Starting from the bottom up, let’s consider why. Recycling has hit the mainstream public consciousness as second nature to many. It is certainly an important part of living sustainably, and has economic benefits as well by creating demand for processing recycled materials. However, in comparison to the other two R’s, it has a couple of main elements that keep it my 3rd choice:

1. You can’t recycle everything.
2. The process of recycling has its own pollution element that offsets some of the benefits.

Essentially, you have a limitation and an offsetting element that hinder the potential benefits. Here are two links for those interested in more detailed information on some of the benefits and disadvantages of recycling.

Moving along, consider the act of reusing. Reusing is my second favourite option, as it works hand in hand with the act of reducing to lower demand for new products by reusing old.

Of course, not everything can be reused. However many things can, and eco-minded (and plain old conservative minded) folks can be innovative in what they reuse and how they do it. For example, check out this article on innovative ways to reuse common household items for crafty individuals (via @GreenSmith on Twitter). Using some of the ideas, one might envision their next gift giving event becoming that much more sustainable**, as follows:

Reuse an egg box to mix paint, that you can then splatter on reused newspaper with an old, reused toothbrush for nice, crafty wrapping paper. You can even skip the tape and reuse a string or ribbon to tie it! And if you need to glue anything for added effect, reuse a burnt out match stick as a dabber.

I may or may not have purposefully used the word “reuse” as many times as possible in there for added emphasis! In other words, if you can find a “re”-use for it, give’r. Not only can it be fun, but reusing can be more sustainable than recycling as well.

Finally, we reach my top choice of the three R’s: Reduce. Reducing is available to everyone, and applicable to everything. It undermines the need to have to reuse or recycle in the first place–the less you use, the less waste you will create to throw out/recycle/reuse. In some cases, this can include a lower cost financially as well–such as reducing the amount of lights you leave on at home.

In spite of the passive nature of the act, I believe that reducing one’s consumption and in turn, environmental footprint, is a strong proactive approach to maintaining a sustainable lifestyle. You have to make the choices to forgo certain comforts or use less of certain products than you or people you know normally would, and these are not always easy to do.

The concept itself, however, is as straightforward as it gets–live simply, tread lightly; in other words, use less. As Adam Shake from Twilight Earth puts it: “Sometimes the greenest thing to do is to do nothing at all.” In a great example, he notes how it is “greener” to recycle your water bottle, but even better to not buy bottled water in the first place — a point expanded on from his post on “pre-cycling”: “Recycling is great, but keeping recyclables out of the recycle stream is Greener than recycling.”

Ultimately, the no-limits, accessible, extra-benefit approach makes Reduce the clear first choice against Reuse and Recycle–and again, this is not to say the other two are not important.

There are a lot of grey areas I haven’t touched on in my very brief and general analysis of the three R’s here. Whether or not you agree with my assertions, the point is to get you thinking about making conscious choices, considering your intentions and what your actions mean for the environment.

**Even more sustainable would be to find ways of spending time or other non-consumerism-related activities to “give” (working with the notion of reduce).

Creative Commons Attribution: “KL Design Week 2009“, Flickr, myadlan

The electric car regained attention during the late 1990s as the environmental movement began to twinkle in the public eye.  This was especially the case in California, which suffered from some of the worst air quality in the country. To alleviate this, the California Air Resources Board (CARB) in Sacramento, CA passed a zero emissions mandate requiring an increasing percentage of cars sold in California to have zero tailpipe emissions.  In 1998 the requirement was 2 per cent, in 2001 it was 5 per cent, and in 2003 it was 10 per cent.  The policy caused much controversy, including Federal Government involvement, and resulted in a set of lawsuits by various automakers concerned about the lack of short term profitability of an electric fleet.  Check out the film Who Killed the Electric Car for details on this situation–while one-sided, it certainly raises some valid questions.

In the meantime, during the zero emissions mandate California saw the re-emergence of a fleet of electric cars from various companies.  While this fleet had all but disappeared by 2004 in favour of the Hybrid, the electric car is again regaining attention around the world.  In Amsterdam, Netherlands, the city will install 45 ChargePoint stations as part of a two-year public demonstration project.  This will be followed by an additional 200 stations by 2012, with the expectation of having enough stations to fuel 10,000 electric cars by 2015.  Similarly, London recently saw its 100th electric car charging station installed by a company called Elektromotive.  This is in accordance with Mayor Boris Johnson’s plan to make London the “electric car capital of Europe”, with 25,000 charging stations in London by 2015 and a requirement that all new developments and 20 per cent of all new car parking spaces be equipped with charging points.  In these areas as well as in North America, automobile companies are coming out with new and improved EV models, with better mileage and more efficiency.

So what is the deal with the electric car?

From the consumer side, EVs are cheap to operate and maintain, not requiring the usual oil changes and other such maintenance costs of an internal combustion engine (ICE).  Of course, the argument that driving the EV car is cheaper is subject to price changes as the market demand for electricity rises.  Another major benefit for countries like the U.S. in adopting EV technology is decreasing dependence on foreign oil — “a matter of national security” as former CIA director asserts in Who Killed the Electric Car.

On the other side of the debate, EV owners face many structural challenges in operating their car.  Electric cars have a limit on how many miles they can drive before needing to charge again.  While the limit is well above the average miles driven in a day and increasing with research, there is still the need for supporting infrastructure within and beyond the market to support electric cars and mitigate their driving limits.  This would include the development of charging stations mentioned above on a wider scale.  Furthermore, the initial investment hurdle make short term profits for producers an issue, creating less incentive for automakers.  The cost of production and in turn, cost of the vehicles, create room for a government subsidy to help develop the market.  In the UK, for example, the government has pledged £250 million in subsidy funding.  If the EV is indeed worth its noted value, such a market intervention can help stimulate the economy.  However, with the financial burden to support the technology, there is quite a bit debate over the value of the electric car.

