters is available here. These are discussed further here and here.

Update 6 August 2018 (v2.8): Updated historical wind and PV deployment data for 2016-2017, adding projected PV deployment for 2018. Data via https://en.wikipedia.org/wiki/Growth_of_photovoltaics and https://en.wikipedia.org/wiki/Wind_power_by_country.
Update 26 October 2017 (v2.7): Updated historical wind and PV deployment data for 2015-2016, adding projected PV deployment for 2017. Data via https://en.wikipedia.org/wiki/Growth_of_photovoltaics and https://en.wikipedia.org/wiki/Wind_power_by_country.
Update 18 December 2016 (v2.7): Added feature to calculate a global EROI index for all energy sources plus intermittency buffering (currently batteries only, but this could be diversified). The index is calculated specifically in terms of energy services in the form of work and heat. That is, it takes the aggregated energy services made available by all sources as the energy output term, and the energy services required to provided the buffered output as the energy input term.

Update 29 June 2016 (v2.6): Added historical emplacement for wind and PV capacity. The maximum historical emplacement rates are then maintained from year 114/115 until the end of the model period. This acts as a base emplacement rate that is then augmented with the contribution made via the feedback control mechanism. Note that battery buffering commences only once the additional emplacement via the feedback controller kicks in. This means that there is a base capacity for both wind and PV for which no buffering is provided, slightly reducing the energy services required for wind and PV supplies, as well as associated costs. Contributions from biomass and nuclear have also been increased slightly, in line with the earlier intention that these should approximately double during the transition period. This leads to a modest reduction in the contributions required from wind and PV.

Added calculation of global mean conversion efficiency energy to services on primary energy basis. This involves making an adjustment to the gross energy outputs for all thermal electricity generation sources. The reason for this is that standard EROI analysis methodology involves inclusion of energy inputs on a primary energy equivalent basis. In order to convert correctly between energy inputs and energy service inputs, the reference conversion efficiency must therefore be defined on a primary energy basis. Previously, this conversion was made on the basis of the mean conversion efficiency from final energy to energy services.

Update 14 December 2015 (v2.5): correction to net output basis LCOE calculation, to include actual self power demand for wind, PV and batteries in place of "2015 reference" values.

Update 20 November 2015 (v2.4): levelised O&M costs now added for wind & PV, so that complete (less transmission-related investments) LCOE for wind and PV is calculated, for both gross and net output.

Update 18 November 2015 (v2.3: development of capital cost estimates for wind, PV and battery buffering, adding levelised capital cost per unit net output, for comparison with levelised capital cost per unit gross output. Levelised capital cost estimate has been substantially refined, bringing this into line with standard practice for capital recovery calculation. Discount rate is user adjustable.

Default maximum autonomy periods reduced to 48 hours for wind and 72 hours for PV.

Update 22 October 2015 (v2.2): added ramped introduction of wind and PV buffering capacity. Wind and PV buffering ramps from zero to the maximum autonomy period as wind and PV generated electricity increases as a proportion of overall electricity supply. The threshold proportion for maximum autonomy period is user adjustable. Ramping uses interpolation based on an elliptical curve between zero and the threshold proportion, to avoid discontinuities that produce poor response shape in key variables.

Update 23 September 2015 (v2.1): added capital investment calculation and associated LCOE contribution for wind generation plant, PV generation plant and storage batteries.

**This version (v2.0) includes refined energy conversion efficiency estimates, increasing the global mean efficiency, but also reducing the aggressiveness of the self-demand learning curves for all sources. The basis for the conversion efficiencies, including all assumptions relating to specific types of work & heat used by the economy, is provided in this Excel spreadsheet.

Conversion of self power demand to energy services demand for each source is carried out via a reference global mean conversion efficiency, set as a user input using the global mean conversion efficiency calculated in the model at the time of transition commencement (taken to be the time for which all EROI parameter values are defined. A learning curve is applied to this value to account for future improvement in self power demand to services conversion efficiency.**

The original "standard run" version of the model is available here.

Energy EROI Economy

ISCI 360 - Project Finale

Fisheries Sustainability Management Economy

Environment Brazil Soy Agriculture Economy Export

This model is an attempt to understand the interactions within an economy in an attempt to determine where the leverage points are to stimulate an economy.

Economy

A model to gain understanding of the causes and effects of a population's interest in engineering.

Engineering Interest Economy

this is economy as it is in reality.

