Science, Technology and the Environment
This lecture may
be listened to by clicking the word
It will take several seconds to begin.
A goal of environmental science is to understand the complex interactions of the Earth’s populations and resources and then manage them. We must also use all available informational to understand how our actions may alter natural processes in ways that could make life unsustainable and develop appropriate solutions.
Nature of Science
Science is an attempt to discover order in nature and then use that knowledge to make predictions about what should happen in nature. Science is based on the assumption that there is discoverable order in nature. When scientists encounter a problem or question they collect scientific data, or facts, by making observations and taking measurements. Such facts must be verified or confirmed by repeated observations and measurements, ideally by several different investigators.
The primary goal of science is not facts in and of themselves, but a new idea, principle, or model that connects and explains certain facts and leads to useful predictions about what is happening in nature. Scientists working on a particular problem try to come up with a variety of possible explanations or scientific hypotheses of their (or other scientists) observations in nature. Then experiments are conducted to test the explanations or predictions for each hypothesis. The goal of these tests is to arrive at the most plausible hypothesis. However, although experiments can eliminate (disprove) various hypotheses, they can never prove that any hypothesis is the best (most useful) or the only explanation. All scientists can say is that an explanation is the most useful one at this time.
If many experiments by different scientists support a particular hypothesis, it becomes a scientific theory—a well-tested and widely accepted idea, principle, or model that usually ties together and explains many facts that previously appeared to be unrelated. Converting a scientific hypothesis to a scientific theory is a difficult process, often requiring decades, even hundreds of years. To scientists, theories are not to be taken lightly, for they are ideas or principles stated with a high degree of certainty because they are supported by a great deal of evidence. Some scientific theories are more certain (widely accepted) than others, and they can be revised—or, more rarely, even overthrown—because new data or more useful explanations come to light.
A scientific law is a description of what we find happening in nature over and over in the same way, without known exception. For example, after making thousands of observations and measurements over many decades, scientists discovered what is called the second law of energy or thermodynamics. One simple way of stating this law is that heat always flows spontaneously from hot to cold-something you learned the first time you touched a hot object. Some laws have been refined or even discarded because new or better data, or errors come to light. The more complex the areas of nature scientists study, the more difficult it becomes to discover scientific laws. There are many scientific laws of physics and chemistry, only a few in biology, and even fewer in fields involving complex interactions of multiple factors (variables), such as ecological, climatology (study of climate), and social sciences such as economics and politics.
Scientists can disprove things, but they can never prove anything. To win us over to their particular viewpoint, people often say that something has or has not been “scientifically proven.” They either don’t understand the nature of science, or they are trying to mislead us by falsely implying that science yields absolute proof or certainty. Instead of certainty, scientists speak of degrees of probability. They might predict that if we do a certain thing, then, based on the data, hypotheses, models, theories, and laws underlying the processes involved, there is a high, moderate, or low probability that such and such will happen. The goal of the rigorous scientific process is to reduce the degree of uncertainty as much as possible. However, the more complex the system being studied, the greater the degree of uncertainty or unpredictability. Despite such limitations, science is the best way we have discovered to get reliable knowledge concerning how nature works.
Science accepts nothing on faith. Science is a method by which we attempt to understand nature and how it behaves.
1. Recognize a problem. (Observations)
2. Make an educated guess--a hypothesis--about the answer.
3. Predict the consequences of the hypothesis. (Models)
4. Perform experiments to test predictions.
5. Formulate the simplest general rule that organizes the three main ingredients: hypothesis, predictions, experimental outcome.
If the experiments or observations do not support a model or hypothesis, its proponents must be ready to modify it or let it go, no matter how fond of it they may have grown. This is very important! Science is self-correcting.
The scientific method is something that most of us utilize informally, for
problem solving, in our everyday lives. Example: I am driving down
the expressway when all of the sudden my car begins to chug chug
cough…..stops running. As I sit on the side of the expressway I realize I have
a problem I want to solve (I have made an observation). Then I think
about what may be the problem. I notice that the gas gauge is reading empty. My
educated guess (hypothesis) is that the car is out of gas if the gas
gauge is working. My prediction (model) is that if I walk to the gas
station and obtain a gallon of gas, return to the car, pour the gas into the
tank, and finally turn the key in the ignition the car will start. If the car
does not start, then I might assume that the gas gauge is not working and there
was already gas in the tank. Next, I perform a test (experiment) to see if my
model is accurate. I actually get the gas and pour it into the gas tank. If the
car starts then I yell
In science, a fact is generally a close agreement by competent observers of a series of observations of the same phenomena. A scientific hypothesis, on the other hand, is an educated guess that is only presumed to be factual when demonstrated so by experiment. When hypotheses have been tested over and over again and have not been contradicted, they may become known as laws or principles. A scientific theory is a synthesis of a large body of information that encompasses well-tested and verified hypotheses about certain aspects of the natural world. (In everyday speech a theory is the same thing as a hypothesis. Not true in science.)