Turning to the environment, the most obvious benefit of an electric car is its deviation from the use of gasoline to drive.  In countries with low-carbon power sourcing, the replacement of oil by electricity creates significant potential for reduction of carbon emissions.  Many studies that examine the life cycle analysis of the environmental impact of EVs (also called the “well-to-wheel” (WTW) assessment), find a generally lower carbon footprint in electric vehicles than petrol based ones.  However, while the majority of them demonstrate at least some reduction in emissions, the numbers, methods and calculations are highly varied.

Does this mean the EV is not worth it?  In spite of some of the data, I’d argue no.

According to Pike, “the complete replacement of all 30 million passenger cars in the UK, which form 12 per cent of the UK carbon footprint, can be shown to lower this figure to just 10 per cent at best.”  While this may not be much, it’s something–although Pike is rightfully concerned about the cost/benefit portion of the analysis.  Similarly, the U.S. Department of Energy finds WTW efficiencies (including all transition stages, from raw materials to motive power at the drive wheels) at 17% for EVs versus just 11% for gasoline-fueled vehicles.  In other words, the EVs can indeed be more efficient in fueling by plugging into the energy grid than using the gasoline burned in an ICE, even if by a lesser factor than it may initially seem.

Ultimately, like any “eco-friendly” product on the market – greenwashed or not – creating a consumer movement from the notion of conserving the environment only helps fuel the demand for more efficient and environmentally sound technology.   Not only this, but behavioural changes from eco-minded individuals can have powerful impact on an aggregate level.  Simple actions of knowing a limit to your daily mileage may cause people to plan their day more efficiently to reduce overall KM drive.  The opposite is also possible — one might get into the habit of driving MORE because it “costs less” per kilometre — but at least the economic conservation of the EV generally comes with a similarly attached environmental conservation awareness.

Moreover, because of this very debate, the transition to electricity for fuel with the attached environmental awareness is and has been encouraging further research and development in the renewable energy sector.  With renewable energy added to the equation, electric cars are starting to look a bit more like that utopian vision after all.  Check out the report from the recent Summit of the U.S Department of Energy on the topic, including the knowledge of over 120 experts in the field here for more information. We may be “miles” away (literally and figuratively), but we’re getting there.

Creative Commons Attribution: “Electric car outside the office“, Flickr, renaissancechambara

What many people may fail to realize is that not only does the health of our planet depend on improved environmental standards, but our health may as well. Health complications of industry can essentially be divided into two categories, both direct and indirect.

Direct health conditions which have arisen as a result of the burning of fossil fuels, for instant, are increased asthma rates in areas with high smog indices. Even mild cases of asthma can deteriorate overall respiratory capacity over time and leave breathing seriously diminished if the quality of the air people breathe is unimproved. Release of chlorofluorocarbons into the atmosphere has shown to lower our filtered sunlight, increasing ultraviolet light exposure. Ultraviolet light has been conclusively link to skin cancer. Perhaps it is no surprise then that skin cancer incidence in countries like South Africa and Australia, where the atmosphere is most diminished, is much higher than other areas of the earth.

Indirect health consequences include those which can be attributed to antiquated industrial infrastructure, including toxin exposure among workers. Oil refinery workers, for instance, are shown to have a much higher chance of developing mesothelioma, a rare cancer caused by exposure to asbestos, than those in cleaner industries. While asbestos was banned for most uses in the late 1970s several of these refineries and factories are still using pre-ban equipment which is exposing workers to harmful asbestos fibers.

We see then, that there is a clear advantage to implementation of cleaner, more sustainable energy policies and environmental attitudes, not only for the health or our planet and our posterity, but that of world’s population even today.

Creative Commons Attribution: “Asbestos“, Flickr, ktheory

Contributing Author: James O’Shea is a representative of maacenter, the web’s leading organization for relevant and authoritative information regarding asbestos and health complications associated with asbestos exposure. The organization is staffed entirely by writers and other developers who recognize the importance of building awareness in the fight against cancer.

“Clean” Coal Potential (The Supporting Side)

America’s Power, amongst others, think so. According to the site’s research, there is $6 billion in clean coal research underway right now in 41 states. A portion of this is for improved modeling and efficiency, as well as technological innovation to further reduce the regulated emissions mentioned in Part II . The majority is directed towards researching the process of sequestering carbon, or “carbon capture and storage” (CCS).

CCS is the process of capturing carbon dioxide from coal plants or other large point sources. The CO2 is then permanently stored away from the atmosphere in various areas, such as being injected into geological formations and old mines. If successful, CCS has the potential to account for 10 to 55 percent of the total CO2 mitigation efforts–reducing emissions from coal plants by 80-90 percent. (Source, Section 8.3.3)

While there are some inefficiencies with CCS, to be outlined later, the method has gained attention worldwide and already has a functioning CCS plant in Germany called the Schwarze Pumpe power station. The plant is an important innovation for the coal industry; as NASA physicist Dr. James E. Hansen asserts, the coal industry cannot continue at its current levels of pollution: “Saving the planet and creation surely requires phase-out of coal use except where the CO2 is captured and sequestered (stored in one of several possible ways).” (Source, p2)

America’s Power and others’ plan for clean coal is certainly in the works. In Austin, Texas, an economical technology is being developed with the goal of a 90 percent reduction of CO2 emissions from coal-fueled plants. The project is being headed by Chemical Engineering Professor Gary Rochelle and his colleagues at The University of Texas at Austin. TXU Power will donate $1.8 million to the project as part of the TXU Carbon Management Program. This financing has in turn been matched by a dozen or more other power companies and process suppliers participating in TXU’s program. Rochelle hopes to use the funds over a six-year program to improve the efficiency of the carbon capture process so it uses ten percent less energy.

The main argument America’s Power presents against turning all efforts to renewable energies, such as solar and wind power, is their inability to meet “base-load” demand. Base-load power essentially refers to the energy required to meet a constant minimal demand and keep the electricity grid energized. The other category of electricity production, peaking power, is the variable energy demand that increases or decreases throughout the day when base usage changes. As the site outlines, “Peaking power uses intermittent power resources like solar and wind that produce electricity only when there’s sufficient direct sunlight or sufficient sustained wind speed. For base-load power, you must use so-called hard-path fuels such as coal, which can provide power 24 hours per day.” Others, such as Repower America have countered that renewable energies indeed have the ability to meet base-load demand if strategically implemented. I leave this part of the debate to the reader’s own further examination.