Economy Business

Modern Monetary theory (MMT) has shown how modern monetary systems actually work. It has shown  that governments that issue their own currency, such as the US, can never run out of money or be forced to default on debt issued in their own currency. It has also demonstrated that government spending to stimulate the economy is logical and that the resulting deficit is irrelevant - the government always has the monetary means to eliminate it. This directly contradicts neoliberal doctrine that wants to limit government spending and posits that deficits destabilize the economy. Neoliberalism often constitutes a 'worldview' and 'personal identity'. Those who hold such strong beliefs cannot be persuaded to abandon them using rational arguments and facts - psychological reasons usually impede it as research has shown. The worldwide dominance of the doctrine, vested interests and psychologically grounded opposition suffocate MMT and rational arguments showing its superiority are seemingly of no avail. 

Economy MMT Neoliberalism Finance

Scratch build of a stock-flow consistent model of a closed economy, based on a current transactions matrix

Economy Macroeconomics

Wealth can be seen as the factories, infrastructure, goods and services the population of a nation dispose of. According to Tim Garrett,  a scientist who looks at the economy from the perspective of physics, it is existing wealth that generates economic activity and growth. This growth demands the use of energy as no activity can take place without its use. He also points out that the use of this energy unavoidably  leads to concentrations of CO2 in the atmosphere.  All this, Tim Garrett says,  follows from the second law of thermodynamics.  If wealth decreases then so does economic activity and growth. The CLD tries to illustrate how wealth, ironically, now generates the conditions and feedback loops  that  may cause it to decline. The consequences are  inevitably economic  stagnation (or secular recession?). 

You can read about the connection Tim Garrett makes between 'Wealth, Economic Growth, Energy and CO2  Emissions' simply by Googling 'Tim Garrett and Economy'.

Economy Global Warming Resource Depletion

The effect of austerity in the wrong place

Budget Deficits Economy Austerity GDP

Environment Sustainability Society Economy

The upper diagram shows the principal factors that have an influence on the budget deficit and indicates what needs to be done to correct it. But this is not the full story. The diagram below shows that  cutting public expenditure reduces aggregate demand and  increases unemployment. The reduction of aggregate demand  reduces  economic activity which has the effect of reducing  tax revenue.  In addition, the state has to pay out funds as there is a need for more unemployment benefit payments.   The result of these austerity measures  is often the opposite of their intended purpose: they can increase rather than decrease the budget deficit.

There is plenty of empiric evidence to show that this has happened time and time again. For instance, a report from UNCTAD (United Nations Conference on Trade and Development) found that between 1990 and 2000 in all the  cases examined where cutbacks in public spending and tax increases were used, the fiscal situation did not only not improve but worsened. Despite such repeated evidence, unfortunately calls for  austerity measures continue to be heard. 

Economy Economics

Economy Finance

The significance of reduced energy return on energy invested (EROI) in the transition from fossil fuel to renewable primary energy sources is often disputed by both renewable energy proponents and mainstream economists.​ This model is a first attempt to illustrate the impact of EROI in large-scale energy transition using a system dynamics approach. The variables of primary interest here are: 1) net energy available to "the rest of the economy" as renewable penetration increases [Total final energy services out to the economy]; and 2) the size of the energy sector as a proportion of overall economic activity, treating energy use as a very rough proxy for size [Energy services ratio].
This model aggregates energy use in the form of fuels and electricity as a single variable, total final energy services, and treats the global economy as a single closed system.
The model includes all major incumbent energy sources, and assumes a transition to wind, PV, hydro and nuclear generated electricity, plus biomass electricity and fuels. Hydro, biomass and nuclear growth rates are built into the model from the outset, and wind and PV emplacement rates respond to the built-in retirement rates for fossil energy sources, by attempting to make up the difference between the historical maximum total energy services out to the global economy, and the current total energy services out. Intermittency of PV and wind are dealt with via Li-ion battery storage. Note, however, that seasonal variation of PV is not addressed i.e. PV is modeled using annual and global average parameters. For this to have anything close to real world validity, this would require that all PV capacity is located in highly favourable locations in terms of annual average insolation, and that energy is distributed from these regions to points of end use. The necessary distribution infrastructure is not included in the model at this stage.
It is possible to explore the effect of seasonal variation with PV assumed to be distributed more widely by de-rating capacity factor and increasing the autonomy period for storage.

Energy EROI Economy

Economy

A detailed description of all model input parameters is available here. These are discussed further here and here.

Update 26 October 2017 (v2.7): Updated historical wind and PV deployment data for 2015-2016, adding projected PV deployment for 2017. Data via https://en.wikipedia.org/wiki/Growth_of_photovoltaics and https://en.wikipedia.org/wiki/Wind_power_by_country.

