Making Boulder Colorado a better place for biking and walking – changes to Right Of Way

Boulder’s Planning Board will meet this evening (Thursday, August 7th) to discuss a key change in how Right Of Way will be used in new projects, easing how bike and pedestrian projects can change the ROW. Please consider writing the Planning Board to advocate for these changes. Here is a sample letter:

There has been a lot of press and discussion about the ROW (right of way) issue coming before Planning Board tonight (see Daily Camera link below).

One important fact that has been left out of this discussion is that approval of this proposal would eliminate the development disincentive inherent in ROW dedications, and as a result would speed up the process of building out our bike/ped network, which is particularly important in breaking up the super-blocks that dominate the east side of the city. The current ordinances make it much less likely that a developer will build small, human-scaled streets, bike paths, and sidewalks as part of new development. In fact, if a developer does choose to build small streets, bike paths and/or side walks, he/she is penalized for doing so. As a result, this creates super-block development with little to no bicycle or pedestrian access and a very unfriendly pedestrian, bicycle and human environment. By passing this ordinance change, Boulder will make it economically feasible for new development to be built on a human scale, with small, traffic calmed streets and interconnected bike paths and sidewalks. Without this ordinance change, Boulder is encouraging large, auto-oriented super blocks to be built.If we want a city that has walkable, bike-able, connected 15 minute neighborhoods, with local retail, where a car is not a requirement to get to school, work or to do errands, this ordinance change must be approved.

This voice has not been heard in the current discussion. If we loose these bike/ped connections, they will be lost for many years.

We encourage you to write Planning Board (and cc City Council) with the following- you are encouraged to modify into your own words, but if you can’t – just send this. If you can come to Planning Board tonight in person (around 7:30 pm) that would be the best!

Dear Planning Board:

I support the ROW changes before the board tonight. These changes will allow for more bicycle and pedestrian connections and facilities and will help break up   existing super blocks and prevent future super blocks that are unfriendly to people on bikes and walking. Please support the staff recommendations for ROW changes that will help create a more walkable, bike-able Boulder.

Personalize this letter and send to both:
boulderplanningboard@bouldercolorado.gov
Council@bouldercolorado.gov

A link to more information in the Daily Camera about the proposed changes to building density:
http://www.dailycamera.com/news/boulder/ci_26290515/boulder-weighs-density-change
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Facebook is the suburbs while Twitter is the city

Facebook headquarters

Social networks enable varied forms of interaction within their users, through spectrums of openness, hierarchy, and discovery. Facebook and Twitter are the most used services to connect people socially, but bring people together in surprisingly different ways. Facebook’s strengths rely on easily connecting with established networks, showing highlights, and sharing meta-actions (like posting pictures or events). Twitter’s advantages lie in instant communication, building ad-hoc networks, and providing public and widely accessible information.

Why compare social networks to geographical networks? I’ll argue that the same openness, hierarchy, and discovery also applies to suburbs and the city, greatly affecting our modes of relationship. Suburbs span a large area, creating both silos of community and a greater privacy. Suburbs aren’t great for meeting people, but they do provide a framework for connecting disjointed entities to a center.

Twitter headquarters

In comparison, cities move quickly and connect people through greater density. People gather at the local events or meetups, making new friendships from shared interests. Rather than surrounding a center, the city is the center.

The Chasm of Public versus Private

Probably the most often used word in a sentence concerning “Facebook” is privacy, a concept almost never discussed about Twitter. Many Facebook posts are marked ‘for friends only’, and some are concerned over the privacy issues with ‘mutual friends’. Conversations happen in response to a post and comments—very much in a stimulus/response methodology. While commenters sometimes talk among themselves in a post, a trend emerges: the commenters are either friends already, or there will likely be no lasting relationship between them. Compared to Twitter, responses to an original tweet may invoke conversation between responders (sometimes facilitated by the original tweeter), and at its conclusion, responders may choose to follow one another, building a network link between them where one previously didn’t exist and being a part of future cross-talk. Choosing to follow someone on Twitter denotes a more casual relationship than befriending someone on Facebook.

Facebook interactions tend to center around the originator, making only weak links between the other parties.

