Effect of Coal Production on Chronic Respiratory Disease Mortality Rates in Kentucky Between 1980 and 2016

Effect of Coal Production on Chronic Respiratory Disease Mortality Rates in Kentucky Between 1980 and 2016 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effect of Coal Production on Chronic Respiratory Disease Mortality Rates in Kentucky Between 1980 and 2016 Aayush Gandhi This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7558480/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Current public health literature has strongly associated coal mining with negative health and environmental impacts, ranging from chronic conditions to air pollution. However, existing research hasn’t explored the effects of coal mining on specific health indicators over an extended period (> 30 years) at the county level, data otherwise necessary to support policymakers when designing effective energy policies to transition away from coal. My paper aimed to bridge this gap by creating a Panel Vector Autoregression model and subsequent impulse response functions in R to model the impact of increases in coal production on chronic respiratory mortality rates in Kentucky. My findings suggest that increased coal production may exacerbate chronic respiratory illnesses in the short term (within 2 years), after which respiratory mortality rates plateau, possibly due to other factors (e.g., genetics or inadequate health insurance). These findings underscore the need for policymakers to expedite the transition away from coal and refrain from permitting the construction of new mines or facilities that would increase coal production. Furthermore, my model’s strong impulse response functions lay the foundation for modeling the short and long-term health effects of changes in coal production, thereby assisting legislators and researchers in designing similar models for energy policy proposals. coal production chronic respiratory disease vector autoregression policy simulation Kentucky Figures Figure 1 Figure 2 1. Introduction Over the past two decades, coal production and electricity generation in the U.S. have significantly declined [32, 35, 36]. However, despite these national trends, Kentucky remains heavily reliant on coal. In 2023, 69% of the state’s electricity came from coal, and production exceeded 28 million short tons [28, 33]. While coal might inevitably cease to be a significant fuel source in Kentucky’s future, the path to that looks to be years, if not decades, away. Considering this, policymakers and scientists will need information on the environmental and health risks posed by continued dependence on coal to make informed decisions about transitioning away. Previous systematic reviews of public health literature have found considerable evidence linking coal mining, including the production, processing, and transportation of coal, to health issues such as chronic conditions, reproductive abnormalities, and other diseases, as well as environmental hazards such as air pollution and water contamination [17, 19, 20]. My review reached similar conclusions: many studies found that mortality and rates of disease incidence are significantly higher in communities near mines or harsh mining practices such as mountaintop removal [2, 3, 9-11, 13, 14, 18, 21]. While this association has been firmly established, there are few studies correlating the effects of coal production on health over a more extended period (>15 years) and modeling the impact of shocks on coal production on health outcomes. Rather most studies have focused on short intervals (e.g. Flunker, et al., 2024 [14]) or used historical data without measuring the lagged and timed effects of coal production over time (e.g. Harrison, et al., 2022 [18]). However, this information is necessary to support simulations on specific energy policies and arguments, if they exist, for an accelerated transition away from coal. To bridge this gap, I investigated the relationship between coal production and chronic respiratory disease mortality rates over time in Kentucky through a quantitative analysis. I hypothesized that increases in coal production will have a negative effect on chronic respiratory disease mortality rates in the long run, as exposure to contaminants is a slow and painful process [16]. In the short run, I also hypothesized that there might be slight increases to respiratory mortality rates in response to greater coal production, as increased air and water contaminants can exacerbate an individual’s existing medical conditions [24]. 2. Results 2.1 Background Results There was a total of 4033 unique cross-sectional data points, identified by their “County_ID” (range: 1-120) and “Year_ID” (range: 1-37) which correlated to specific counties in Kentucky and exact years between 1980 and 2016. Specific entries with missing chronic respiratory and/or population data were excluded from the panel Vector Auto-Regression model. The endogenous variables in the dataset (coal production and respiratory disease rates) passed the Augmented Dickey-Fuller Test for Stationarity at every lag order 1 to 3 (Table 1a & 1b). Per the MMSC test, as described in the later “Materials and Methods” section, the lag order was determined to be 3 (Table 2). The panel VAR model also passed the stability condition as all the eigenvalues were inside the unit circle (Table 3 and Figure 1). Table 1a. Augmented Dickey-Fuller Test for Coal Production Lag Order Dickey-Fuller P-Value Alternative Hypothesis 1 -10.353 <0.01 Stationary 2 -9.8838 <0.01 Stationary 3 -9.4427 <0.01 Stationary Table 1b. Augmented Dickey-Fuller Test for Respiratory Disease Rates Lag Order Dickey-Fuller P-Value Alternative Hypothesis 1 -21.667 <0.01 Stationary 2 -19.271 <0.01 Stationary 3 -17.474 <0.01 Stationary Table 2. MMSC Test for Lag Order Lag Order MMSC_BIC MMSC_AIC MMSC_HQIC 1 31778.73 31864.91 31831.21 2 26452.09 26525.18 26496.5 3 21665.25 21725.52 21701.78 Table 3. Stability Condition Test Eigenvalue Modulus 1 0.6189253216+0.00000000i 0.6189253216 2 -0.2508522132+0.35904115i 0.4379924422 3 -0.2508522132-0.35904115i 0.4379924422 2.2 