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ES295 Lecture Notes - Lecture 2: Nature Reserve, Ecotourism

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School
WLU
Department
Environmental Studies
Course
ES295
Professor
David N Morris

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Document Summary

Generally alternative tourism is considered goof tourism and the latter is bad tourism. While both types of tourism in the end were found to have positive and negative impacts, jafari believed that there needed to be a more holistic and objective approach to ensure the safety and prosperity of the destination. Knowledge based platform - by 1990s, it was realized that alternative tourism was nothing more than a partial and localized solution. As well, both types of tourism offered positive and negative solutions, therefore there was no point in having two of the same results. What happened next was an objective and holistic approach which looked at all positive and negative areas of each area. This allowed people to choose where they wanted to go, what type of trip, and what they would be doing.

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Related Questions

Check My Work eBook Cornerstone Exercise 17.2 (Algorithmic) Keep-Or-Drop Decision, Alternatives, Relevant Costs Reshier Company makes three types of rug shampooers. Model 1 is the basic model rented through hardware stores and supermarkets. Model 2 is a more advanced model with both dry-and wet-vacuuming capabilities. Model 3 is the heavy-duty riding shampooer sold to hotels and convention centers. A segmented income statement is shown below. Model 1 Model 2 Model 3 Total Sales $270,000 $594,000 $616,000 $1,480,000 Less variable costs of goods sold (94,000) (158,800) (334,000) (586,800) Less commissions (4,500) (33,000) (19,500) (57,000) Contribution margin $171,500 $402,200 $262,500 $836,200 Less common fixed expenses: Fixed factory overhead (405,000) Fixed selling and administrative (295,000) Operating income $136,200 While all models have positive contribution margins, Reshier Company is concerned because operating income is less than 10 percent of sales and is low for this type of company. The company’s controller gathered additional information on fixed costs to see why they were so high. The following information on activities and drivers was gathered: Driver Usage by Model Activity Activity Cost Activity Driver Model 1 Model 2 Model 3 Engineering $81,000 Engineering hours 740 73 187 Setting up 200,000 Setup hours 12,400 12,000 29,187 Customer service 113,000 Service calls 13,700 1,480 19,187 In addition, Model 1 requires the rental of specialized equipment costing $19,000 per year. Required: Hide 1. Reformulate the segmented income statement using the additional information on activities. Use a minus sign to indicate any negative margins. Do NOT round interim calculations and, if required, round your answer to the nearest dollar. Reshier Company Segmented Income Statement Model 1 Model 2 Model 3 Total $ $ $ $ Contribution margin $ $ $ $ Less traceable fixed expenses: Product margin $ $ $ $ Less common fixed expenses: Operating income $ 2. Using your answer to Requirement 1, assume that Reshier Company is considering dropping any model with a negative product margin. What are the alternatives? Which alternative is more cost effective and by how much? (Assume that any traceable fixed costs can be avoided.) Do NOT round interim calculations and, if required, round your answer to the nearest dollar. will add $ to operating income 3. What if Reshier Company can only avoid 160 hours of engineering time and 4,800 hours of setup time that are attributable to Model 1? How does that affect the alternatives presented in Requirement 2? Which alternative is more cost effective and by how much? Do NOT round interim calculations and, if required, round your answer to the nearest dollar. will add $ to operating income

What do you believe is the ultimate impact of technology on job creation? Should there be curbs to the pace of modernization?

(Answer in a paragraph(around 200 words))

What Econ 101 Can Teach Us About Artificial Intelligence

Here's why advancing technology often leads to more jobs for humans, not fewer

A Tesla Model X 75D semi-autonomous electric vehicle is parked at the Governor's Mansion in Olympia, Wash., this year. Goldman Sachs predicts that 25 years from now, driver employment will be growing by 300,000 less per year because of autonomous vehicles.

By Greg Ip

Aug 9, 2017

The first chart you draw in Economics 101 is the downward-sloping demand curve. It shows that when the price of something drops, people consume more of it.

This elementary rule of economics is remarkably helpful in discussing the effects of technological change. As I wrote last week, when technology makes something cheaper, we consume more of it, often by finding new uses. This lesson repeats through history: When coal-fueled steam power became more efficient in the 1800s, demand for steam power and coal spread. When automated teller machines made it cheaper to operate bank branches in the 1980s, banks opened more branches.

My column cites spreadsheet programs like Lotus 1-2-3, whose arrival in the early 1980s made repetitive recalculation vastly simpler and faster. This reduced demand for bookkeepers but created more demand for people who could run numbers in new and interesting ways, such as accountants and management consultants.

In a recent article for the Harvard Business Review, Ajay Agrawal, Joshua Gans and Avi Goldfarb, economists at the University of Toronto who study artificial intelligence, say that in the 1990s, economists didn’t buy into the hype that the internet and the World Wide Web would upend everything:

“It wasn’t that we didn’t recognize that something changed. It was that we recognized that the old economics lens remained useful for looking at the changes taking place. The economics of the ‘New Economy’ could be described at a high level: Digital technology would cause a reduction in the cost of search and communication. This would lead to more search, more communication, and more activities that go together with search and communication. That’s essentially what happened.”

They apply that lesson to artificial intelligence and more specifically to machine learning, the use of powerful algorithms to make predictions from patterns in large quantities of data. Machine learning, they say, means many problems will be reframed as prediction problems. Autonomous driving no longer involves programming the car to respond in a certain way to a variety of controlled scenarios, but instead, to watch what humans actually do and then respond the same way.

How does this affect jobs? The authors says it depends on whether the technology competes with or complements what you do. Spreadsheets competed with what bookkeepers do (record keeping and calculation) and thus made them less valuable, but complemented what accountants and consultants do (analysis), and made them more valuable.