These human interactions mirror those of a suburb versus a city. Imagine the conversation on the Facebook post as a house party in the ‘burbs. The friend of a friend that you met has context per your conversation at the party, but you may have little connection afterward. And without much way to build a relationship between the two, you may never cross paths again perhaps unless you happen to be at your friend’s house. But Twitter is like meeting that friend of a friend at your local coffee shop that you three frequent. You can establish a few things in common, and if you go there in any frequency, you’ll meet up again and potentially create that new relationship. Not only that, but because your conversations are public, like a group of 10-15 people meeting at that coffeeshop, new organic connections will be made between individuals with shared interests much more easily than in the private confines of a home.

Screen Shot 2013-06-03 at 9.36.26 PM

Twitter interactions have fewer barriers to growing beyond the initiator.

Crosstalk is a feature of Twitter where, if two people you follow converse with messages to each other, these notes will show up in your timeline. Facebook has a similar feature—if one posts to a mutual friend’s wall, you’ll often see this in your Facebook timeline. But on Twitter, conversations are natural—encouraging others to pipe in—as they are clearly public, after all. Twitter serves as an equivalent of a public forum, rather than overhearing someone’s conversation, as there is no expectation of privacy.

The Destination versus The Journey

Suburbs classically keep their gems along huge arterial roads, surrounded by seas of parking lots. Drivers often complain about traffic while traveling on unsafe roads, having to walk across a large expanses of blacktop, traveling five or ten miles from their homes to a destination. City-living people often walk, bike or bus a few minutes from their homes, to a small and livable place, built at human scale. The experience that can be enjoyable—or terrifying—like our interface through Facebook or Twitter.

Facebook posts are often post-facto—reflections, pictures, collection of thoughts—in which the things you see are selected by an algorithm, based on likes, comments, or how much a company has paid for you to see it. It’s prepared. Sometimes stale. May be non-chronological. Packaged for sitting on shelves in a big box store, staying ‘fresh’ for months. Twitter, on the other hand, has fresh vegetables from the farmer’s market downtown. The fish from the sea this morning (sometimes with a less-than-pleasant smell). Rather than buying a five pound box, you get just what you need for breakfast this morning.

Tweets are often not fully-processed, mass-produced packages. They have defects, or they don’t sufficiently capture an idea. But sometimes they capture someone’s first thought in the morning, or an observation walking down the street not significant enough to warrant a post to Facebook. And sometimes that thought comes in a series of three, four, or five tweets—the timing, organic nature matters, and gives a unique flavor that big-box chain restaurants will consistently fail to deliver.

Choosing your friends versus Befriending your neighbors

When someone retweets someone else’s tweet into your timeline, it’s introducing something new into your system. The tweet is reproduced whole, with no comment, and including the original poster’s avatar. At first, retweets can feel almost like a breach of friendship—similar to a friend letting an unknown stranger into your home. It’s not uncommon for new Twitter users to ask how to turn off retweets in their timeline.

This similar occurrence doesn’t wholly exist on Facebook, but people can share an item (usually with comment). Instead, Facebook’s filtering isolates you from unwanted intrusions. Your Facebook timeline’s stories are likely influenced from a friend’s likes or comments, or Facebook’s algorithms show you things it determines you may want to see. Getting that semi-random retweet from a person that’s not expected, or sometimes not particularly wanted, doesn’t exist. You can move further out of town to stop this (i.e. disable retweets from the retweeter) . You can put up a fence (blocking the retweeted person). But you live in a dense neighborhood which by default accepts diversity over homogeny.

Mapping existing networks versus Building new networks

Friends don’t let friends move to the suburbs. Why? Because often it’s a precursor to devolving friendships rather than building them. Partly the lack of density strains friendships, requiring travel to visit old friends (suburban or urban). People keep their friendships when moving out of the city, but the friendship structure is now virtually out of balance with reality. The same is often true with Facebook.

Facebook communities and friendships are descriptive—they exist to mirror an external friendship/relationship (school, family, acquaintances . Twitter communities and friendships are conscriptive—they’re often building a relationship that wasn’t there prior to Twitter (location based, interests, learning). The problem with Facebook networks is that they quickly get out of date, stale, and siloed.

Living versus Deteriorating

Twitter is the ever-changing city. There are always new people to meet (follow) and old friendships may lapse when it’s time (unfollowing). Time is a constraint. There is a limit to how many people, things, and ideas a single person can care about. Twitter rarely has the nostalgia factor that keeps so many dead Facebook friendships alive. Yet Facebook allows relations to persist like an old, dead mall that’s long past its relevance.