Panel Vector Autoregression (PVAR) Results Below is the raw output of the Panel VAR model (Table 4). Based on community consensus, however, the IRFs will be used for determining the association between coal and health. Table 4: Panel Vector Autoregression (PVAR) Output. Respiratory Disease Rates Coal Production lag1: Respiratory Disease Rates 0.0658 (0.2905) 0.0571 (1.0820) lag1: Coal Production 0.0592** (0.0215) 0.0514 (0.1366) lag2: Respiratory Disease Rates 0.0655 (0.2721) 0.0569 (0.9542) lag2: Coal Production 0.0613 (0.0741) 0.0532 (0.2419) lag3: Respiratory Disease Rates 0.0652 (1.4708) 0.0565 (6.4198) lag3: Coal Production 0.0617 (0.1029) 0.0536 (0.3347) Population -0.0000 0.0000 -0.0000** (0.0000) Education 0.1515*** (0.0457) 0.1315 (0.2125) PCPI 0.1383 (0.7927) 0.1201 (3.8932) const 0.0140 (1.4615) 0.0122 (7.5869) *** p < 0.001; ** p < 0.01; * p < 0.05 2.3 Impulse Response Functions (IRFs) Results Per the impulse response function, the following were the results (Figure 2): · Increases in coal production have an initial negative effect on coal production until year 2, after which coal production levels out. · Increases in coal production have an initial positive effect on respiratory disease rates until year 2, after which the health indicator plateaus. · Increases in respiratory disease rates have an initial positive effect on coal production until year 2, after which coal production plateaus. · Increases in respiratory disease rates have an initial negative effect on respiratory disease rates until year 2, after which the health indicator levels out. 3. Discussion My study evaluated all 120 counties in Kentucky from 1980 to 2016 to determine the lagged and real association between coal mining and respiratory disease, revealing an important finding. Coal production increases respiratory disease mortality rates, particularly in the short term; this suggests that initial exposure to coal exacerbates existing conditions (e.g., lung disease) or causes new symptoms (e.g., asthma) to develop within populations. In the long run, other factors—such as smoking, genetics, net worth, and health insurance—might play a bigger role in determining respiratory disease risk, decreasing the long-term effect of increased coal production years earlier. The other three impulse response functions were not interpreted since the respective relationships were not statistically significant per the raw panel VAR output. Overall, these findings confirm my hypothesis about coal’s short-term impacts, but not about its long-term effects. Before proceeding, I must address certain limitations. First, since I focused specifically on counties in Kentucky, the results of this study likely cannot be generalized to other major coal producing regions in the U.S. such as Wyoming, West Virginia, Pennsylvania, and Illinois [ 37 ]. Second, due to the lack of county-level data tracing back to the 1980s, my model didn’t include certain environmental and lifestyle exogenous variables (e.g., smoking and air quality), which may have negatively affected the accuracy of the results. To alleviate this issue, I incorporated available socioeconomic data (e.g., high school diploma rates and per capita personal income). Prior studies have confirmed that education and income are associated with lifestyle choices (i.e. obesity, smoking, etcetera), which can indirectly control for some of those variables [ 7 , 8 , 12 , 15 , 23 ]. Furthermore, since coal is a big cause, even if not entirely, of air quality issues in the state, even that variable can be indirectly controlled for [ 26 ]. Lastly, some studies, e.g. Betz, et al., 2015 [ 6 ], proposed using coal employment data, noting that coal production data might not accurately reflect coal intensity due to rising coal productivity which means inevitable production increases over time or different mining techniques by region. However, for my analysis, it was hard to find specific coal employment data for individual counties spanning back to 1980, so I opted for coal production instead. Notwithstanding these limitations, my paper has several strengths. To my knowledge, it is the first to examine the association between coal mining and respiratory disease over a long time (> 30 years) at the county-level while controlling for specific exogenous variables. While my paper confirmed the results of existing studies, for the first time in the literature, strong impulse response functions (IRFs) were created. In the context of this paper, these functions are specifically valuable for policymakers, interest groups, and scientists to understand the short and long-term impacts of changes to coal production on community health and help warrant future research, utilizing IRFs, on specific coal policy proposals. My study aimed to address a gap within the existing academic space. That said, future research will still be valuable to strengthen energy policy. New studies should utilize the similar panel VAR data but extend the model to include other coal regions and incorporate variables relating to socio-economic factors, as previously explained. Additional research on specific energy policies (e.g., carbon taxes, federal coal subsidies, coal electricity generation) should be rigorously explored as well to similarly assist policymakers. 