Example: "Intelligent life exists on other planets somewhere in the universe." This hypothesis is not scientific. It is a speculation. Although it can be proved correct by the verification of a single instance of intelligent life existing elsewhere in the universe, there is no way to prove it wrong if no life is ever found. If we searched the far reaches of the universe for eons and found no life, we would not prove that it doesn't exist "around the next corner." A scientific hypothesis is testable. It is more important that there be a means of proving it wrong than there be a means of proving it correct. Einstein stated, "No number of experiments can prove me right; a single experiment can prove me wrong."
Scientific laws are the rules of nature. The same laws apply everywhere in the universe. For example gravity: the rules that govern the behavior of gravity on Earth are the same rules that determine the motion of two stars in a very far away system. Gravity behaves the same everywhere in the universe.
Technology is the creation of new products and processes intended to improve our efficiency, our chances for survival, our comfort level, and our quality of life. The goal of science is to develop widely accepted knowledge or ideas, which are intangible; by contrast, technology is concerned primarily with the development of tangible things. In many cases, technology develops from known scientific laws and theories. Scientists invented the laser, for example, by applying knowledge about the internal structure of atoms. Applied scientific knowledge about chemistry has given us nylon, pesticides, laundry detergents, pollution control devices, and countless other products. Applications of theories in nuclear physics led to nuclear bombs and nuclear power plants.
Many technologies arise by trial and error, before anyone understands the underlying scientific principles. For example, aspirin, extracted from the bark of a tropical willow tree, relieved pain and fever long before anyone found out how it did so. Similarly, photography was invented by people who had no inkling of its chemistry, and farmers crossbred new strains of crops and livestock long before biologists understood the principles of genetics. In fact, much of science is an attempt to understand and explain why various technologies work.
Although some forms of technology use scientific knowledge, nearly all science needs technology. Scientists use machines and instruments to collect and analyze data, to perform experiments, and to make complex computations. Scientists would be hard-pressed to get along without such things as paper, pencils, test tubes, oscilloscopes, computers, books, copiers, and telephones—all products of technology.
Although science and technology share similar processes (both are essentially trial and error), they usually differ in the way the ideas and information they produce are shared. Many of the results of scientific research are published and distributed freely to be tested, challenged, verified, or modified. This process strengthens the validity of scientific knowledge and helps expose cheaters.
In contrast, many technological discoveries are kept secret until the new process or product is patented. Information concerning much valuable technology is never published, but is instead learned “on the job” by industrial workers and passed informally among selected individuals only. The basis of other technology gets published in journals and enjoys the same kind of public distribution and peer review as science.
Because technology forms the basis of new products, society utilizes technology on a wide-spread basis before the impact of the science supporting the new technology is clearly understood. This means that we will not know the full impact of this new technology until years after the technology is introduced.
Environmental science is the study of how we and other species interact with one another and with the nonliving environment (matter and energy). It is a science that integrates knowledge from a wide range of disciplines, including physics, chemistry, biology, ecology, geology, geography, resource technology and engineering, resource conservation and management, economics, politics, sociology, psychology, ethics, and, demography (the study of population dynamics). In other words, it is a study of how all the parts of nature and human societies operate and interact truly a study of connections and interactions.
There is intense controversy over the knowledge provided by environmental science. One limitation involves arguments over the validity of data. There is no way to measure accurately how many metric tons of soil are eroded worldwide, how many hectares of tropical forest are cut, how many species become extinct, or how many metric tons of certain pollutants are emitted into the atmosphere or aquatic systems each year. We may legitimately argue over the numbers, but the point environmental scientists want to make is that the trends in these phenomena are significant enough to be evaluated and addressed. The data should not be dismissed because they are “only estimates,” which are all we can ever have. Another limitation is that most environmental problems involve so many variables and such complex interactions that we don’t have enough information or sufficiently sophisticated models to aid in understanding them very well. We still know much too little about how Earth works, its current state of environmental health, and the effects of our activities on Earth’s life support systems.
These limitations provide proponents of any proposed course of action (or inaction) on an environmental problem a justification for their beliefs. This can cause confusion and controversy among the general public and their elected officials and can lead to either excessive or inadequate government regulation. It can also lead to the paralysis by analysis trap, which insists that we fully understand an environmental problem before taking any action—an impossible dream because of the inherent limitations of science and the complexities of environmental problems.
Because environmental problems won’t go away at some point we must evaluate the available information and make political and economic decisions about what to do. This is why people with different worldviews and values can take the same information, come to completely different conclusions, and still be logically consistent.
Living in the world allows us to try new things. Trying everything out to see whether or how it works is time-consuming, expensive, and sometimes dangerous. Over time, people have learned the value of using models to serve as an approximate representation or simulation of a real system and to help us to find out which ideas work. Among the many kinds of models, people use the following:
· mental models to perceive the world, control their bodies, and think about things.
· conceptual models to describe the general relationships among the components of a system.
· graphic models to compile and display data in meaningful patterns. A map is an example of a graphic model.