@ericmbudd on twitter

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bikeshare: analyzing incentives to increase annual members

With any system, every potential member has a price he or she is willing to pay for the service provided. When deciding whether to purchase an annual bikeshare membership, customers often have only one option: the list price, unless a special discount applies. In essence, system operators try to balance two variables: appropriate revenue for the system to cover its costs, while serving the general public with an affordable transit option.

When setting price, system operators must have an understanding of what potential customers will pay for their service. Those who value the service highly would often pay more than the list price, while those with better alternatives would pay significantly less for the same service. Because we have limited data on customer preferences (without issuing a survey or having other transit data), we can start by analyzing the general needs of particular transit groups, and estimate an average cost change over the current mode of transit. The end goal will be to understand how variable pricing can increase the number of annual bikeshare members.

Each mode will be ranked individually (grouped by transit mode) with these parameters:

1. Willingness to pay (demand) – low, medium, high
2. Cost change (-/+) over current mode

The chart below is the end result, after the analysis has been complete. Later in the article, I’ll discuss each transit mode to justify the rankings.

With this ranking, we can now try to plot this to desired price-points along this spectrum. Again, we don’t have hard data yet, so let’s simply try to anchor the upper and lower bounds.

On the low end, imagine a single parent with several young kids whom all need transportation to school; she may like the idea of bike sharing, but her needs are simply not fulfilled by the service – we’ll set her willingness to pay at $20/year. On the high end, imagine a young male who lives downtown and his primary mode of transit is walking/busing and doesn’t want to own a car or his own bicycle – we’ll set his willingness at $130/year.

The rest of the points in the range will now be interpolated through our rankings:

Another reference point we add in the average full price, based on prices from Boulder – $55, Denver – $80, and DC – $75.

Now that our framework is built, I’ll provide further justification for these rankings. Moving from the least ideal demographics to the most ideal, we’ll discuss each transit group’s incentives to use bikeshare and their potential costs.

Least-ideal demographics

family vehicle, L++

Families usually need flexibility, including a method to transport younger children – an automobile is the most common choice. If these families use bicycles, they will often purchase bikes that are capable of transporting children instead of using bikeshare, which can only transport one person each. Bikeshare will be an added cost over a car and/or other commuter bicycles, and may not meet their needs.

bike commuters (non-bikeshare), L+

This group is paradoxical: while bike enthusiasts are often the most vocal and supportive of bike sharing, they have a low potential to pay for or use the system since many already commute by bike. Only the most earnest supporters will buy an annual membership – more as a gesture of community support rather than for direct personal benefit. One positive benefit bike-sharing provides is against theft. With traditional bike theft is so hard to stop, bike sharing is a win for both the user and the system. Anti-theft is a good selling-point, but often not a sole reason bike-commuters would opt for bike share. Bikeshare will be an added cost over their commuter bike, with limited upside.

carpool/vanpool riders, M+

Carpool and vanpool riders often use this mode of transit to save money – particularly on the capital cost of owning an additional vehicle. This group may positively gain daytime flexibility of bike sharing when individuals might not have access to a vehicle or as an alternative to using a bus/metro. However, carpool/vanpool riders are more commonly a small demographic, and often commuting from suburban areas, which would limit their use to mainly work/weekday hours. This group of people may be willing to pay a reduced price for bike-sharing, but likely won’t have enough benefit to justify a full price.

Key demographics

single-occupancy drivers, M-

Single-occupancy drivers are numerous and potentially the most desirable customers: they use significant road and parking resources, are less fuel-efficient, and spend a great deal of money to own and operate their vehicles. However, the barriers to entry are harsh; often these drivers are often not comfortable with biking/alternate transportation, and typically have the means to pay for the convenience of car use, and thus choose not to give up the flexibility of the car.

Reducing single car use is an important goal for environmentally sustainable communities, but one that’s difficult without significant assistance from local and regional governments and shifting culture. Other strategies may involve promoting bus/carshare along with bikeshare to fully replace the convenience of owning a car. Many single-occupancy drivers have already paid the fixed cost of car, and may be reticent to invest in other options while the vehicle still has many years of life.

walking, H++

Walkers (and those particularly without bikes) are ideal candidates for bike sharing. According to research from Walkscore, the ideal range for walkers tends to be one mile or less. Using bike sharing, a walker could easily extend to longer trips. This Capital Bikeshare data analyzed by JD Antos shows that 83% of trips are under three miles, and up to seven miles – an order of magnitude increase over the typical walking range (and likely faster). Another bonus: walkers will have fewer barriers in walking to a bikeshare station.