“Dirty Coal” (The Opposition)

There are still many that take issue with investing in clean coal technologies. While the possibilities of the technology are promising, the time-line, cost, and lack of concrete supporting policy concerns some scientists and groups like The Alliance for Climate Protection (responsible for projects like This is Reality and Repower America).

Many of these concerns are highlighted in a publication by the Union of Concerned Scientists. The report notes that in its current form, CCS technology would greatly increase the cost of building and operating coal plants–calculating an increase in operating costs of an estimated 21-91 percent for plants near a storage location (Source, Section 8.3.3). According to the report, plants further away or application of the technology to existing plants would be even more expensive. Furthermore, the process itself uses energy, thereby increasing the fuel needs of the plant itself by 25-40 percent.

As mentioned in the previous section, there is current research being conducted to improve the efficiency of this consequence. Another report, done by the Coal Utilization Research Council (CURC) reports that if research, development and deployment of the technology are successful, CCS coal-based electricity generation in 2025 will coast less than the cheap unsequestered coal-based electricity generation today. Nonetheless, the question has been raised whether it is necessary to wait and whether the resources would be better invested in other energy technologies such as wind and solar.

In addition to costs, the process of storing CO2 in large quantities has raised concern over associated environmental risk such as leakage, which could lead to ground water contamination. Careful selection and monitoring of the geologic storage (or “sequestrian”) sites is needed, as well as the development of policy mechanisms to support and guide it. Ultimately, the vast amount of new infrastructure required to construct, capture, process, and safely transport large quantities of CO2 has yet to be demonstrated in commercial-scale, fully integrated projects at coal-fired power plants. To truly measure the cost-effectiveness and environmental of CCS in comparison to other carbon-reducing strategies, demonstration projects are needed.

Conclusion

“Clean” coal, and specifically CCS technology is certainly an exciting prospect in many aspects, but one must wonder how different the costs will be, and whether it would be more efficient to instead invest in renewable energy sources. Coal may be our most “abundant” source for now (given the base-load argument), and we have enough for “over 200 years”, but the next question is: And then what?

In addition, how long is it going to take for the clean coal to become commercially available? Mandated? How does this compare to the time it would take to further develop other markets? Is it possible for these other energy technologies to meet our increasing energy demands? What are the costs and benefits of each?

Consider the following: Suppose it is decided that coal does not have a future in the climate change solution. In this case, if aggregate energy demand remains constant, the demand would have to be re-distributed throughout the other energy-generating market sectors, such as wind, solar, natural gas, etc. In the real market, an increase in just half of the energy demand from coal generation to natural gas (which has a lower CO2 output per kWh), for example, would represent an increase of over 25% of all electricity demand shifting to natural gas, as coal makes up over 50% of the market in electricity generation. This would cause prices to soar in this market, coal would again become more attractive, and the cycle would continue until a new equilibrium was met. In this example, it is evident phasing out coal completely is a tricky business without strong policy to support and mitigate the effects on other markets (in the short term).

Clean energy, whether it is renewable sources like solar or wind or new clean coal technology, is expensive. According to a study hosted on America’s Power’s blog, rising energy costs in 2008 consumed 20% of after-tax income for families earning $50,000 or less per year, and 25% for those at $30,000 and under–a statistic is up from 11% in 2001. (Note: as always with statistics, take these numbers with a grain of salt. I haven’t looked at the source data to decide whether the results account for real income, tax increases, etc. Either way, it is true that energy costs have gone up in recent years, and it is the pattern and the disproportionate impact that matters.)

I’m not going to do a cost-benefit analysis here to compare the renewable energy and clean coal markets–there are arguments in each sector that illustrate the potential for job creation and economic stimulation. My point here is that there is no silver bullet solution. The only solid conclusion is that change is essential in the energy industry, and that it will cost.

If coal is a desired part of the climate change solution, we need to see the commercialization and legislation of capture and store techniques for all coal plants. If not, there needs to be strong investment and innovation to develop other markets and a strategic phasing out of coal to mitigate market effects. Either way government investment and legislation to help develop and promote a market will help reduce prices for both consumers (by increasing demand), and for producers (investment in research and development to enhance efficiency can help reduce input costs.) There is a strong role for government in this issue, and it is imperative that the U.S. and other OECD countries take a leadership role in promoting clean energy technology. This is where your voice comes in–I’ve just scratched the surface of the issue, but hopefully I’ve stimulated some discussion. Whatever you choose to support, make sure you educate yourself about it and the opposing arguments.

A couple of Resources for readers interested in measuring their own footprint or finding more information:

Environmental Protection Agency: Clean Energy
Repower America
Natural Resources–Government of Canada
Ecokids (measure your energy efficiency)

Creative Commons Attribution: “SOOC“, Flickr, Robert S. Donovan

Part One

I was spurred to write this article by a Twitter comment made to me (@NikkiJade) by @AmericasPower–one of coal’s most vocal supporters in the U.S..  America’s Power is sponsered by a partnership of industries involved in producing electricity from coal, the American Coalition for Clean Coal Electricity (ACCCE).  The organization strongly believes coal has a role to play in the future as America’s largest source of electricity generation.

The comment attempted to correct The Green Rocket’s video post that claimed clean coal in the U.S. was a myth, asserting: “Actually, technology has made coal 77 percent cleaner since 1970. See for yourself: http://snipurl.com/77-percent”. Indeed, by its data there has been some improvements in the efficiency of coal-burning plants.   As the site illustrates:

“Over the last 35 years, America’s coal-based electricity providers have invested more than $50 billion in technologies to reduce emissions. Due to investments like these, our coal-based generating fleet is more than 77 percent cleaner on the basis of regulated emissions per unit of energy produced.

