Regional und umweltfreundlich – Wie man in Sindelfingen CO2 arm leben kann

Seit etwas über einem Jahr lebe ich nun wieder in Sindelfingen und nach meiner Zeit in Indien und Macau, wo die Umweltverschmutzung katastrophale Zustände angenommen hat, möchte ich gerne aufzeigen wie man hier in Sindelfingen umweltfreundlich und doch gut leben kann. Dieser Blogpost ist auf Deutsch gehalten da es um ein Thema in der Region handelt.

Energieverbrauch zu Hause:

Die Stadtwerke Sindelfingen (als regionaler Anbieter) haben in Ihren Tarifmodellen den Ökostromtarif Primero Strom Öko100, der sich laut Selbstauskunft zu 100% aus CO2-Emmissionsfreien Energiearten zusammensetzt. Man kann sich natürlich bei Stromvergleichsportalen wie z.b. Check24 andere Anbieter ansehen, aber für diese Posr beschränke ich mich auf regionale Angebote.

Der Ökostrom der Stadtwerke kostet derzeit (12.11.2016) 21,60cent pro kWh. Der “normale” Strommix ist im billigsten Vergleich mit 21,06cent pro kWh angegeben. Somit liegt man pro kWh um 0,54cent teurer bei Ökostrom als mit dem normalen Mix. Wie viel macht dies pro Jahr aus? Ein Blick in den Stromspiegel 2016 hilft.

Zwischen durchschnittlich 3000 kWh (Einpersonenhaushalt) und 5200 kWh (5 Personenhaushalt). Dies bedeutet das der jährliche Mehraufwand für einen CO2-neutralen Energieverbrauch im eigenen Haus zwischen 16,20€ und 28,08€ liegt.

Somit kann ein durchschnittlicher Sindelfinger, der bei den Stadtwerken seinen Strom bezieht für nur 16€ bis 30€ mehr über 1 Tonne CO2 sparen:


Arbeitsweg / Verkehr in der Region Sindelfingen:

Man kann sagen was man möchte über die S-Bahn der Region Stuttgart, aber die Linie S60 hat wirklich einen Mehrwert für die Gemeinden Maichingen, Renningen und Magstadt gebracht. Vor Eröffnung der S-Bahn Linie war es eigentlich Usus dass man mit dem Auto zur Hulb oder zum Goldberg gefahren ist und erst dann auf die S1 nach Stuttgart Downtown umgestiegen ist. Jetzt mit der s60 steigt man in Böblingen um und hat (wenn es zu keinen Verspätungen kommt) auch einen Zeitgewinn gegenüber der Autofahrt durch Sindelfingen (zum Goldberg). Und nicht nur das, man spart auch bei jeder Fahrt CO2! Hier ein paar Rechenbeispiele:


Für mich bedeutet das also, dass ich bei jeder Fahrt Richtung Stuttgart im Vergleich zu früher, durch die S60 zwischen 560gramm und 849gramm CO2 spare. Bei gerechnet 350 Fahrten im Jahr (Arbeit, etc.) sind das schon 196kg CO2 die ich spare. Ich verbrauche zwar immer noch 93kg CO2 für die Strecke zum Goldberg aber da ich nicht das Auto nehme habe ich den Verbrauch auf dieser Strecke um über 50% reduziert!




Flickr – Machine Tags for Things – Greek Art

Statutes of the Cult Association of the Molpoi - Close up photo

Having written about adding machine tags to Flickr, I have recently visited Berlin and also explored the Museumsinsel with its museums Altes Museum, Neues Museum, Bode Museum and Pergamonmuseum. My interest in artefacts and archaeology has spurred me to photograph a number of exposes in these museums, see e.g. my Greek Art set.

Naturally I would like to include as much information as possible with the photo, also in the form of machine tags. Unfortunately I was not able to find a suitable ontolofy of things that would help me describe such basic characteristics such as :

  • Acquired in XXXX (Year)
  • Material (Clay, Marble, etc.)
  • Era of origin: (time period)
  • Place of origin: (City)

Hence I resorted to create a simple machine tag in the style of


A few examples are below:


If any of you know a suitable ontology that I can use instead of or in addition to the above, please let me know in the comments.

An overview to Flickr machine tags and a list of common machine tags

Flickr has enabled machine tagging quite some time ago and there has been a general push in the web towards semantic relations and data structures (e.g. see the archive posts on the Music China Heritage Project). However there seems to not have been a comprehensive “Quick to use” list of common machine tags that photographers and Flickrites can use. In fact while finding a number of high-ranked explanatory posts about the nature of machine tags, I have not found a brief overview of what I (as a typical photographer) can do to enhance my photos.

Updated on 2017-03-17 and 2017-11-06 with further machine tags as found useful as well as additional resources and projects in the web.