4. Conclusion This study aimed to model the effects of changes in coal mining on chronic respiratory disease mortality rates, over a long period based on county-level data. My findings indicate that increased coal production exacerbates respiratory illness in the short term but in the long-run, respiratory mortality rates plateau, possibly due to other factors (e.g., genetics). By extending current public health research to incorporate longer periods and account for lagged effects of coal while simultaneously laying the foundation for the use of impulse response functions to model changes in coal production, I provide a fresh perspective to assist academic researchers, policymakers, and interest groups in policy design related to coal. 5. Materials and Methods 5.1 Data Collection In this study, county-level panel data was used to control for specific local factors, as well as to provide more data points, thereby increasing the reliability of the results. Furthermore, the data spanned from 1980 to 2016, providing a sufficiently large time interval to measure the specific impacts of coal on health over time. My endogenous variables were coal production, measured in short tons, and chronic respiratory disease mortality rates, measured in a crude rate. For my analysis, coal production was used as the measure for coal intensity. Chronic respiratory disease mortality rates were used as the health indicator as I hypothesized that coal production likely has the greatest health impact on the respiratory system. Total population, per-capita personal income, and high school education rates were included as other variables to control for specific socio-economic determinants of health. To begin, coal production was initially aggregated through the Kentucky Geological Survey’s online database and filtered for the period from 1980 to 2016 for each individual county (120 counties total) [ 34 ]. Since counties in Kentucky that historically have not produced coal were not in the database, I assumed coal production was zero for counties with no records available. Similarly, chronic respiratory disease mortality rates were collected from the CDC Wonder online database and separated by county and year [ 29 ]. However, the data was split between 1980–1998 and 1999–2016 due to changes in ICD Codes. It is essential to note that slight differences in disease classification may have negatively impacted the reliability of the results. Many of the crude rates were also labeled as “unreliable” by the CDC or were missing and hence deleted, which similarly affected the study’s conclusion. The data for the total county population was collected from the same database as the chronic respiratory disease mortality rates. Per-capita personal income (PCPI) data was aggregated from the Federal Reserve Bank of St. Louis’s FRED database [ 30 ]. High school education rates were gathered from the USDA Economic Research Service’s county-level datasets on education [ 31 ]. Since the data was not available in annual intervals, I applied the 1980 value for the period 1980–1989, the 1990 value for 1990–1999, the 2000 value for 2000–2007, and the 2008 value for 2008–2016. This process likely undermines the use of education rates as a strong exogenous variable, negatively affecting the reliability of the results. 5.2 Data Analysis To determine the association between the variables, I created and used a Panel Vector Autoregression (PVAR) model in RStudio. This model was selected since it allows us to determine the relationship between coal production and chronic respiratory disease mortality rates, specifically over time across multiple counties. Additionally, changes to coal production may have delayed effects on specific health indicators, which the PVAR model is able to test for. On the data side, coal production, chronic respiratory mortality rates, and per-capita personal income underwent a logarithm transformation due to specific errors relating to failed stability condition tests, an inability to visualize impulse response functions, and other reasons. Furthermore, from this point onwards, I will refer to the “log of coal production” and “log of chronic respiratory disease mortality rates” simply as coal production and respiratory disease rates. To design the model itself, I conducted the following tests and transformations. Through the MMSC test proposed by Andrews, et al., 2001 [ 4 ], I determined the optimal lag number by measuring the MMSC_BIC, MMSC_AIC, and MMSC_HQIC values at lag orders 1, 2, and 3. As suggested by Abrigo, et al., 2016 [ 1 ], in a dataset with missing data at certain years, a first difference (FD) transformation is not suitable. Since my dataset has missing chronic respiratory data for certain counties, a forward orthogonal deviation (FOD) transformation was used instead. Due to frequent RStudio errors relating to processing capacity when performing a two-step GMM model, I opted for a “one- step” model instead. In reality, the differences between the two GMM estimators are negligible (J. Eloriga, personal communication, 2025). Other standard panel VAR tests, as determined by Yang, et al., 2023 [ 27 ] were performed before interpreting the results: a unit root test for stationarity, impulse response analysis, and stability condition tests. Declarations 6.1 Ethics Statement Not applicable. 6.2 Consent for Publication Not applicable. 6.3 Availability of data and materials The exact databases from where I aggregated the data are described in the Methodology section of the paper and can be found in the references. Furthermore, the code used to construct the statistical model for this research is available at https://doi.org/10.5281/zenodo.15858671. The aggregated dataset supporting the conclusions of this article and research is available at https://doi.org/10.5281/zenodo.15858745. Please note that the Mortality Data on CDC Wonder database (e.g., Chronic Respiratory) has data use restrictions which must be abided by and can be found through the reference links. 6.4 Competing Interests The author declares that they have no competing interests. 6.5 Funding The author has not received any funding. 6.6 Authors’ contributions AG was the sole author for this paper. AG researched the literature base, collected the relevant data from various sources, coded the PVAR model in RStudio, interpreted the results, and wrote the entire manuscript. 6.7 Acknowledgements I would like to thank Professor Lukas Althoff, Stanford University for his mentorship and guidance to me with my research. References Abrigo MRM, Love I. Estimation of Panel Vector Autoregression in Stata. Stata J. 2016;16(3):778–804. Ahern M, Hendryx M. Cancer Mortality Rates in Appalachian Mountaintop Coal Mining Areas. J Environ Occup Health. 2012;1(2):63–63. Ahern M, Mullett M, MacKay K, Hamilton C. Residence in Coal-Mining Areas and Low-Birth-Weight Outcomes. Matern Child Health J. 2011;15(7):974–9. Andrews DWK, Lu B. Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models. J Econ. 2001;101(1):123–64. Arel-Bundock V. modelsummary: Data and Model Summaries in R. J Stat Softw. 2022;103:1–23. Betz MR, Partridge MD, Farren M, Lobao L. Coal mining, economic development, and the natural resources curse. Energy Econ. 2015;50:105–16. Cao P, Jeon J, Tam J, Fleischer NL, Levy DT, Holford TR, et al. Smoking Disparities by Level of Educational Attainment and Birth Cohort in the U.S. Am J Prev Med. 2023;64(4):S22–31. Casetta B, Videla AJ, Bardach A, Morello P, Soto N, Lee K, et al. Association Between Cigarette Smoking Prevalence and Income Level: A Systematic Review and Meta-Analysis. Nicotine Tob Res. 2017;19(12):1401–7. Christian WJ, Huang B, Rinehart J, Hopenhayn C. Exploring Geographic Variation in Lung Cancer Incidence in Kentucky Using a Spatial Scan Statistic: Elevated Risk in the Appalachian Coal-Mining Region. Public Health Rep. 2011;126(6):789–96. Cortes-Ramirez J, Naish S, Sly PD, Jagals P. Mortality and morbidity in populations in the vicinity of coal mining: a systematic review. BMC Public Health. 2018;18(1):721. Esch L, Hendryx M. Chronic Cardiovascular Disease Mortality in Mountaintop Mining Areas of Central Appalachian States. J Rural Health. 2011;27(4):350–7. Escobedo LG, Peddicord JP. Smoking prevalence in US birth cohorts: the influence of gender and education. Am J Public Health. 1996;86(2):231–6. Finkelman RB, Orem W, Castranova V, Tatu CA, Belkin HE, Zheng B, et al. Health impacts of coal and coal use: possible solutions. Int J Coal Geol. 2002;50(1):425–43. Flunker JC, Sanderson WT, Christian WJ, Mannino DM, Browning SR. Environmental exposures and pulmonary function among adult residents of rural Appalachian Kentucky. J Expo Sci Environ Epidemiol. 2024;34(6):981–9. Garrett BE. Socioeconomic Differences in Cigarette Smoking Among Sociodemographic Groups. Prev Chronic Dis [Internet]. 2019 [cited 2025 Jun 29];16. Available from: https://www.cdc.gov/pcd/issues/2019/18_0553.htm Gasparotto J, Da Boit Martinello K. Coal as an energy source and its impacts on human health. Energy Geoscience. 2021;2(2):113–20. Gopinathan P, Subramani T, Barbosa S, Yuvaraj D. Environmental impact and health risk assessment due to coal mining and utilization. Environ Geochem Health. 2023;45(10):6915–22. Harrison H. Health Impact of Mountaintop Removal and Federal Air Quality-related Policies: A Study on Respiratory Disease and Lung Cancer Mortality Rates in KY. MPA/MPP/MPFM Capstone Projects [Internet]. 2021; Available from: https://uknowledge.uky.edu/mpampp_etds/364 Hendryx M. The public health impacts of surface coal mining. Extractive Industries Soc. 2015;2(4):820–6. Hendryx M, Zullig KJ, Luo J. Impacts of Coal Use on Health. Annual Review of Public Health. 2020;41(Volume 41, 2020):397–415. Pless-Mulloli T, Howel D, Prince H. Prevalence of asthma and other respiratory symptoms in children living near and away from opencast coal mining sites. Int J Epidemiol. 2001;30(3):556–63. Sigmund M, Ferstl R. Panel vector autoregression in R with the package panelvar. Q Rev Econ Finance. 2021;80:693–720. Silventoinen K, Piirtola M, Jelenkovic A, Sund R, Tarnoki AD, Tarnoki DL, et al. Smoking remains associated with education after controlling for social background and genetic factors in a study of 18 twin cohorts. Sci Rep. 2022;12(1):13148. Thurston GD, Rice MB. Air Pollution Exposure and Asthma Incidence in Children: Demonstrating the Value of Air Quality Standards. JAMA. 2019;321(19):1875–7. Trapletti A. Hornik [aut K, cre, code) BL (BDS test. tseries: Time Series Analysis and Computational Finance [Internet]. 2024 [cited 2025 Jul 10]. Available from: https://cran.r-project.org/web/packages/tseries/index.html Walker E, Payne D. Health Impact Assessment of Coal and Clean Energy Options in Kentucky. Yang R, An X, Chen Y, Yang X. The Knowledge Analysis of Panel Vector Autoregression: A Systematic Review. SAGE Open. 2023;13(4):21582440231215991. Coal Data Browser. - Aggregate coal mine production for all coal [Internet]. [cited 2025 Jun 29]. Available from: https://www.eia.gov/coal/data/browser/ Compressed Mortality File on CDC WONDER [Internet]. [cited 2025 Jun 29]. Available from: https://wonder.cdc.gov/mortsql.html Counties | FRED |. St. Louis Fed [Internet]. [cited 2025 Jun 29]. Available from: https://fred.stlouisfed.org/categories/28340 County-level Data. Sets - Education [Internet]. [cited 2025 Jun 29]. Available from: https://data.ers.usda.gov/reports.aspx?ID=4026 Electricity generation, capacity, and sales in the United States - U.S. Energy Information Administration (EIA) [Internet]. [cited 2025 Jun 29]. Available from: https://www.eia.gov/energyexplained/electricity/electricity-in-the-us-generation-capacity-and-sales.php Kentucky Profile [Internet]. [cited 2025 Jun 29]. Available from: https://www.eia.gov/state/print.php?sid=KY Search the Coal Production Database [Internet]. [cited 2025 Jun 29]. Available from: https://kgs.uky.edu/kgsweb/DataSearching/Coal/Production/prodsearch.asp U.S. production of all types of coal has declined over the past two decades - U.S. Energy Information Administration (EIA) [Internet]. [cited 2025 Jun 29]. Available from: https://www.eia.gov/todayinenergy/detail.php?id=64924 Use of coal - U.S. Energy Information Administration (EIA). [Internet]. [cited 2025 Jun 29]. Available from: https://www.eia.gov/energyexplained/coal/use-of-coal.php Where our coal comes from - U.S. Energy Information Administration (EIA) [Internet]. [cited 2025 Jun 29]. Available from: https://www.eia.gov/energyexplained/coal/where-our-coal-comes-from.php Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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1","display":"","copyAsset":false,"role":"figure","size":52567,"visible":true,"origin":"","legend":"\u003cp\u003eRoots of Companion Matrix\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7558480/v1/d899dae588ed89f9f2dc801d.png"},{"id":92412265,"identity":"521e7f03-17a2-49f3-86c2-6765dca68d3d","added_by":"auto","created_at":"2025-09-29 12:45:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":67581,"visible":true,"origin":"","legend":"\u003cp\u003eGeneralized Impulse Response Function\u003c/p\u003e\n\u003cp\u003e*log_Health_Outcome1 refers to the logarithm transformed “Chronic Respiratory Mortality Rate” variable.