The calculations are based on five pollutants: carbon monoxide, volatile organic compounds, sulfur dioxide, nitrogen oxide and particulate matter. The data from the U.S. Environmental Protection Agency reflects the environmental performance per unit of energy produced. That is, the relationship of emissions per billion kilowatt-hours. From 1970 to 2005, the value for that ratio fell from 30,510 short tons per billion kilowatt-hours to just 6,970 short tons per billion kilowatt-hours — a reduction of 77.15 percent.”

This certainly seems like an astronomical achievement, though the organization claims “this is just a start”.  Which is true.  In reality, the data does not include carbon dioxide statistics–arguably the most significant concern surrounding coal combustion.  This was unfortunate to me, as the issue is presented both on America’s Power and on the ACCEE’s main site in a manner that makes overall emissions appear to be down over 70%, which is untrue of CO2.  As a result, many who do not take the time to look further into their claims could be easily confused that the changes are more significant than they are.  This is just my opinion, potentially biased from frustration that it took me a while to set the full context of the data when it was first presented to me.  In any case, opinion aside, the data used is specified as “regulated” emissions, and CO2 has only recently been in discussion to become such, as mentioned in Part One.  In addition, both sites make specific reference to CO2 as a separate challenge of interest and necessity.  And so, I digress.

So in reality, while other areas have improved, there has been only minimal improvements in CO2 emissions.  America’s Power’s blog, Behind the Plug, asserts that there have been “improvements in efficiency of CO2 emissions”, though a representative graph of this like we were presented with the other emissions was evasive.  The organization does, however, provide valuable information about the potential of clean coal technologies, as well as some very good cost-benefit studies.  I will go over this part of the debate and these solutions in Part III.

From another source, a working paper by the National Petroleum Council claims unit efficiency of coal-burning plants has barely changed.  It claims the efficiency improvements over time that are demonstrated by data in the EIA and IEA outlooks are not as straightforward as they seem.  Instead, the improvements largely come from substitution of old plants with new plants that have better efficiency (p3). What they mean by this is that while there have been improvements, innovation is not as significant as it seems–upgrading older units improves efficiency, so much of the data could be representing minor upgrades rather than full scale innovation.  In other words, if we’re not at a stage of having clean coal plants in the U.S., why build new ones until we are?  There is technology out there, but it is not yet widely commercialized or with strong policy to implement it.

Furthermore, since I am analyzing the data and methodology on a surface level here, it is also worth mentioning a report done by the Energy Information Administration (EIA).  The study develops and analyzes probably the most comprehensive measurement of estimating the amount of CO2 emitted from coal-burning in the U.S..  It uses factors for estimating the amount of carbon dioxide emitted from coal that are not generally accounted for in other studies.  Much of the data on coal are small samples that are generalized, and therefore hold important factors such as geographic origin and content ratios constant.  In more simple terms, where the coal comes from geographically means it can have a different make-up in terms of how much carbon, hydrogen, oxygen, sulfur, and etc. that it holds.  These different types are what the EIA calls “ranks”, and uses the rank and geographic origin to weight the data for more accuracy in measuring emissions.

Why am I rambling about this?  Well, as the EIA puts it, “The amount of heat emitted during coal combustion depends largely on the amounts of carbon, hydrogen, and oxygen present in the coal and, to a lesser extent, on the sulfur content. Hence, the ratio of carbon to heat content depends on these heat-producing components of coal, and these components vary by coal rank. ” In other words, what might seem like shifts in technological efficiency from year to year emissions may actually be less significant when shifts BETWEEN types of coal are taken into consideration.

Essentially, the point I’m trying to make is that no, “clean” does not currently characterize the United States coal industry–although there is seemingly potential, as I will outline in Part III. It is still a major contributor to the climate change problem, and while there is a great deal of money going into research, there is not yet the legislation and commercialization of methods to provide environmentally sound electricity from coal.  This has led groups like Repower America and This Is Reality to maintain that we do not need to wait for coal to become clean, instead we can invest time and resources into alternative energy markets.

Before I finish, let me just clarify what I mean by “clean” coal — basically, coal that is not contributing significant harm to the environment, or more specifically, with significantly reduced CO2 and other emissions and high efficiency.  There is a separate debate about whether coal will ever be fully “clean”, as per its method of collection being quite destructive to mountaintops, for example.  Of course, many human-made things are intrusive… but in any case, that is a debate to be explored another time (before I go into another digression…)

Parts One and Two of this article set demonstrate that the coal industry needs significant overhaul if it wants to be a part of the climate change solution.  Stick around for Part III to find out about developing clean technologies and their challenges, the financial burden of clean energy and renewable alternatives, and finally some general policy recommendations I picked up from the various studies.

Creative Commons Attribution: “Coal Transport – close-up“, Flickr, MoToMo

Source of Data: Human-Related Sources and Sinks of Carbon Dioxide (2008)

Coal has the highest carbon intensity among fossil fuels, resulting in coal-fired plants having the highest output rate of CO2 per kilowatthour (kWh) (Source, p7).  Clearly, coal-plants have a huge role to play in climate change.  Yet, the improvements in efficiency of CO2 emissions in the U.S. coal industry have been minimal, as I will outline in detail in Part II.

This sentiment has also been reflected in recent politics and policy in the U.S..  Obama’s administration has been aiming for green policy reforms even during the election, and including clean coal.  As quoted by Behind The Plug, Obama maintained a platform as a “big proponent of clean-coal technology…I want us to move rapidly in developing those sequestration technologies that’s required.”  He noted an Obama administration will, “provide incentives to accelerate private sector investment in commercial scale zero-carbon coal plants.”  Specifically, “What we need to do though is to put clean coal technology on the fast track and that means money. It means investment in research. That’s something that we should have already been doing.”