General tags applicable to most or all photos:

location:city=* (e.g. Stuttgart, Beijing, New York, etc.)
location:country=* (e.g. Germany, France, etc.)
location:state=* (e.g. Baden-Württemberg, Illinois, Hebei, etc.)
location:street=* (street name)

Every photo is being taken somewhere, and hence the content of the photo has been taken at the LOCATION as specified. Machine tags like these are nice add-ons, while Flickr itself takes the EXIF information to place the photo on the Flickr Map.

wikipedia:en=* (e.g. Paris, Banana, etc.)

This will create a link between the photo and the wikipedia page that is mentioned here. Be aware to NOT enter the whole wikipedia link, but only the title of the page that you are refering to. E.g. a photo representative of Paris should be linked to Paris. But if you have a photo that shows the Eifel Tower it might be better to link that to the Wikipedia article of the Eifel Tower and NOT Paris.

dc:identifier=* (e.g. URL to Wikipedia page or another page about the photo’s content main topic)

The DC Namespace explains the identifier as follows:

An unambiguous reference to the resource within a given context.

and I usually use this machine tag if there is a clear Wikipedia page with further information.

owl:sameas=* (e.g. “Sand Shark”)

I use the owl:sameas machine tag whenever a certain item or animal or person has more than one name and can be identified with another name as well. Animals often have more than one common name and with the owl:sameas tag I do mention each name individually.

Meta data about your camera (even though it is in the EXIF data, some processes do not read those data fields:

camera:maker=Nikon (or Canon, …)
camera:model=D90 (or D70, …)
In principle one could add all EXIF data such as aperture, shutter speed, ISO, focal length, etc. but as a start camera maker and model are sufficient.

For buildings / architecture photos:

architecture:building=* (e.g. Airport / cathedral / church / etc.)
architecture:completed=* (requires a year, e.g. 2015)
architecture:name=* (if a building has a certain name, such as “Schloß Hohenzollern)
architecture:architect=* (name of Architect)
architecture:completed=* (year in which the building as such was completed)

airport:iata=* (if the photo is taken inside an airport or depicts a part of an airport, then you can assign the IATA code here, e.g. FRA for Frankfurt International Airport).

For flower / plant / animal lovers:

taxonomy:kingdom=Animalia (for animals)
taxonomy:kingdom=Plantae (for plants)
taxonomy:binomial=”* *” (replace the stars with the genus and species)

Information for individual species can be found at Wikipedia. The Flickr group EOL has more information on how they use machine tags.

Furthermore, as pointed out by the BLL there is a great tool that auto-generates the tags for various animals: Taxonomic Tags for Flickr by iNaturalist

For food / dishes / restaurant shots

food:cuisine=* (Cantonese, Chinese, etc.)

For things in general (e.g. archeological items from museums)

See the separate post about things.

Here are a few examples of machine tags for things:

thing:material=Marble (e.g. Stone, Cotton, Cloth, Steel)
thing:acquiredin=1857 (Year only)
thing:placeoforigin=Tarquinia (Area of origin)
thing:category=* (e.g. Statue, Figurine, Tool, Weapon, etc.)
thing:length=* (e.g. “35m” or “50cm”)

If it is known from where an archeological artifact has been excavated one can add a reference to the Pleiades gazetter.

pleiades:place=* (with the identifier as explained here)

For machines and factory plants

Similar to “things” above (which is more general), there are a number of possible machine tags for “machines” / “engines” / etc.

machine:model=* (Model number / type name / etc.)

For Music / Band Promo Shots / Events such as concerts/festivals:

event:type=* (e.g. concert, festival, fair, musical, opera, etc.)
event:venue=* (requires a specific name, such as CGGB or House of Blues Chicago)

music:artist=* (Name of the artist. Be aware of the differences between ARTIST, MUSICIAN and ROLE)
music:band=* (Name of the band, such as Slipknot, Metallica, etc.)


Musicbrainz, one of the large music directories online, has assigned a specific ID for artists, CDs, tracks and labels. On their explanatory page they offer a ay to identify this ID on their website.

foursquare:venue=* (see here for explanation)

Colors / Concepts:

For photos that are more minimalistic in their approach one has the option to include descriptive tags such as:

abstract:shapes=* (e.g. dot / circle / grid / lines)
color:contains=* (e.g. blue / green / yellow / etc.)

Summary and Updates:

  • This should be a handy guide for Flickrites to tag one’s own photos with appropriate machine tags.
  • If you have further tags that are useful, please add them as comments and I will include them in the list.
  • Intitial version of this post as posted on 29.09.2015

Additional resources:



Macau in Figures – A year into the recession

2015_08_Monthly Gross Revenue

One could say that the above graph shows all there is to say about the current state of Gambling in Macau, yet we will come to that as we are deep diving into the current situation. First of all, the month of August has ended and figures for August will be released sometime later in September by DICJ. Until then we can work with what we see above. And what do we see?