Mssrs. Agrawal, Gans and Goldfarb say that AI reduces the value of those who predict things for a living, such as whether a mark on an X-ray is a tumor, but it raises the value of those who use predictions to make judgments:

“When prediction is cheap, diagnosis will be more frequent and convenient, and thus we’ll detect many more early-stage, treatable conditions. This will mean more decisions will be made about

medical treatment, which means greater demand for the application of ethics, and for emotional support, which are provided by humans.”

So AI will create winners and losers within and among industries and occupations. But what will the net effect be? More jobs or less, and for whom? This isn’t knowable, but history suggests that while a specific occupation or industry may suffer losses, the aggregate effects will be positive. The growth in accounting and analytical jobs since the early 1980s is much larger than the loss of bookkeeping jobs.

Nor did all those bookkeepers necessarily lose jobs; many probably became accountants or analysts. As Mr. Gans told me, bookkeepers may have been best placed to exploit the capabilities of spreadsheets: “If you were doing calculations all day as a bookkeeper, chances are you thought a bit about why you are doing that. When you got a spreadsheet, rather than doing one calculation per day you could do 10 to 50, so you had a better sense of what the [calculations] might say.”

Some readers said AI is a much bigger threat than spreadsheets because it has so many more potential applications—and they displace uniquely human abilities. I agree with the first part, less so with the second. I suspect workers in previous eras, whether they drove a horse and plow, handled a stock trade or connected a phone call, thought there was something about their job that a machine could never replace.

Will the disruptions this time around be greater? Maybe, but not necessarily. Goldman Sachs predicts that 25 years from now, driver employment will be growing by 300,000 less per year because of autonomous vehicles. That’s 25,000 jobs per month to make up, compared with the 5,000 per month that the decline in bookkeeping since 1985 represents. But it’s hardly insurmountable in a labor market where five million jobs are created and destroyed each month.

Others said I was ignoring the potential for rogue algorithms and robots to turn on us or strip us of our freedom.

The experts, of which I am not one, tell me AI is nowhere near that capability. (Before the robots can kill us they must first stop killing themselves.) And don’t assume spreadsheets have been all benign. Excel may not have slain anyone Terminator-style, but it did help wound J.P. Morgan through its role in the “London Whale” trading disaster.

Which brings me to the final point. We should not confuse what technology enables us to do with how much better off it makes us. Spreadsheets enabled analysts to construct much more intricate forecasts of stock prices and the economy, but didn’t make those forecasts any more accurate. AI may enable companies to better manage the risks of their products and actions, but those products and actions may not be more profitable. Many a CEO has wondered whether the money they spend on spreadsheet-wielding consultants yields any real value. Some day they may wonder the same thing about their AI-wielding consultants.

Silicon Valley in California and Route 128 in Massachusetts are the preeminent high-tech clusters in the world. Silicon Valley dates back to the early 1930s when Stanford University encourages its electrical engineering graduates to stay in the area and start companies.

In the early 1950s, Stanford created the Stanford Industrial Park, leasing University land to high-tech companies that worked closely with its engineering school. In the mid-1950s, defense contractors such as Lockheed brought dollars to the area. By the late 1960s, a critical mass of such talent had accumulated. For example, in 1968, eight young engineers left their employer over a disagreement; over the next 20 years, they founded 65 new companies, including Intel Corporation, which later created the microprocessor chip, the brain of personal computers.

This pattern repeated: one researcher estimated that in small and medium-sized firms, 35% of the workforce would, on average, turn over in a year. Silicon Valley became a fertile location for start-ups, with dozens sprouting every year -- everything from firms specializing in hardware and software to network firms like eBay, Facebook, and Google. It also became home to investors who specialize in financing new high-tech companies. Silicon Valley's compact geographical location allowed people to form close social and research bonds eve while working for rival firms.

On the other side of the country, a high-tech cluster known as Route 128 lies on a 65-mile highway surrounding Boston and Cambridge. It owes its beginnings to the Massachusetts Institute of Technology (MIT), the top engineering university in the world, as well as funding from the U.S. military, NASA, and the National Science Foundation. In the 1950s Route 128 dominated Silicon Valley, with three times the employment.

But early on Route 128 differed from Silicon Valley in significant ways. Geographically, Route 128 was more spread out than Silicon Valley. Its forms were larger; reflecting the needs of defense contractors during the Cold War. And MIT extended little help to Route 128 firms.

Another major difference between the two clusters lay in how firms were organized. Route 128 firms tended to be "vertically integrated", combining the entire chain of production from research to design to production in the same firm. Silicon Valley firms focused exclusively on research and design, contracting production out to specialized firms that achieved economies of scale. In contrast to the fluidity of employees and ideas across companies in Silicon Valley, Route 128 firms emphasized a commitment to lifetime employment and closely guarded their innovations to remain competitive.

The 1970s and 1980s were harsh for Route 128. Military spending dried up, and it lost its edge in microcomputers when Apollo Computers lost its preeminence to an aggressive Silicon valley from, Sun Microsystems. By 1980, electronics employment in Silicon valley was three times that of Route 128. Over time, Route 128 ceded the advantage to Silicon Valley in electronics and networking. Today its niche is in biotechnology, genetics, materials engineering, and finance.

1. What positive externalities were common to both Silicon Valley and Route 128? What positive externalities were not common to both? Explain.

2. What factors made Silicon Valley such a fertile place for start-ups? How did these factors interact with one another? What inhibited start-ups in Route 128?

3. In hindsight, what could Apollo Computers have done to maintain its advantage in mini-computers? What does this tell you generally about research clusters?