Since taking office, we’ve seen the Obama administration take a number of actions in support of progressive environmental policy.  More recently, EPA review of scientific evidence on the impacts of carbon dioxide has led to  expectations that the government will regulate CO2 emissions.  This is a significant turnover from the Bush presidency, where CO2 was not regulated because the EPA argued it was not a pollutant.  After a major review of the evidence, this decision was overturned and carbon-cutting legislation is now being proposed in Congress.  In addition, the EPA decision – known as an “endangerment finding” – enables certain regulatory cuts to be made without waiting for the proposals to become law.  This is of vast importance not just for Americans, but also for developing countries and the rest of the world looking for leadership in clean energy and climate legislation.  So in relation to the coal industry, this means there is a demand for policy intervention and clean technologies to secure a safe environmental future.

Look out for Part II which will assess the current state of the coal industry in more detail.

Creative Commons Attribution: “No Title“, Flickr, jonasclemens

I love wildlife and I love animals in particular, so why does the introduction to this article sound antagonistic? Here’s my analogy for the day: If a child cries because it wants a toy, you can ignore it only so long before you give it a cookie as a consolation prize to stop the crying. After the cookie is eaten the child realizes that it still didn’t get the toy. This makes the child feel even more defeated–but hey, they got a cookie at least, right?

The following article gathers together a summary of events; I can’t help think that this “victory” wasn’t actually won. I leave it to the reader to come to their own conclusions.

Call to Action!

On April 2 the National Wildlife Federation urged immediate action to protect America’s polar bears from the impacts of climate change by listing them under the Endangered Species Act (ESA). The U.S. Fish and Wildlife Service (FWS) proposed to list the bear as a threatened species under the ESA within a year. Missing the deadline by 3 months raised suspicions that the Bush Administration was attempting to avoid scrutiny of oil and gas leases in the Chukchi Sea on February 8, 2008.

On April 25 the Committee on the Status of Endangered Wildlife in Canada stated that the polar bear was nothing more than a “special concern” nonetheless requiring Canada’s government to develop a management plan to protect the bears if it agreed with the label. Canada was being realistic to the situation: Polar bears were not at risk of going extinct. Through legislative action they still aimed to protect one of Canada’s iconic symbols. At this point it was expected that the FWS would update the polar bear’s listing by the end of June.

U.S. District Court Judge Claudia Wilken in Oakland, Calif., on April 29 ordered the Interior Department to make a choice on the polar bear petition by May 15, 2008. No more delays! By May 14 a decision was indeed made to list the polar bear as a threatened species under the ESA based on “evidence” that the animal’s vital sea ice habitat is shrinking and likely to continue shrinking for the next couple decades. Computer models depicting and theorized a 30% decrease in sea ice by 2050, therefore concluding the possible extinction of the polar bear in 45 years. Cato Institute’s Patrick Michaels reaction to this is that the department’s listing is based on “obsolete science”, making this more of a political than scientific decision.

Conspiracy Theory or Shady Business?

Continued speculation exists that Interior Department was waiting until a $2.7 billion lease of oil reserves off the Alaskan coast were sealed before dealing with the polar bear listing dilemma. Minerals Management Service was closing leases covering 46,000 square miles for continued exploration of oil and gas drilling in the Chukchi Sea. The polar bear being listed as “endangered” would have subjected those lease sales. Officials denied any of this had a bearing on delays in decision making. (Source)

The Bush administration announced a rule allowing oil drilling in the Arctic to continue despite the polar bear now being listed as a threatened species claiming that oil and gas exploration and extraction showed no evidence of harming the bears. You can read the entire press hearing on the following website with annotated comments by Andrew Revkin of Dot Earth (NY Times).

Bye-Polar Disorder Begins

The Bush administration was forced to acknowledge the effects of global warming on the environment by approving the polar bear’s addition to the “threatened” list. Thus begins the well defined “bye-polar disorder“, coined at the Climate Progress blog (as far as I can find). To summarize the site’s definition:

You list the polar bear as threatened because of its melting polar sea ice habitat, but then do nothing to actually protect that polar habitat from its primary threat, greenhouse gas emissions from fossil fuel combustion.

The technical details of this exemption: global warming pollution. This exception cancels out any protective measures the “threatened” listing could have provided for the polar bear. Federal agencies are free to continue their activities without concerning themselves with how global warming pollution affects the polar bears or their habitat, and that oil and natural gas development in the region should not be blamed for the polar bear’s welfare. (Source)

The climate change debate sees opinions on either side of the spectrum; the vast majority of scientists agree that data proves dire consequences to Earth’s biological diversity, while industrial companies and lobbyists, as well as a minority of scientists, believe it’s something we can do nothing about and agree to just let it play out. This situation depicts a clear split between the values of both sides. (Source)

The future will be what it’ll be, but what is here and now, and worth focusing on, whether humans are at fault or not, are situations like the one discovered by Canadian military accompanied by scientists who discovered a new network of fractures in the Arctic region. All arguments concerning the polar bear aside, time and effort needs to go into studying how natural changes like these will affect the biodiversity of the Arctic region.

The Bare Numbers

In the early 1970s, the polar bear numbers were estimated at about 5000, dwindled down significantly due to over-hunting. Today the bear population is five times higher at about 25,000 thanks to efforts made by the Marine Mammal Protection Act of 1972. Nineteen sub-species are scattered across five countries: USA, Russia, Norway and Canada. Canada has an estimated 15,500 polar bears–roughly one third of the global population. The David Suzuki Foundation states that 5 of Canada’s 13 polar bear populations have decreased 22% since 2004.

The polar bear population in Western Hudson Bay of Canada has declined from approximately 1200 bears in 1987, to 1,100 bears in 1995, and then to fewer than 950 bears in 2004 due to ice loss. Arctic sea ice loss set a record low in 2007. This year, the sea ice melt season is already shaping up to break the record set in 2007. (Source)

With the rising temperatures in Arctic regions and ice melting at an accelerated rate, computer models projects a 30% decline in sea ice by 2050, based on those statistics from the US Geological Survey in 2007, polar bear numbers are estimated to be cut to two-thirds by that year.