\u003cbr\u003e\n \u0026nbsp;*log_Coal_Production refers to the logarithm transformed “Coal Production” variable.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7558480/v1/e13fd05a97e078b56495edf8.png"},{"id":92414353,"identity":"c07b3bed-f9c8-4d49-be17-f0098327b762","added_by":"auto","created_at":"2025-09-29 13:09:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":555080,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7558480/v1/60acc62a-ad5c-43d3-8d1a-320c0ba0f226.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effect of Coal Production on Chronic Respiratory Disease Mortality Rates in Kentucky Between 1980 and 2016","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eOver the past two decades, coal production and electricity generation in the U.S. have significantly declined [32, 35, 36]. However, despite these national trends, Kentucky remains heavily reliant on coal. In 2023, 69% of the state’s electricity came from coal, and production exceeded 28 million short tons [28, 33]. While coal might inevitably cease to be a significant fuel source in Kentucky’s future, the path to that looks to be years, if not decades, away. Considering this, policymakers and scientists will need information on the environmental and health risks posed by continued dependence on coal to make informed decisions about transitioning\u0026nbsp;away.\u003c/p\u003e\n\u003cp\u003ePrevious systematic reviews of public health literature have found considerable evidence linking coal mining, including the production, processing, and transportation of coal, to health issues such as chronic conditions, reproductive abnormalities, and other diseases, as well as environmental hazards such as air pollution and water contamination [17, 19, 20]. My review reached similar conclusions: many studies found that mortality and rates of disease incidence are significantly higher in communities near mines or harsh mining practices such as mountaintop removal [2, 3, 9-11, 13, 14, 18, 21].\u003c/p\u003e\n\u003cp\u003eWhile this association has been firmly established, there are few studies correlating the effects of coal production on health over a more extended period (\u0026gt;15 years) and modeling the impact of shocks on coal production on health outcomes. Rather most studies have focused on short intervals (e.g. Flunker, et al., 2024 [14]) or used historical data without measuring the lagged and timed effects of coal production over time (e.g. Harrison, et al., 2022 [18]). However, this information is necessary to support simulations on specific energy policies and arguments, if they exist, for an accelerated transition away from coal.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo bridge this gap, I investigated the relationship between coal production and chronic respiratory disease mortality rates over time in Kentucky through a quantitative analysis. I hypothesized that increases in coal production will have a negative effect on chronic respiratory disease mortality rates in the long run, as exposure to contaminants is a slow and painful process [16]. In the short run, I also hypothesized that there might be slight increases to respiratory mortality rates in response to greater coal production, as increased air and water contaminants can exacerbate an individual’s existing medical conditions [24].\u003c/p\u003e"},{"header":"2. Results","content":"\u003cp\u003e\u003cem\u003e2.1\u0026nbsp;\u003c/em\u003e\u003cem\u003eBackground Results\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThere was a total of 4033 unique cross-sectional data points, identified by their \u0026ldquo;County_ID\u0026rdquo; (range: 1-120) and \u0026ldquo;Year_ID\u0026rdquo; (range: 1-37) which correlated to specific counties in Kentucky and exact years between 1980 and 2016. Specific entries with missing chronic respiratory and/or population data were excluded from the panel Vector Auto-Regression model.\u003c/p\u003e\n\u003cp\u003eThe endogenous variables in the dataset (coal production and respiratory disease rates) passed the Augmented Dickey-Fuller Test for Stationarity at every lag order 1 to 3 (Table 1a \u0026amp; 1b). Per the MMSC test, as described in the later \u0026ldquo;Materials and Methods\u0026rdquo; section, the lag order was determined to be 3 (Table 2). The panel VAR model also passed the stability condition as all the eigenvalues were inside the unit circle (Table 3 and Figure 1).\u003c/p\u003e\n\u003cp\u003eTable 1a. Augmented Dickey-Fuller Test for Coal Production\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLag Order\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDickey-Fuller\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlternative Hypothesis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e-10.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eStationary\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e-9.8838\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eStationary\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e-9.4427\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eStationary\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 1b. Augmented Dickey-Fuller Test for Respiratory Disease Rates\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLag Order\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDickey-Fuller\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAlternative Hypothesis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e-21.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eStationary\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e-19.271\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eStationary\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e-17.474\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u0026lt;0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003eStationary\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2. MMSC Test for Lag Order\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLag Order\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMMSC_BIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMMSC_AIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMMSC_HQIC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e31778.