Coal-Fired Power Plant Final Blog Post

Introduction

This summer, I was tasked with preparing historical institutional and governance aspects related to a study on coal-fired power plants in the United States for Professor Maliniak and Professor Harish. The principal investigator, Yana Jin has found much of the economic data related to these plants and explained that in order to understand the complete story behind coal plants, I need to look at the geopolitical variables as well.

The first and most important distinction we made early on in the process of data collection, was to collect data based on the state and if possible, the county level. The second distinction we made was to collate data going back ten years before the operation of each coal plant. We found this step important because the approval for a coal plant’s construction takes between 5-10 years before a plant is declared operational. These variables included county population, GDP, proximity to state border/water body and gubernatorial election results. We hope these variables will hopefully help explain the reason for the location of coal-fired power plants in the United States.

Problems faced

Although our tasks involved mining data from sources that had previously reported on these statistics we were looking for, there were several impediments to the research.

The more specific we got, the harder it was to find relevant data. It was easy enough to obtain data pertaining to the population and GDP at the county level. However, once we started looking for data as it pertains to US coal production, state wages, and coal prices, we had trouble finding the data at the detailed level we were looking for. Hence, for some of these statistics, we had to ultimately resort to collecting data only as deep as the national level or as wide as 15 years ago.

This problem relates to another problem that we encountered. A lot of the data we searched for could effectively show many negative effects of coal plants, which is exactly why a lot of that could not have been found outside of reports from environmental journals. Plenty of the statistical data have been kept hidden from public knowledge are not found easily as publicly available datasets. The reports that we obtained from environmental journals gave us many other aspects to look at as we conducted our research.

Sociological Context

Looking back on how we prioritized at the beginning of the summer, I realize how naive it was for me not to look into the sociological impacts. We had collected all sorts of data relating to personal finance, coal plant reserves dating back to 1930, but we had not explored a crucial part of the data collection until recently. Some interesting data we found relating to the sociological impacts of coal plants came from these reports. They explored the relationship between race and location of coal-fired power plants. For example, Black Americans are more likely to suffer health effects from air pollution because of where they live. Studies show this group is far more likely to live near power plants and their waste sites than other groups in America. In 2002, nearly 3 out of 4 Black Americans lived in counties that violate federal air pollution standards. 68% of Black Americans lived within 30 miles of a power plant. 30 miles is the distance within which the most harmful effects of the plant emissions are known to take place. Besides air pollution and its harmful effects, a 2002 estimate showed that 1 out of every 3 African Americans are fisherman. Due to the fact that they eat fish more often than other Americans and their proximity to these plants is rather close, they also have a higher exposure to mercury poisoning. Another research paper explains that the dynamic of racism in the United States has created a differentiation between the residences of people of color (POC) and white populations. Many people of color are situated in separated communities that are more disadvantaged than those of the white population because of “white flight”. Since the government and corporations want as little resistance as possible, they tend to pollute near neighborhoods where the people are more socially isolated and politically powerless.

A working paper written in on behalf of the National Center for Environmental Economics, however, states the opposite. They argue that while it is true that plants located near minority neighborhoods are inspected less often, plants located in lower-income areas seem to face more regulatory activity. They also explain that the level of political and community activism relates to race and poverty which may lead to certain environmental inequities around neighborhoods. Plants that violate federal regulations in counties where the voter turnout rate is higher, tend to undergo more regulatory activity. There is a correlation between environmental risk and poverty, but not the relationship we might assume. Instead of arguing that coal-fired plants appear where low-rent apartments are situated, the paper argues that the poor move to higher-pollution neighborhoods because they have low-rent costs. The rent costs are driven lower because of the environmental risk. People who can afford to live further away from these risks are willing to pay more for a greater environmental quality.

During the school year, we intend to collect our own data on sociological impacts of coal-fired power plant emissions. We will attempt to look beyond racial variables when talking about the sociological impact of these plants. The addition of sociological variables was a rather recent development that I thought would be interesting and important to include in my summer research presentation. Some of the ideas we have of incorporating in our research include perceptions of environmental risks based on gender; how that would play across genders as a whole and also gendered responses as they vary by race. Another idea that one of my fellow research assistants suggested, would be to use number of kids in schools who get free lunch to assess poverty levels within a state. This may prove to be difficult though, since every state has their own standards and those standards are subject to change. We hope to use this as an alternate metric.

Economic Context

Most all of the economic review that I collected were industry-related and personal finance statistics. One of the most useful datasets we have broke down each state by an industry ID and GDP for each of those sectors. The downside of this dataset is that it only contains data from 1997 onwards. This problem relates to other aspects of the research as well where we find less wide data as our datasets become more specific in interest. The largest collection of datasets we found, of course, related to population statistics. But the datasets we found that only dated back 10-15 years back, were some of the most eye-opening parts of the economic standpoint of the project.