Although illegal hunting does exist–for example, in the Chukotka region of Russia off the Bering Sea, it is said over 200 polar bears are killed a year–the polar bear has seen some protective measures through the years as well that helped their numbers rise. There are 15 native villages in Alaska who practice substance hunting. This will continue to be allowed, but has been reduced due to younger generations not liking to eat the bears. There are also conservation efforts in place in the area; a voluntary quota overseen by the Alaska Nanuuq Commission. The annual take has dropped from 100 polar bears to the upper 50s. In Nunavat, Canada’s largest territory, the annual hunting quota was dropped from 56 to 38. (Source)

The Rise of the Grolar Bear!

The first grolar bear was found in the wild in April 2006 after a DNA test conducted by Wildlife Genetics International in British Columbia, Canada confirmed the bear was mothered by a polar bear with a grizzly bear father. Hybrids like this aren’t uncommon in zoos but are extremely unlikely in the wild due to each bear requiring extended mating rituals to reproduce. A female polar bear ovulates only after spending several days with the male; once ovulation occurs they mate for several days after. (Source)

On May 7 scientists suggested that melting ice in Arctic regions would cause polar bears to be stranded on beaches as grizzlies explored more northern regions. During these rare contacts, unions are made, and hybrids are born. I’m of the group of people who don’t see a negative in this scenario. These grolar bear hybrids would preserve the polar bear genes. Even if extinction of the polar bear is a possibility, science would always have access to their genetic makeup.

Not Just a Polar Bear’s Playground

With all this information about the polar bear it seems unfortunate that the perspective is so narrowly focused. Why just the polar bears? Are they the only creatures under the threat of extinction based on the scientific models and environmental predictions described above?

The narwhal, a whale with a “unicorn” like tusk has an even greater connection to its Arctic habitat. Its feeding ranges are more narrow and diet limited that researchers fear they’ll be unable to adapt to increased Arctic temperatures. Narwals exist in a very specific region between Baffin Island and Greenland numbering just over 80,000. (Source) Could it encounter different complications due to global warming that will get it the attention that came to the polar bear?

With much more massive numbers we have the arctic caribou sitting at an estimated 377,000. This estimate was recorded in July 2007 and although high, it still represents a drop in about 113,000 caribou since 2004. The downward trend may be due to several recent mid-winter freeze events. (Source) Could this be a trend directly related to less hospitable conditions?

How about the ringed seal, ribbon seal and bearded seal who give birth and nurse their pups on the ice and use it as a resting platform and feeding zone? The ringed seal in specific requires that the ice remains stable through spring in order to successfully raise its young. If the ice melts too fast, mothers leave young sooner, increasing the newborn mortality rate. If any of these seals give birth to young on land they highly increase the chances of predator attacks. Will these Arctic mammals be ignored? (Source)

… And the Battle Cries Continue …

Is it a surprise that new battles are on the horizon and that battle shouts can already be heard and chanted across hordes of activists? Early this month on June 9, two environmental groups filed a notice that they plan to sue the federal government for not imposing new regulations concerning oil and gas development in Alaskan waters in correlation with the protective status of the polar bear being listed as “threatened”. The Center of Biological Diversity and Pacific Environment are claiming that the Interior Department is in violation of the Endangered Species Act by allowing oil companies to continue their operations unchecked.

On the other side of the pendulum swing comes a lawsuit that will sue the U.S. government for listing the polar bear as a threatened species, arguing that it’ll slow development activities in Alaska. Any development requiring federal permits or funds would have to go through a time-consuming but required consultation, mandated by the Endangered Species Act making certain the polar bear was not being jeapordized.

Then there are those going down the obvious path of protecting other Arctic creatures. The Center for Biological Diversity formally notified Secretary of the Interior Dirk Kempthorne of its intent to file suit against him for refusing to process an Endangered Species Act listing petition for the Pacific walrus, imperiled by global warming and increasing oil development in its habitat in the Bering and Chukchi Seas off Alaska.

Conclusion?

I’m not sure it’s even worth giving a conclusion to this article–it might not be a conclusion after all in about a month. In short, this whole scenario has opened up some very interesting dialogue between politics and science, human interference and natural circumstances, and business and ecology. Most importantly, these events have created a symbol out of the polar bear, the catalyst for Arctic awareness, no matter how things resolve themselves. We can only hope that in 50 years the polar bear won’t be nothing more than a martyr for a cause.

I’m going to go eat some cookies now.

Referenced Works

• www.greenpeace.org, Dec 15, 2005, “Conservationists File Lawsuit to Protect Polar Bear Under Endangered Species Act”
• www.nwf.org, April 2, 2008, “National Wildlife Federation Urges Protection for Polar Bears”
• www.reuters.com, Apr 25, 2008, Louise Egan,”Polar bear seen in trouble, not endangered”
• www.sciam.com, May 14, 2008, Larry Greenemeier, “U.S. Protects Polar Bears Under Endangered Species Act”
• www.planetsave.com, May 15, 2008, Joshua S. Hill, “Polar Bear Finally Listed as ‘Endangered’”
• dotearth.blogs.nytimes.com, May 14, 2008, Andrew C. Revkin,”The (Annotated) Polar Bear Decision”
• www.climateprogress.org, May 14, 2008, Joseph Rom, “Bye-polar Kempthorne: Polar bear IS endangered, but ‘Rule will allow continuation of vital energy production in Alaska’”
• www.greenpeace.com, May 15, 2008,”Endangered Species Act threatens Polar Bears”
• www.dailygalaxy.com, May 16, 2008, Rebecca Sato, “Is the Polar Bears’ Predicament a Sign of Things to Come?”
• www.planetsave.com, May 25, 2008, Joshua S. Hill, “Giant Cracks Appearing in Arctic Ice”
• www.guardian.co.uk, May 17, 2008, Richard Luscomb, “Great white hope”
• www.climateprogress.org, May 10, 2008, Joseph Rom, “Warming’s new hybrid — the Grolar bear or Pizzly”
• www.enn.com, May 7, 2008, “Arctic ice melt could see rise of ‘Grolar bear’”
• www.guardian.co.uk, May 13, 2008, Edward Helmore, “Mysterious Arctic whale under threat from changing habitat”
• www.sitenews.us, May 19, 2008, “Latest Census Shows Decline in Alaska’s Largest Caribou Herd”
• http://www.acia.uaf.edu/pages/overview.html, Cambridge University Press, “Impacts of a Warming Arctic”
• www.reuters.com, Jun 9, 2008, Yereth Rosen, “Groups to sue over oil impacts to polar bears”
• www.reuters.com, May 22, 2008, Yereth Rosen, “Alaska to sue to block polar bear listing”
• www.biologicaldiversity.org, May 27, 2008, “Lawsuit to Be Filed Seeking Endangered Species Act: Protection for the Pacific Walrus”

What Was the Size of the Sample Taken?