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e31864.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003e31831.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e26452.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e26525.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003e26496.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 170px;\"\u003e\n \u003cp\u003e21665.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 166px;\"\u003e\n \u003cp\u003e21725.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003e21701.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3. Stability Condition Test\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 336px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEigenvalue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModulus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 336px;\"\u003e\n \u003cp\u003e0.6189253216+0.00000000i\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003e0.6189253216\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 336px;\"\u003e\n \u003cp\u003e-0.2508522132+0.35904115i\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003e0.4379924422\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 336px;\"\u003e\n \u003cp\u003e-0.2508522132-0.35904115i\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 204px;\"\u003e\n \u003cp\u003e0.4379924422\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e2.2\u0026nbsp;\u003c/em\u003e\u003cem\u003ePanel Vector Autoregression (PVAR)\u0026nbsp;Results\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eBelow is the raw output of the Panel VAR model (Table 4). Based on community consensus, however, the IRFs will be used for determining the association between coal and health.\u003c/p\u003e\n\u003cp\u003eTable 4: Panel Vector Autoregression (PVAR) Output.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003eRespiratory Disease Rates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003eCoal Production\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003elag1: Respiratory Disease Rates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e0.0658\u003c/p\u003e\n \u003cp\u003e(0.2905)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e0.0571\u003c/p\u003e\n \u003cp\u003e(1.0820)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003elag1: Coal Production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e0.0592**\u003c/p\u003e\n \u003cp\u003e(0.0215)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e0.0514\u003c/p\u003e\n \u003cp\u003e(0.1366)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003elag2: Respiratory Disease Rates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e0.0655\u003c/p\u003e\n \u003cp\u003e(0.2721)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e0.0569\u003c/p\u003e\n \u003cp\u003e(0.9542)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003elag2: Coal Production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e0.0613\u003c/p\u003e\n \u003cp\u003e(0.0741)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e0.0532\u003c/p\u003e\n \u003cp\u003e(0.2419)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003elag3: Respiratory Disease Rates\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e0.0652\u003c/p\u003e\n \u003cp\u003e(1.4708)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e0.0565\u003c/p\u003e\n \u003cp\u003e(6.4198)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003elag3: Coal Production\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e0.0617\u003c/p\u003e\n \u003cp\u003e(0.1029)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e0.0536\u003c/p\u003e\n \u003cp\u003e(0.3347)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003ePopulation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e-0.0000\u003c/p\u003e\n \u003cp\u003e0.0000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e-0.0000**\u003c/p\u003e\n \u003cp\u003e(0.0000)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e0.1515***\u003c/p\u003e\n \u003cp\u003e(0.0457)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e0.1315\u003c/p\u003e\n \u003cp\u003e(0.2125)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003ePCPI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e0.1383\u003c/p\u003e\n \u003cp\u003e(0.7927)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e0.1201\u003c/p\u003e\n \u003cp\u003e(3.8932)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003econst\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e0.0140\u003c/p\u003e\n \u003cp\u003e(1.4615)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 229px;\"\u003e\n \u003cp\u003e0.0122\u003c/p\u003e\n \u003cp\u003e(7.5869)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e*** p \u0026lt; 0.001; ** p \u0026lt; 0.01; * p \u0026lt; 0.05\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e2.3\u0026nbsp;\u003c/em\u003e\u003cem\u003eImpulse Response Functions (IRFs)\u0026nbsp;Results\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003ePer the impulse response function, the following were the results (Figure 2):\u003c/p\u003e\n\u003cp\u003e\u0026middot; Increases in coal production have an initial negative effect on coal production until year 2, after which coal production levels out.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Increases in coal production have an initial positive effect on respiratory disease rates until year 2, after which the health indicator plateaus.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Increases in respiratory disease rates have an initial positive effect on coal production until year 2, after which coal production plateaus.\u003c/p\u003e\n\u003cp\u003e\u0026middot; Increases in respiratory disease rates have an initial negative effect on respiratory disease rates until year 2, after which the health indicator levels out.