However, walking is a thrifty activity. While some may choose to walk for enjoyment or relaxation, others may necessarily walk due to the expense of other forms of transportation. Fortunately, bike sharing is an inexpensive option for consistent commuting and likely worth the price/time savings. Bike sharing can serve as important service for those who cannot afford other options.

bus/metro regionally, H+

Regional busing provides important longer distance travel, but often has limited transit options outward from the designated bus stops. Similar to car/van sharing, regional buses often travel a significant distance, but often leave the rider still some distance from where he or she needs to go. Bike sharing can help greatly to close this last mile gap in reaching a destination.

One newer innovation in bike sharing from Bcycle allows for free sharing of annual passes between two regions – Boulder and Denver, Colorado, for example. This is an exciting development as it can close the last mile gap on both ends, while also providing greater value for using the service in either area. Connecting bike sharing programs is a huge win for residents of these cities, and adds a tremendous value to the region. Customers who can take advantage of multiple regions will also be willing to pay more for this service, raising the average revenue.

bus/metro locally, H=

Those who often ride bus/mass transit should be prime targets for bike sharing as a more flexible and functional option. Bikeshare’s main advantage is its asynchronous operation, allowing a user to check out a bike at nearly any time, while buses have specific time schedules or limited service hours. In the case of light rail/subway, the same rules apply – and these services may be a relatively expensive option for some riders, while a bikeshare membership will be only a fixed cost for the customer. Bikeshare may also provide quicker and easier access to areas that a person may need to go. And lastly, bus/metro users want to take one leg of a trip using bikeshare, and return using the bus/metro.

When adding up all the benefits of pairing bikeshare with multiple alternative transit options, the sum is much greater than its parts, making a bike sharing annual membership a welcome addition.

Bridging the price gap

My hope from this article is to provide a method of viewing potential customers in a more descriptive and useful way, using some of the incentives we know about each population. More data will be needed to make judgments on each group’s willingness to buy an annual pass from changes in price or offering targeted discounts.

When we combine this with the concepts from my previous article on optimizing usage,  the next logical step will be determining what amount of discounts may be offered, how much the average rider will use the system, and how discounts can be justified relative to the fixed and variable costs of running a bike sharing program.

@ericmbudd

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bike sharing: optimizing system usage/revenue while keeping a great user experience

Great user experience for a bike sharing system has many components, including station location, ease of use, bike quality and availability. Specifically to bike operation and availability, two real factors apply:

  1. the start station with adequate bikes (influenced by a. total bikes in use, and b. bike distribution)
  2. the end station has adequate docks (influenced by a. total bike-to-dock ratio, and b. bike distribution)

The focus of today’s analysis will be on how bike sharing user experience changes with increases in total bikes in use (up to peak usage), while also considering the bike distribution among stations (as some stations tend to have a net-positive or net-negative flow which requires re-balancing).

Using data from Capital Bikeshare in Washington, DC (a large, urban bike sharing system with over 1500 bikes and 165 stations), we can begin to identify a case for maximum bike usage - both under an equal distribution of bikes, as well as under a more likely distribution of bikes (considering the balance under a natural station flow). The dataset used will be used from 1222 bikes (2443 docks) with 147 stations in the final two weeks of Q1 2012. The average number of bikes is determined by halving the number of total docks available, as we know that Capital Bikeshare uses a 50% bike-to-dock ratio. Based on this information, we can generate a model that simulates how many bikes the average station contains when x bikes are in use (i.e. not at any station).

Nominal operation

Here’s how the graph looks when no bikes are in use – the minimum number of docks at a station is 10, while the maximum is 39 (and will first model them at half capacity):

Next, we’ll use this as a baseline and steadily increase the number of bikes in use, decreasing bikes from stations at an equal rate. By maintaining our first goal of user experience, that the start station has adequate bikes, we must not let the peak rise beyond a level where the smallest stations no longer have bikes. For Capital Bikeshare, the smallest station is 10 docks; as it is an outlier, we’ll optimize for the many stations that have 11 docks:

Once we reach 662 bikes in use, we now see that stations with 10 or 11 bike capacity have less than 1 bike per station, on average – a total of 44 stations without bikes. Although the system still has 560 bikes available, there’s now a possibility that a new user will come to a station with no bikes available.