A sample refers to a subset of something, while the set from which the sample is obtained is called a population. So if environmentalists want to study the waste per kilogram created from the production of metal door handles, they would not test every single door handle created but might take a sample of 100 door handles to study from the total population of door handles produced.

The key point about samples is in their size. In general, the larger the sample size is, the more representative it will be of the population. So if environmentalists were to only sample 5 door handles, this may not generate enough data to make a conclusion about the waste created from the entire population of door handles. Whether a sample size is too small or not depends on the size of the total population. For example, if only 10 door handles were produced (the total population), sampling only 5 of them would be sufficient, whereas if 10 000 door handles were produced, a sample of only 5 would likely be too small.

How Was the Sample Taken?

I wanted to bring up the importance of randomness here. A random sample is taken when each observation (unit of a sample to be observed) is drawn at random from the population. This means that no unit is more likely to be selected than any other unit, and each draw is independent of all other draws.

If a sample is not selected randomly, the results of the data will be biased. For example, consider a poll being taken of the importance of environmental policy in a presidential candidate’s platform. If the persons conducting the poll decide to do so by asking everyone that is in a nature park one day, they will likely find more value placed on the environmental policy than they would if they just asked people selected at random on throughout the entire region they are polling. This is because individuals going out of their way to spend time in that nature park may value the environment more as a part of their leisure activity, so only asking them may bias the results.

What Assumptions are Made?

Making assumptions are necessary in most models of statistics, especially when studying large scale populations such as the U.S economy. An assumption just means that the model used for testing a specific number of variables might hold other factors constant. To return to the education and wage model I mentioned before, one assumption made in this case may be to hold number of years of experience constant; that is, assume that the number of years of experience does not affect the wage for the sake of testing the effect of education on it.

With regards to deciding whether an assumption is effective or not, just think intuitively: how strong/logical is the relation of the variables being tested in the first place? If an assumption being made potentially has a larger impact on a variable than the other variable being tested, it is probably not an effective measure of data. For instance, if you are measuring a student’s grade based on how many hours of music a day they listen to, and assuming all other factors are constant (such as how many classes they attend), this assumption is not very realistic because there isn’t a strong logical relation between how many hours a day a student listens to music and their grade, and thus the amount of classes a student attends will likely have a much greater effect on their grade than their exposure to music.

Mean, Median and Mode

Some basic ways of describing data are in the mean, median and mode, which are all a way of describing an “average”. The mean is usually referred to as the average, and is what most people are familiar with. It is taken by summing the values in a data set and then divided by the total number of values in the set.

The median is simply the middle number in a data set that is in chronological order. For example, if the data set is {1,2,4,4,5} the median would be 4. Sometimes the median is a more effective measure than the mean, as the mean can be easily skewed by an “outlier”, or observation (entry) that is substantially different than the bulk of the data (perhaps because of an error).

The mode is the data that is the most “popular”, or appears the most often. So in the above example of a data set, the mode would also be 4.

Ratios, Per Capita and Nominal vs. Real numbers

This point walks hand in hand with putting data in context, as described in Part I. I already brought up ratios in terms of the car accident example in Part I. It is a comparison statistic, giving you the ratio of one number in terms of another to make it easier for you to judge the significance of that number. A type of ratio is a per capita total, which is popular in all sorts of public policy, economic and environmental issues. Per capita just means by or for each individual person—so the emissions of a country per capita are the country’s total emissions divided by their population.

Knowing a per capita total is extremely important, as a number at face value is much less significant without taking factors like population into consideration. For example, say Canada takes on serious environmental initiative to reduce its carbon footprint on the globe, and in five years their total emissions are measured and compared to 2007’s emissions, and are higher. At face value, that makes the initiatives that were taken sound ineffective. However, if the population were to have grown by a third in this time as well, the per capita emissions might actually be less than 2007’s totals, implying that the reduction measures were indeed accurate.

Another set of terminology to keep in mind is nominal numbers versus real. Real numbers are ones that are adjusted to consider other factors: for example, real interest rates are adjusted for inflation, while nominal rates are simply the face values. Again, these can make a large difference in the interpretation of a set of data, especially if you are considering long term gains. For example, an environmental policy that brings $1 000 000 in benefits in ten years from now may actually only be worth $50 000 when considering the inflation over those same ten years.

Margin of Error and Confidence Interval (CI)?

Statisticians should make a model that includes the possibility for various errors in the process measuring and recording the data, as well as in the experiment itself and the assumptions made. This is called the margin of error. This is the acceptable deviation of each observation from the mean/allowance for changing circumstances. It is measured through a confidence interval, which is a process of constructing an interval so that a certain percentage of all data sets (samples taken) will contain the target population value (usually the mean). The confidence level determines this certain percentage, and is most commonly 95% (90% and 99% are other common values.)

The margin of error is important in helping decide how significant a change in data is, as well as how confident you should be in the values as representatives of the true population values. For example, say you are given the statistic that 80% of high school students do not reuse water bottles, with a confidence interval of 72% to 88% and a 95% confidence level. In literal terms, this means that one can say with 95% certainty in any sample of students taken to determine whether students reuse their water bottles or not, the average percent that do not will lie between 72% and 88%, or the margin of error is plus/minus eight percentage points.