  1. The peak gaming revenue was in February 2014
  2. The first sign of a low was in June 2014
  3. The downwards trend has continued (with smaller peaks in between) until July 2015
  4. Lowest point in the downward trend had been June 2014 so far with 17 billion MOP revenue, the same as experienced in November 2010 for the last time.
  5. July 2015, the last month on record logged 18.6 billion MOP, the same as experienced in January 2011 for the last time.
  6. Comparing the low in June 2015 with the peak in February 2014, Macau revenue has dropped by 55% in less than a year.

The following graphs capture  the same set of data in slightly different views:

2015_08 - Comparison of Monthly Growth Rates

The above graph compares total revenue figures (in million MOP) month to previous years’ month. Also here the sudden drop in June 2014 is visible and there had been no month that was able to peak higher than the corresponding season the year before. That includes every month in 2015. That gap was highest in February 2015 as visualized below:

2015_08 - Gap to last year

Whereas one might think that a stabilized graph, as seen between March to July 2015 is a good thing, in reality it is a sign of negative growth as it represents the gap towards the last years revenue figure and if continuing over a year resembles a downward trend.

2015_08 - Monthly Gross Revenue compared to last years month

The above graph, with data from January 2010 all the way to July 2015 shows the growth rate of revenues compared on a month-to-last-years month basis in percentage. Once again, the downward trend is more than visible.

2015_08 - Growth Month to Month and Average

This graph is focussed on comparing growth rates between consecutive months, i.e. January 2015 to December 2014 and gives an indication on the general trend. The more peaks are below the 0% line, the more of a downward trend it is, whereas when there are more peaks above the 0% line, revenues are growing. The red line resembles the average for each half year of each month’s growth/decline percentage. Once again, the downward trend starting in 2014 is visible.

Whereas the above are great representations of probably the most talked about issue in Macau, they are all based upon one single set of data:

  • Revenue of Games of Fortune in Macau

The above alone cannot be representable for the state of Macau is the following is not taken into account:

  • Visitor figures
  • Openings of hotels and casinos
  • Unemployment figures
  • External factors

Let’s have a look at the visitor figures as published for Macau. DSEC has released figures up to Q2 2015 (and the month of July, which is being excluded in our comparison below).

2015_08 - Visitor Figures

Immediatelly visible is a drop in visitors from mainland China. This is also visible when one makes the overall graph for comprehensible:

2015_08 - China vs Rest

Before going into further discussions on any of the implications, let’s review this a little bit further.

  • Peak visitors overall to Macau had been in Q3 2014 (surprisingly in the quarter in which the drop of revenues occurred). The same quarter had also seen the highest amount of visitors from mainland China.
  • Q2 of 2015 is the lowest quarter in the recent downward trend and is the lowest quarter since Q2 of 2013 (i.e. two years)
  • The peak in visitors had actually not just been one quarter, but two quarters, with Q4 of 2014 having had the second highest number of mainland Chinese tourists (and the second highest overall visitor figure as well)
  • Rest of World is pretty stable and Hongkong has not escaped it longterm downward trend.

Let’s have a look at the gambling revenue broken down into visitors (per-head-analysis):

2015 - Revenue Down

Since Q2 2014, revenue per head (i.e. per visitor) is down compared to the previous quarters. No remember that Q3 and Q4 of 2014 had been extremely strong visitor months hereas revenue as sinking, so let’s have a look at an overlap of the figures:

2015 - Visitors and Revenue

What can be seen above is a very dangerous trend: Not only are the overall visitor figures decreasing, but at the same time, the spending per head is decreasing as well. The current spending rate of about 7740 MOP per visitor in gambling revenues is the lowest figure since Q3 2010.

This leads us to our next verification: which major casinos or hotels opened during the recession? Often it is talked about that Macau is adding it’s Cotai 2.0 projects to the market. Galaxy Macau Phase 2 was the first of several large resorts to open. It opened on May 27th 2015. So let’s have a closer look at the months that followed the opening. In particular we are looking at visitor figures to see if an additional casino would attract more visitors:

2015 - Visitors after Galaxy Opened

It is important to note that we want to compare Total Visitor figures on a monthly basis agains the same period the year before and the year before that. We do NOT want to compare consecutive months as that might be a one-time trend, whereas comparing a similar time in the year the year before will give one a clue if there had been an impact or not.

As can be seen above, June 2014 and July 2014 had seen much higher visitor figures than 2015 or 2013. That is in line with previous indications about a peak in 2014. As such whatever stimulus the opening of Galaxy Macau Phase 2 had, it was not enough to get visitor figures back up to 2014 levels. They are however higher than 2013.

Above we have looked at revenue figures and total visitors. We have not yet looked at individual casino’s performances (which would require a little bit of deep research for which I do not have time at hand right now). We can however have a look at the unemployment rates of Macau:

2015_Unemployment Rate

Over the years unemployment dropped significantly and even throughout most of the recession, unemployment was at a record low of 1.7%. Only in the second quarter of 2015 unemployment slightly increased to 1,8%. As such it can be concluded that the recession had no impacts on the employment situation of the Macau workforce.

What are the external factors that caused the above?