This isn't to say this doesn't happen in western countries but it is less common. A large number of deaths was also associated with the Qatar world cup for example.
Look at the public reports of engineering and manufacturing companies. How many of them have a "target zero" approach to safety and report the TIFR as a key KPI? I've known executives in engineering organisations to be fired for persistent safety breaches making them substantially more risk averse.
This all costs more money and takes more time and I would posit that if China/India/Africa become wealthier and more individualistic then their construction rates would also slow.
A primary driver of the 'slowness' is a major focus upon safety in the workplace. Take a look back at the death rates per 1000 workers for some of the historical projects that people often like to point to and then look at a modern project.
The second element is that these safety elements are often enforced by regulation. For example, look at how much extra scaffolding is in use today during construction (which used to be ladders).
To directly look at corruption, this is an issue but not in a direct way. Corruption happens through the 'contractor ladder' where the primary contractor has a subcontractor who has a subcontractor who has a subcontractor. Repeat ad infinitum.
One of the primary reasons for this is the challenges in maintaining a large enough workflow to keep a standing workforce employed. It's difficult to justify paying expensive construction workers and engineers when they're not actually building anything.
Finally, tendering protocols are often quite naive and have been implemented to make something "least cost". This has led to a nightmare scenario of companies underquoting in order to win a tender, secure in the knowledge that a government will not leave a project half finished. To remedy this better contracts are required, for example, you can offer a recurring revenue stream (e.g. 20-30 years) to a company in return for a particular project. On the other hand, this can often lead to poorly build projects that last exactly 30 years.
If you haven't read it pick up a copy of Silent Spring by Rachel Carson which covered the indiscriminate use of pesticides and the effect on the wider ecosystem.
The awareness she helped raise contributed to the founding of the EPA and the banning of DDT.
There is a huge network cost towards doing this. The centralised platforms by definition will push us closer towards the mean (as the mean is most profitable) at the expense of the outlier.
This means that you'll have a higher quality median (note, not mean) experience at the cost of experiencing any true outliers. The best way it's been put to me is the follows:
* (AI/ML/Algorithmic Recommendation) = 8/10 products you will like, none you'll love or hate
* (Serendipitous Searching) = 5 products you will guaranteed like, 1 you will hate, 1 you will absolutely love
By only having access to the current rotation on Netflix/Amazon/HBO you cannot find the 'diamonds in the rough' that suit your taste.
I am in complete agreement with you, I rent for very similar reasons.
What I was attempting to flag was that for many this was no longer about it being a choice. The dramatic rise in house prices has meant that saving a deposit is now a lengthy endeavour in many areas, one that is driving a much larger renter culture. It is to be expected that lifestyle norms will change around this as well.
What isn't mentioned in this article is any commentary regarding the falling rates of home ownership in various countries compounded with rapid price increases (driven by low rates and zoning policies). This is particularly acute as it appears to be driven predominately by a generational gap as opposed to a socio-economic gap. Pitting the young against the old is never a good thing.
What is also missed completely is the rise of the professional share house. How many professional people on high incomes are now forced into multi-person rental accommodation due to affordability concerns.
Not quite, the instantaneous energy output from the system will be at 200GW when the sunshine is at a maximum. The average output of the system would be 60GW (at a 30% capacity factor). In terms of total energy this would equate to around 525TWh worth (60 * 8760 / 1000). Or, 525,000,000 MWh.
That is, for every $1/MWh that the solar station gets paid for it's output it will receive half a billion dollars a year.
Now, a station of this size would massively depress power prices throughout the region unless there was some form of large scale storage unit which could soak it up.
Probably more impactful is the Saudis are currently using oil for their power stations which is heavily subsidized.
Yes, it's a shame for the founders in this situation, particularly if the wished to continue whilst their investors wished to exit and recoup any losses they may have had.
I'm reminded every day that the adoption curve can be brutal for those at the sharp end of the stick.
We currently use a combination of Pandas and Scikit-Learn to run our production models. We're not in the big data space, instead, creating small tightly tuned models for a very specific purpose in a large energy company.
At the moment the general work flow is:
* Internal library based over Pandas which abstracts our mess of internal databases
* Application specific model code that utilises the internal library to pull data in. This is then fed into a trained scikit-learn model and then further processed by Pandas.
* Internal monitoring tools (dashboards based upon Ploty and Flask as well as an alerting system) are built using the internal library and Pandas as the glue.
From a design decision we focused upon Pandas as the root source of all data. Everything is a DataFrame throughout the entire application.
Painpoints:
* Writing to a database is pretty painful (SQL Server here as Windows shop).
* Minor API changes can be irritating.
* Pandas MultiIndexing is both very painful and mind bending at the same time trying to get the slice syntax to work.
Overall though, Pandas is a huge value add and we've gradually rolled out from 2 people to approximately 9-10 people who hadn't used python in anger before.
Almost all reporting functionality is being migrated into Pandas instead of SQL stored procs, excel, tableau etc for the additional flexibility it provides.
How much is unreported?
This isn't to say this doesn't happen in western countries but it is less common. A large number of deaths was also associated with the Qatar world cup for example.
Look at the public reports of engineering and manufacturing companies. How many of them have a "target zero" approach to safety and report the TIFR as a key KPI? I've known executives in engineering organisations to be fired for persistent safety breaches making them substantially more risk averse.
This all costs more money and takes more time and I would posit that if China/India/Africa become wealthier and more individualistic then their construction rates would also slow.