Now say the high school in question implements an awareness program to help educate its students about the amount of waste generated by water bottles (which is huge, on a true note!). As a result, the amount of students who do not reuse their water bottles falls from 80% to 74%. While this may seem that the policy was effective, when one considers that 74% was within the original margin of error, it could mean that no real changes occurred.

These points, as well as the intuitions presented in Part I are just a few in helping to properly read and understand information. As Mark Twain once said, “There are lies, damned lies and statistics.” In other words, readers often do not realize how easily a set of data can be manipulated with the smallest of efforts. So it is indeed important as an interpreter to ensure you are fully analyzing the information being presented so when you develop your own judgment, you can do so with an accurate and valid interpretation.

So here are a few things to keep in mind when reading and interpreting statistics. Let’s start with the basics—some important, non-technical questions/points to keep in mind in your initial glance at the data.

What is the background of the data?

Most of the time, experiments are conducted because a certain issue prompted the need to further develop or prove something. Check out the background of the issue:

• What prompted the experiment that gathered the data?
• Who is funding it?
• Does anyone stand to gain from the outcome/interpretation of the data?
• What are the external factors? I will go into more detail about this point in the discussion of causality, but for now think of this example: If pollution data is sampled from a nation with carbon-intensive industries, the data is obviously going to demonstrate high pollution levels. But consider beyond the data—why might the environmental policies be so lax. What are their other options?

Who conducted the research?

All data is created through some type of experiment, and, like anytime you are developing an opinion from someone’s research, it is a good idea to check out who your source is. In the case of statistics, reliable sources are going to be less likely to use deceptive practices when presenting the results.

Another note here is to make sure you are looking at the original data and not someone’s repost and own interpretation of it, like a game of telephone around a campfire, the message (data) can change the more people (interpretations) it is passes through. If you are assessing the original source data, you will find it easier to create your own interpretation of the information. If original data is inaccessible or too raw for interpretation, then you may pose the question of “who interpreted the data” as well as who conducted the research when you consider a secondary source.

How was the data collected?

How data is collected and recorded can make a huge difference on its accuracy. One of the popular methods of collecting data is through surveying or polling; that is, asking specific questions to a sampled group of individuals. It is important to consider how questions were asked, as the survey could contain leading or ineffective questions. A leading question is one that entices the responder towards a certain direction. An ineffective question might be something like inquiring on the years of education a person has versus the degree obtained—inquiring solely on years in school does not take into consideration that an individual may have done school part time, skipped a grade or failed a grade, and therefore may not be an accurate measure of the level of education that individual has.

Hypothetical questions can be difficult to interpret, so it is always important to see how they are worded. An example of a hypothetical question can be found in one of the ways that environmental economists measure how “valuable” the environment is in dollar terms, called contingent valuation. This might be a survey passed around a neighbourhood beside a proposed airport development site, and the hypothetical question posed would be “how much would you be willing to pay to maintain the low level of noise pollution that currently exists.” Without having to make an actual transaction, a person living in that neighbourhood is more likely to overstate what the worth of their peace and quiet would be to them.

The main point here is to analyse the questions yourself and consider how they might affect the response or the data.

Are the data presented in context?

This point goes with the idea of “what were the questions asked”. Anything can sound more exciting than it is when it is taken out of context. It is always important to think of the bigger picture. Say you’re told on the day of an ice storm that there were 25 accidents throughout the day. Only 25? (This is less than a typical day). No problem–the roads must not be that bad then–but thinking beyond the statistic, there are likely less people on the road, so the actual ratio of accidents (number of accidents per car to how many cars are on the road) may be greater than on a regular day, implying that the roads are indeed dangerous.

Also, when given a percentage, always make sure the base of that percentage is given to place it in context. I see this all the time in advertising: “This product is 75% better!” Okay… 75% better than what? Your last product? Your competitor’s product? The recipe your mom uses? Than nothing?

In Michael Moore’s, “Bowling for Columbine”, during the scene where he demonstrated that the trusting Canadians leave their doors unlocked in Toronto, wondered how many doors he tried that were locked before he caught the ones that were not. (Everyone I know that lives in Toronto locks their doors…)

What is the relationship of the data?

Here we address the issue of correlation versus causality. Correlation implies that the variables studied have a relationship of some sort (they are co–related). For example the numbers may rise and fall together. However, just because the data has a relationship does not mean that one causes the other. It could be coincidence, or it could actually be a third factor that causes both other variables to rise. This third factor is called a causal mechanism.

One illustration of this can be seen in analysing an experiment on the effectiveness of firefighters. Say it is found that the more firefighters that are sent in to combat a blaze, the more damage the fire does. Does this mean that firefighters are less effective in larger numbers? Not necessarily. The causal mechanism here is the size of the fire—more fire fighters are going to be sent in for a larger fire, and a larger fire is going to cause more damage.

On an environmental note, causality has been the base of much of the debate surrounding climate change. Basically, if variables are presented as linked through causality, make sure to explore that their causality is explained logically and enough evidence of it is given, and that third factors are considered.

Visual Aids

Be wary of visual aids—graphs of any type can be quickly skewed by changing the scale or range measuring and presenting the data. Consider the two images I created as an example:

Believe it or not they represent the exact same set of data, but with a larger range (0 to 25) the data for each month looks more evenly distributed, whereas when I made the range smaller, the month of May looks massive compared to the other months.

By asking these types of questions, you can begin to critically analyse and understand what the data is proving, if anything. The Walking vs. Driving case study by the Pacific Institute that was summarized and compared recently on TheGreenRocket.com is a great example of this. The researchers at the Pacific Institute did their homework when they were presented data they believed was biased. By thinking in the bigger picture and including more factors, their results were very different than those of environmentalist Chris Goodall.

For an even deeper understanding of statistics, watch out for Thursday’s article on some of the more technical elements of examining data that are important to understand as a reader.

Continue to Part Two >>

Creative Commons Attribution: “Actual is not normal“, Flickr, kevindooley