IS GOLD A SAFE INVESTMENT
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.
Investment vs Austerity v2
The Tyranny of 'Efficiencyism'
Microeconomic Savings can convert to Macroeconomic Costs
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!
ROBOTS AND A DISATROUS ECONOMIC DYNAMIC
Energy transition to lower EROI sources (v2.8)
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.
The Dynamic that shows that Government Deficits benefit the Private Sector
This video by professor Stephanie Kelton contains evidence that supports the modle.
Solution of Recycling Problem in Vancouver
Sustainability in Fisheries Finale
Factors affecting Brazilian soy export growth
Simulating an Economy v1.0
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.
Public interest in engineering
Irrational rejection of ''Modern Monetary Theory''.
Model of Covid-19 Outbreak in Burnie, Tasmania (Yue Xiang 512994)
Government policy is activated when there are 10 or fewer reported cases of COVID-19. The more people tested, the fewer people became infected. So the government's policy is to reduce infections by increasing the number of people tested and starting early. At the same time, it has slowed the economic growth (which, according to the model, will stop for next 52 weeks).
ECONOMIC GROWTH WILL MAKE EVERYTHING WORSE
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'.
The effects of Austerity
Recycling and Waste Treatment in Vancouver
Fallacy of Spending Cuts
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.
Swedish monetary policy
Clone of Energy transition to lower EROI sources
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.
Model of COVID-19 outbreak in Burnie(Yafei Shi 489576)
This is a system dynamics model of COVID-19 outbreak in Burnie which shows the process of infections and how government responses, impact on the local economy.
First part is outbreak model, we can know that when people is infected, there are two situations. One is that he recovers from treatment, but even if he recovered, the immunity loss rate increase, makes him to become infected again. The other situation is death. In this outbreak, the government's health policies (ban on non-essential trips, closure of non-essential retailers, limits on public gatherings and quarantine ) help to reduce the spread of the COVID-19 new cases. Moreover, government legislation is dependent on number of COVID-19 cases and testing rates.
Second part: the model of Govt legislation and economic impact. Gov policy can help to reduce infection rate and local economy at same way. The increase of number of COVID-19 cases has a negative impact on local Tourism industry and economic growth rate. On the other hand, Govt legislation also can be change when reported COVID-19 case are less or equal to 10.