\u003c/p\u003e"},{"header":"3. Discussion","content":"\u003cp\u003eMy study evaluated all 120 counties in Kentucky from 1980 to 2016 to determine the lagged and real association between coal mining and respiratory disease, revealing an important finding. Coal production increases respiratory disease mortality rates, particularly in the short term; this suggests that initial exposure to coal exacerbates existing conditions (e.g., lung disease) or causes new symptoms (e.g., asthma) to develop within populations. In the long run, other factors\u0026mdash;such as smoking, genetics, net worth, and health insurance\u0026mdash;might play a bigger role in determining respiratory disease risk, decreasing the long-term effect of increased coal production years earlier. The other three impulse response functions were not interpreted since the respective relationships were not statistically significant per the raw panel VAR output. Overall, these findings confirm my hypothesis about coal\u0026rsquo;s short-term impacts, but not about its long-term effects.\u003c/p\u003e\u003cp\u003eBefore proceeding, I must address certain limitations. First, since I focused specifically on counties in Kentucky, the results of this study likely cannot be generalized to other major coal producing regions in the U.S. such as Wyoming, West Virginia, Pennsylvania, and Illinois [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Second, due to the lack of county-level data tracing back to the 1980s, my model didn\u0026rsquo;t include certain environmental and lifestyle exogenous variables (e.g., smoking and air quality), which may have negatively affected the accuracy of the results. To alleviate this issue, I incorporated available socioeconomic data (e.g., high school diploma rates and per capita personal income). Prior studies have confirmed that education and income are associated with lifestyle choices (i.e. obesity, smoking, etcetera), which can indirectly control for some of those variables [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Furthermore, since coal is a big cause, even if not entirely, of air quality issues in the state, even that variable can be indirectly controlled for [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Lastly, some studies, e.g. Betz, et al., 2015 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], proposed using coal employment data, noting that coal production data might not accurately reflect coal intensity due to rising coal productivity which means inevitable production increases over time or different mining techniques by region. However, for my analysis, it was hard to find specific coal employment data for individual counties spanning back to 1980, so I opted for coal production instead.\u003c/p\u003e\u003cp\u003eNotwithstanding these limitations, my paper has several strengths. To my knowledge, it is the first to examine the association between coal mining and respiratory disease over a long time (\u0026gt;\u0026thinsp;30 years) at the county-level while controlling for specific exogenous variables. While my paper confirmed the results of existing studies, for the first time in the literature, strong impulse response functions (IRFs) were created. In the context of this paper, these functions are specifically valuable for policymakers, interest groups, and scientists to understand the short and long-term impacts of changes to coal production on community health and help warrant future research, utilizing IRFs, on specific coal policy proposals.\u003c/p\u003e\u003cp\u003eMy study aimed to address a gap within the existing academic space. That said, future research will still be valuable to strengthen energy policy. New studies should utilize the similar panel VAR data but extend the model to include other coal regions and incorporate variables relating to socio-economic factors, as previously explained. Additional research on specific energy policies (e.g., carbon taxes, federal coal subsidies, coal electricity generation) should be rigorously explored as well to similarly assist policymakers.\u003c/p\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThis study aimed to model the effects of changes in coal mining on chronic respiratory disease mortality rates, over a long period based on county-level data. My findings indicate that increased coal production exacerbates respiratory illness in the short term but in the long-run, respiratory mortality rates plateau, possibly due to other factors (e.g., genetics). By extending current public health research to incorporate longer periods and account for lagged effects of coal while simultaneously laying the foundation for the use of impulse response functions to model changes in coal production, I provide a fresh perspective to assist academic researchers, policymakers, and interest groups in policy design related to coal.\u003c/p\u003e"},{"header":"5. Materials and Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e5.1 Data Collection\u003c/h2\u003e\u003cp\u003eIn this study, county-level panel data was used to control for specific local factors, as well as to provide more data points, thereby increasing the reliability of the results. Furthermore, the data spanned from 1980 to 2016, providing a sufficiently large time interval to measure the specific impacts of coal on health over time.\u003c/p\u003e\u003cp\u003eMy endogenous variables were coal production, measured in short tons, and chronic respiratory disease mortality rates, measured in a crude rate. For my analysis, coal production was used as the measure for coal intensity. Chronic respiratory disease mortality rates were used as the health indicator as I hypothesized that coal production likely has the greatest health impact on the respiratory system. Total population, per-capita personal income, and high school education rates were included as other variables to control for specific socio-economic determinants of health.