Station outflow/inflow

The above “average” case needs to also be viewed as the best-case scenario: bikes are evenly distributed, and new users check out bikes at an equal rate among all stations. However, bike sharing system rebalancers know better – some stations tend to always have a surplus, and some that tend to have few bikes, if any. We can model this by showing the total in/out data from the Capital Bikeshare system for the same period (knowing that any rebalancing does not reflect in this data):

As you can see, the graph of station flows are roughly 30% on each end that require rebalancing (+/-), and about 40% that mostly rebalance themselves. Interestingly, some stations have opposite trends on weekdays vs. weekends.

Typical operation

The modeling difficulty now: how can we apply this flow data to our static model of averages? My method is to take these measured flows (two week period) and divide them into a 12-hour average (which could be tweaked for more/less effect), which I then add to my baseline. Here’s the new graph, sorted by bikes available under typical use, starting at zero bikes:

Even with no bikes in use, some stations are empty or near empty. Using this new baseline, now we’ll assume the same previous number of bikes in use (our potential optimal maximum), 661:

With 661 bikes in use, we now show 40 stations without bikes – as compared to the nominal model, which only found one station without bikes. Some stations are deeply negative, which suggests even more demand for bikes (i.e. unhappy users who can’t check out a bike at the station they want).

A system operator must understand the potential trade-offs between increasing levels of ridership and the cost/ability to rebalance. This last graph helps answer that question, with its output being the number of stations that have less than one bike as the concurrent riders increase:

Using this data, a system operator can then make educated decisions for a target peak ridership (in order to maximize revenue) and what level of service customers should expect at peak system use. The cost of having peak bike usage less-than-optimal will be to forgo revenue or reduce community benefit; the cost of higher-than-optimal usage being increased or unmanageable rebalancing, or degraded user experience for the typical customer.

@ericmbudd

 

Read the first article in this series: bike sharing usage patterns in Washington DC’s Capital Bikeshare

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bike sharing usage patterns in Washington DC’s Capital Bikeshare

Using data from Capital Bikeshare in Washington, DC (a large, urban bike sharing system with over 1500 bikes and 165 stations), we can analyze system usage and time distribution patterns for:

  1. All users of the system for each 24 hours of the day.
  2. By weekday, a distribution of all, casual, and annual users.

The first look – all days, all users [and subdivided between annual/casual users] uses data from 2012 – January first through March 31st:

To begin, note the peak average utilization occurs at 5PM (165 bikes – 13.6%) while minimum utilization at 5AM (5 bikes – 0.4%).

However, we can find more interesting data points when separating between registered users (an annual and monthly pass) and casual users (three-day and daily pass).

Peaks for registered users occur at 8AM and 5/6PM, coinciding with typical working hours, with sharp changes during these periods.

Casual users, more likely visitors to the city and less likely workers or students, begin a gradual rise in usage about two hours later than the registered users, showing a steady increase in the number of bikes in use until 4PM, when the number begins a significant but more gradual decline.

The next three graphs use this data and subdivide it by days of the week, first showing all users, second just registered users, and third showing just casual users:

All users

Capital Bikeshare - all users - average number of bikes per hour per day 2012 Q1This graph features the same data as the blue line from the first graph above, divided by weekday. A few notes:

  1. The heaviest average day (most total bike-hours) occurs on Saturday; the lightest on Wednesday.
  2. The afternoon peak users shifts left (earlier) gradually, starting on Monday (latest) to Sunday (earliest)
  3. Late night use (midnight to 4 AM) is significantly higher on Saturday/Sunday mornings, though still a low number of total bikes used.

Registered Users

Capital Bikeshare registered users - average number of bikes per hour per day - 2012 Q1

This graph features the same data as the red line from the first graph above, divided by weekday. A few notes:

  1. With the casual users removed from the graph, we can see a more significant/deliberate usage during the lunch hours (noon to 1PM)
  2. Weekdays have a distinct pattern (largely centered around the work week) while on weekends, registered users behavior in a manner very similarly to casual users.

Casual users

Capital Bikeshare - casual users - average number of bikes per hour per day - 2012 Q1

This graph features the same data as the green line from the first graph above, divided by weekday. A few notes:

  1. Bike sharing system usage is similarly distributed throughout the day for each day of the week.
  2. As noted above, he heaviest average day (most total bike-hours) occurs on Saturday; the lightest on Wednesday – but the difference is even larger here.
  3. Peak system usage is highest at approximately 3:30PM for casual users.