Update 18 December 2016 (v2.7): Added feature to calculate a global EROI index for all energy sources plus intermittency buffering (currently batteries only, but this could be diversified). The index is calculated specifically in terms of energy services in the form of work and heat. That is, it takes the aggregated energy services made available by all sources as the energy output term, and the energy services required to provided the buffered output as the energy input term.

Update 29 June 2016 (v2.6): Added historical emplacement for wind and PV capacity. The maximum historical emplacement rates are then maintained from year 114/115 until the end of the model period. This acts as a base emplacement rate that is then augmented with the contribution made via the feedback control mechanism. Note that battery buffering commences only once the additional emplacement via the feedback controller kicks in. This means that there is a base capacity for both wind and PV for which no buffering is provided, slightly reducing the energy services required for wind and PV supplies, as well as associated costs. Contributions from biomass and nuclear have also been increased slightly, in line with the earlier intention that these should approximately double during the transition period. This leads to a modest reduction in the contributions required from wind and PV.

Added calculation of global mean conversion efficiency energy to services on primary energy basis. This involves making an adjustment to the gross energy outputs for all thermal electricity generation sources. The reason for this is that standard EROI analysis methodology involves inclusion of energy inputs on a primary energy equivalent basis. In order to convert correctly between energy inputs and energy service inputs, the reference conversion efficiency must therefore be defined on a primary energy basis. Previously, this conversion was made on the basis of the mean conversion efficiency from final energy to energy services.

Update 14 December 2015 (v2.5): correction to net output basis LCOE calculation, to include actual self power demand for wind, PV and batteries in place of "2015 reference" values.

Update 20 November 2015 (v2.4): levelised O&M costs now added for wind & PV, so that complete (less transmission-related investments) LCOE for wind and PV is calculated, for both gross and net output.

Update 18 November 2015 (v2.3: development of capital cost estimates for wind, PV and battery buffering, adding levelised capital cost per unit net output, for comparison with levelised capital cost per unit gross output. Levelised capital cost estimate has been substantially refined, bringing this into line with standard practice for capital recovery calculation. Discount rate is user adjustable.

Default maximum autonomy periods reduced to 48 hours for wind and 72 hours for PV.

Update 22 October 2015 (v2.2): added ramped introduction of wind and PV buffering capacity. Wind and PV buffering ramps from zero to the maximum autonomy period as wind and PV generated electricity increases as a proportion of overall electricity supply. The threshold proportion for maximum autonomy period is user adjustable. Ramping uses interpolation based on an elliptical curve between zero and the threshold proportion, to avoid discontinuities that produce poor response shape in key variables.

Update 23 September 2015 (v2.1): added capital investment calculation and associated LCOE contribution for wind generation plant, PV generation plant and storage batteries.

**This version (v2.0) includes refined energy conversion efficiency estimates, increasing the global mean efficiency, but also reducing the aggressiveness of the self-demand learning curves for all sources. The basis for the conversion efficiencies, including all assumptions relating to specific types of work & heat used by the economy, is provided in this Excel spreadsheet.

Conversion of self power demand to energy services demand for each source is carried out via a reference global mean conversion efficiency, set as a user input using the global mean conversion efficiency calculated in the model at the time of transition commencement (taken to be the time for which all EROI parameter values are defined. A learning curve is applied to this value to account for future improvement in self power demand to services conversion efficiency.**

The original "standard run" version of the model is available here.

Energy EROI Economy

Peak oil will occur when it is too expensive to bring oil to the surface and not when reserves reach their limit. Companies must make a profit to be able to extract oil and stay in the oil business.  However, that endeavour is becoming more and more difficult because of diminishing returns. They have to dig ever deeper to get to the oil  at ever increasing costs, and the oil they find deep down is of a lesser quality.  We have now reached a point where the price needed by oil companies to make a profit and stay in business is far higher than the price  the market can bear. That price is probably about $ 100 per barrel - and rising every year! A market price o $ 100 will almost certainly cause a sharp recession and cause the price of oil to fall back beyond the point of profitability. For example, the combined profit of ExxonMobile, Chevron and Conocophillips fell from 80.4 billion in 2011 to only 3.7 billon in 2016 - see URL below. What the market can bear depends on the spending power of the mass of non-elite workers. The CLD shows the negative feedback loops that prevent oil prices to rise above the level of  affordability. If non-elite workers cannot afford the goods and services offered,  then there will be no demand for them and by extension for oil.  In this situation the market price will not the cover the cost that oil companies need to extract oil. Oil supplies will decline and so will economic activity!

https://srsroccoreport.com/the-blood-bath-continues-in-the-u-s-major-oil-industry/

Peak Oil Oil Industry Economy Oil Prices