One of the charts we found listed off state feed-in tariff (FIT) laws. Feed-in tariffs refer to payments given to real estate developers for the amount of electricity they produce as well as by the energy type. It is not a federally mandated bill, thus each state adopts their own laws. They are either based on real estate project costs, utility avoided costs or fixed-price incentives. Understanding these developments in energy policy, help us to understand the role of coal-fired power plants in the present and future. What will be interesting to see is if the coal conglomerates will react to the spread of FIT’s by concentrating their funds in states where coal energy is more widely used or if they try a more difficult route by lobbying politicians to get rid of these tariffs.

Toby Couture (E3 Analytics) and Karlynn Cory (National Renewable Energy Laboratory) argue in their paper that when done right, FIT policies will benefit ratepayers, real estate developers and society at-large. The first reason that they are more cost-effective is because they do not make use of competitive solutions which involve a higher risk for the developer. The other reason is because it reduces the cost of renewable electricity.

There were other datasets that we came across that were more direct in their relationship to coal-fired power plants. An interesting observation made was how coal mining jobs for operators reduced by 27% between 2012 and 2015, and reduced 25% for contractors. In July 2018, President Trump invoked wartime powers to save coal plants, even though this industry has been on the decline since before 2012. With the implementation of policies like FIT in several states already, the collapse of coal energy seems imminent. Aside from jobs at plants decreasing, a 2017 estimate argues that half of the coal jobs are located in only 25 counties in the United States. Quartz reports that these counties are in nine states: AL, IL, IN, KT, NM, PA, VA, WV, and WY. The reason why coal still dominates is because they have control over these mining towns. To put things in perspective, if 20,000 coal workers lost their jobs, 400 people in each of these counties would lose their jobs. The worst part about this entire plan to save the coal industry is that the consumers who get their energy from these plants are the ones to lose. As a result of this invocation of the wartime powers, premiums for electricity are expected to increase significantly.

Additionally, we gathered statistics relating to coal mining as well. One such dataset we had analyzed, broke the coal down by product-type and nominal and real prices. The four types of coal mined in the United States are bituminous, subbituminous, lignite, and anthracite. In order to understand what the different prices mean for each type of coal, we have to understand what their properties are. Bituminous coal, more commonly known as “steam coal,” is the most abundant form of coal. The burning of bituminous coal causes air pollution due to the high sulfur content it releases.  Subbituminous coal is easier to transport and store because of its low water concentration. In addition, its sulfur content is far lower than that of bituminous coal. Since it has a lower calorific value than bituminous coal, it requires a higher amount of coal burned in order to generate the same amount of energy. Although half of the coal in the world is made up of lignite and subbituminous coal, lignite has not been exploited as much as the aforementioned types of coal. It is geologically young and seen inferior to the other two types of coal because of its low calorific value and difficulty in handling and storage. The cost of production for this type of coal is low since it lies on the surface, but its utilization is very difficult due to its high water content. Anthracite contains has the same amount of calorific value as bituminous coal, except it makes up about 2% of all coal reserves in the country, which means it is more expensive as well. Anthracite is not used as much for domestic heating because other sources of renewable energy are more affordable and readily available.

Unrelated to the goal of the project, I decided it would be a good idea to create a database which chronicles campaign contributions from coal-supporting companies. To determine which companies supported coal companies, I looked at all of the operating plants within the United States and found all of their parent companies and used their lobbying data. I created data visualizations and intend to create an interactive data which allows the user to find trends in coal-supporting contributions to congresspeople.

Historical Contexts

The first thing that I noticed (regarding coal prices by coal type) was how the real prices of all types of coal has decreased since 1980. The slide halted in 2008 when President Obama was elected to office. 2008 was the year that renewable energy companies began to increase their lobbying efforts. Many may remember, one of his big campaign promises was to make the switch to renewable energy. During his administration, the use of wind power nearly tripled and solar energy increased by 2500%. Even before that, when Obama ran for office, renewable energy ramped up their lobbying efforts tremendously. In the year 2000, less than 40 alternative energy companies used federal lobbyists. Ten years later, nearly 200 renewable energy companies used federal lobbyists and jointly contributed $30 million. This figure may not seem large in comparison to coal companies’ contributions, but coal companies began to worry about renewable energy and their growing clout amongst lobbyists. Considering that renewable energy had less than $10 million in lobbying efforts in 2006, the 200 percent plus spending increase came as a surprise to all. At first it seems coal-supporting companies did not think much of the competition with renewable energy lobbying. Once Obama was elected and he signed bills like the Omnibus Public Land Management Act of 2009, they had increased their lobbying efforts. In 2012, these coal companies contributed nearly double of their 2010 spending in order to prevent further damage to their industry.

As previously stated, prices of coal had decreased since 1980, before it spiked in 2008. As I searched around for news and other related information that would explain the price change, I realized that worldwide there was a surge in the price of coal. China and Australia were big exporters of coal and both countries were hit by natural disasters that severely damaged their coal production. In the 2008-2009 fiscal year, the price of thermal coal and coking coal doubled, because two of greatest coal exporters were instead buying coal from other countries. The supply and demand issues effectively increased the price of coal for US coal exports. The US coal industry increased the prices for domestic consumption as well, because of the increase in demand for clean energy as well as the clean energy policy passed by Congress during the Obama administration.

 

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