Some caveats to this data: 1. Due to the time frame involved (winter), it’s possible some data could be skewed as compared to summer months, due to changes in temperature and sunlight (increasing day use, and decreasing night use). 2. The system itself and its usage is in its infancy, and we could see a higher usage during less standard times as the system continues to mature and users check out the bikes for more non-standard trips.

Given the data’s somewhat logical divide between visitors to the city and residents, perhaps it’s most helpful to see what further questions we can ask:

  1. Can we show how changes in pricing for either casual or registered users would affect total system utilization?
  2. What percentage of registered users are using the system, on average?
  3. How can we estimate the total available market for casual users? Registered users?
  4. How could we increase revenue by getting new riders to ride during more off-peak times?
  5. How can we protect the user experience by ensuring bikes/stations are available, if we increase average system use?

I look forward to exploring these (and many more) topics relevant to bike sharing systems.

@ericmbudd

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Three less-subjective reasons to adopt a vegetarian lifestyle

Vegetarian diets are not new, but today’s world offers many compelling reasons to reduce or remove meat products from our diet.

Human health benefits

People most often cite personal health as a reason for becoming vegetarian, which can be broken down into several parts:

Reducing salt and saturated fat – from a macro/micro-nutrient perspective, reducing sodium intake [which is often high in processed meats] and promoting a favorable lipid balance [shifting toward mono-unsaturated, vegetable-based fats] will reduce the risk of high blood pressure and heart attack/stroke, respectively.

Reducing biological magnification – eating food sources lower on the food-chain will reduce an organism’s exposure persistent toxins in the environment such as heavy metals or pesticides that are damaging or cancer-causing to the human body.

Reducing caloric density – as meat is primarily composed of fat and protein, and does not contain a high volume of water nor fiber, eating a diet high in meat content will likely contain a greater number of calories than a plant-based diet, increasing the likelihood of overeating.

Improving energy conservation and human/animal health

Although the ethical issues in eating animals are separate from the intent of this post, the impact of industrialized livestock farming and their effects on animal health [and consequently human health] should not be ignored.

Energy consumption and efficiency – the main drivers of capital food cost are land area, fresh water, and fuel usage, all of which increase which each increase in the percentage of meat in the average person’s diet. Overall energy demands can be decreased significantly be reducing or eliminating meat, and further reduced by adopting a vegan diet which also excludes egg and diary products. Research suggests an economic inability to sustain a meat-biased diet with an ever-increasing world population.
Additional reading:
Sustainability of meat-based and plant-based diets and the environment

Factory conditions and pharmaceutical use – to reduce cost and optimize the supply flow, many low-cost producers use tightly confined environments specialized to cultivate an animal for human consumption at the lowest cost. This often leads to unsanitary living spaces and animals with decreased immune system response. To counteract this threat to the livestock, many animals are treated with antibiotics or other pharmaceuticals which has the externality of potentially increasing immuno resistance and creating more virulent  or drug-resistant strains of bacteria that can infect humans.
Additional reading: CBS News – Animal Antibiotic Overuse Hurting Humans?Confirmed: 80 Percent of all antibacterial drugs used on animals, endangering human health

Reducing government feed subsidies, providing long-term sustainability

Governments established farm subsidies as a particular remedy – to support farmers in times of overproduction, or when prices fall to a level below cost – an important safety net. Subsidies encourage consumption of more costly goods due to lowering the end-consumer price. A prime example is corn subsidy, which is now the main source of food for livestock [which provides a series of other problems, as many of these animals did not evolve eating corn]. The availability of reduced-cost feed lowers the price at the market, but does not reduce the economic cost of meat on society.

Without debating the benefits or detractors of farm subsidies themselves, it is clear that their distribution is out of sync with the guidelines for a health diet – small portions of meat/eggs/dairy, leafy greens and whole grains.

Due to the high cost and environmental effects of heavy livestock production [in increased greenhouse gases, polluted air and water], the current model does not scale well past the Earth’s nearly seven billion, not including the growing percentage of meat in the average diet.
Additional reading: Eat less meat, save the planet? Livestock nears sustainability limitFarm subsidies not in sync with food pyramid

Eat less meat, save the planet? Livestock nears sustainability limit

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More here.

More to come.

Steps:
1. Write down blog ideas.
2. Research idea.
3. Write blog.

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