· physical models (miniature versions of large systems) to try out designs and ideas. Examples are scale models of airplanes, buildings, and landscapes.
models, which consist of one or more mathematical equations to describe the behavior
of a system. People use such models to predict, on paper or in a computer, the
results of experiments or designs.
Mental models guide our perceptions and help us make predictions. Most of our mental models are built into the structure of our nervous systems, and we are usually unaware of them. We interpret the world not according to direct knowledge of reality, but according to mental models, which people often mistake for reality. For example, we all share a built in mental model that the world is continuous, unless our eyes tell us differently. Some mental models are built in; some we learn or make up. For example, most people believe that the automobiles driving down the street will not veer onto the sidewalk and hit pedestrians. If we believed otherwise, we would act like frightened squirrels, always stopping to look around and proceeding only if no automobiles were operating nearby We use mental models of our surroundings to perceive what we believe to be true and to predict what may happen. These mental models apply to our surroundings—we have mental models of objects, of the environment, and of other people—and to our own capabilities and tendencies.
Mathematical Models are some of the most powerful technologies invented by humans. We use mathematical models to supplement mental models. Mathematical models are equations that help us perceive and predict various things. For example, equations that describe the movement of air, moisture, and heat in the atmosphere are used to predict the weather for the next few days. The big difference between mental models and mathematical models is the way we get information from them. To get a perception or prediction from a mental model, we need only think; we get estimates or predictions from such models without even being conscious of them. In contrast, to get a prediction from a mathematical model, we must do some calculating — perhaps in our head, perhaps with pencil and paper, perhaps with a computer. Like mental models, mathematical models are imperfect approximations of reality Some are very good approximations, and some are not. Put another way these “laws” tend to make predictions ranging from fairly accurate to very accurate, depending on the law and the accuracy of the data used to formulate the law.
Mathematical models are important because they can give us improved perceptions and predictions, especially concerning matters for which our mental models are weak. Research has shown that people’s mental models tend to be especially unreliable when there are many interacting variables, when we attempt to extrapolate from too few experiences to a general case, when consequences follow actions only after long delays, when the consequences of actions lead to other consequences, when responses are especially variable from one time to the next, and when controlled experiments are impossible, too slow, or too expensive to conduct. Under such conditions, a good mathematical model can do better than most mental models.
The basic process for developing mathematical models is essentially trial and error. Making a mathematical model usually requires going many times through the same three familiar steps: (1) make a guess—write down some equations; (2) compute the predictions implied by the equations; and (3) compare the predictions with observations and with the predictions of mental models.. If our first guess doesn’t conform to all the evidence and thought experiments we and other investigators can think of, then we either modify the first guess or try another approach. Developing a good model can seem like an endless treadmill. Sometimes it takes years to come up with a mathematical model that passes enough tests to be sufficiently credible or useful.
Models are tested and judged by comparing their predictions or explanations with three kinds of knowledge:
· Inherited knowledge — is the mental models found naturally in our brains. People find it difficult to believe models that conflict with reproductive drive or other basic instincts. Billions of years of evolutionary history compel us to make babies, and mathematical models that say we need to slow or stop population growth meet a lot of emotional resistance from our built-in mental models.
· Experience — includes direct personal memories, written and oral history, previous theories and mathematical models, and any mental models built up from them. One powerful form of experience is the controlled experiment, but many aspects of the environment do not render it suitable for controlled experiments.
· Imagination — is not only thought-experiments (like Einstein’s), but also information that is unconsciously supplied by our nervous systems, such as the filling in of our blind spots and the filling in of incomplete memories.
System Models describe any system being studied or modeled and has one or more inputs of things such as matter, energy, or information. Inputs flow through a system at a certain rate. Such flows or throughputs of matter, energy, or information through a system are represented by arrows. Forms of matter, energy, or information flowing out of a system are called outputs and end up in sinks in the environment. Examples of such sinks are the atmosphere, bodies of water, underground water, soil, and land surfaces.
A feedback loop occurs when one change leads to some other change, which then eventually either reinforces or slows the original change. Feedback loops determine how things happen over time. Feedback loops occur when an output of matter, energy, or information is fed back into the system as an input. For example, recycling aluminum cans involves melting aluminum feeding it back into an economic system to new aluminum products. This feedback loop of materials reduces the need to find, extract, and process virgin aluminum ore. It also reduces the flow of waste matter (discarded aluminum cans into the environment.
After building and testing a model, the model can be applied and used to predict what should happen under a variety of alternative conditions. In effect, we use a mathematical model to answer “If - Then” questions: “If we do such and such, then what is likely to happen now and in the future?” We can also vary the model to test its sensitivity to certain variables. A good model can be used as a substitute natural environment in which we can safely try many different courses of action many times, looking for those that might ensure our future with minimum risk. Using mathematical models of some aspect of the environment, we can gain the equivalent of hundreds or thousands of years of experience in a few weeks or months. Just as pilots can use an aircraft simulator to gain experience without risking their lives, environmental scientists or concerned citizens can use computer models to safely try out different strategies for solving environmental problems.