\u003c/p\u003e\u003cp\u003eTo begin, coal production was initially aggregated through the Kentucky Geological Survey\u0026rsquo;s online database and filtered for the period from 1980 to 2016 for each individual county (120 counties total) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Since counties in Kentucky that historically have not produced coal were not in the database, I assumed coal production was zero for counties with no records available. Similarly, chronic respiratory disease mortality rates were collected from the CDC Wonder online database and separated by county and year [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. However, the data was split between 1980\u0026ndash;1998 and 1999\u0026ndash;2016 due to changes in ICD Codes. It is essential to note that slight differences in disease classification may have negatively impacted the reliability of the results. Many of the crude rates were also labeled as \u0026ldquo;unreliable\u0026rdquo; by the CDC or were missing and hence deleted, which similarly affected the study\u0026rsquo;s conclusion.\u003c/p\u003e\u003cp\u003eThe data for the total county population was collected from the same database as the chronic respiratory disease mortality rates. Per-capita personal income (PCPI) data was aggregated from the Federal Reserve Bank of St. Louis\u0026rsquo;s FRED database [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. High school education rates were gathered from the USDA Economic Research Service\u0026rsquo;s county-level datasets on education [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Since the data was not available in annual intervals, I applied the 1980 value for the period 1980\u0026ndash;1989, the 1990 value for 1990\u0026ndash;1999, the 2000 value for 2000\u0026ndash;2007, and the 2008 value for 2008\u0026ndash;2016. This process likely undermines the use of education rates as a strong exogenous variable, negatively affecting the reliability of the results.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e5.2 Data Analysis\u003c/h2\u003e\u003cp\u003eTo determine the association between the variables, I created and used a Panel Vector Autoregression (PVAR) model in RStudio. This model was selected since it allows us to determine the relationship between coal production and chronic respiratory disease mortality rates, specifically over time across multiple counties. Additionally, changes to coal production may have delayed effects on specific health indicators, which the PVAR model is able to test for.\u003c/p\u003e\u003cp\u003eOn the data side, coal production, chronic respiratory mortality rates, and per-capita personal income underwent a logarithm transformation due to specific errors relating to failed stability condition tests, an inability to visualize impulse response functions, and other reasons. Furthermore, from this point onwards, I will refer to the \u0026ldquo;log of coal production\u0026rdquo; and \u0026ldquo;log of chronic respiratory disease mortality rates\u0026rdquo; simply as coal production and respiratory disease rates.\u003c/p\u003e\u003cp\u003eTo design the model itself, I conducted the following tests and transformations.\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003eThrough the MMSC test proposed by Andrews, et al., 2001 [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], I determined the optimal lag number by measuring the MMSC_BIC, MMSC_AIC, and MMSC_HQIC values at lag orders 1, 2, and 3.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eAs suggested by Abrigo, et al., 2016 [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], in a dataset with missing data at certain years, a first difference (FD) transformation is not suitable. Since my dataset has missing chronic respiratory data for certain counties, a forward orthogonal deviation (FOD) transformation was used instead.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eDue to frequent RStudio errors relating to processing capacity when performing a two-step GMM model, I opted for a \u0026ldquo;one- step\u0026rdquo; model instead. In reality, the differences between the two GMM estimators are negligible (J. Eloriga, personal communication, 2025).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003eOther standard panel VAR tests, as determined by Yang, et al., 2023 [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] were performed before interpreting the results: a unit root test for stationarity, impulse response analysis, and stability condition tests.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003e6.1\u0026nbsp;\u003c/em\u003e\u003cem\u003eEthics Statement\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e6.2\u0026nbsp;\u003c/em\u003e\u003cem\u003eConsent for Publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e6.3\u0026nbsp;\u003c/em\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe exact databases from where I aggregated the data are described in the Methodology section of the paper and can be found in the references. Furthermore, the code used to construct the statistical model for this research is available at https://doi.org/10.5281/zenodo.15858671. The aggregated dataset supporting the conclusions of this article and research is available at https://doi.org/10.5281/zenodo.15858745. Please note that the Mortality Data on CDC Wonder database (e.g., Chronic Respiratory) has data use restrictions which must be abided by and can be found through the reference links.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e6.4\u0026nbsp;\u003c/em\u003e\u003cem\u003eCompeting Interests\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e6.5\u0026nbsp;\u003c/em\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe author has not received any funding.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e6.6\u0026nbsp;\u003c/em\u003e\u003cem\u003eAuthors\u0026rsquo; contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAG was the sole author for this paper. AG researched the literature base, collected the relevant data from various sources, coded the PVAR model in RStudio, interpreted the results, and wrote the entire manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e6.7\u0026nbsp;\u003c/em\u003e\u003cem\u003eAcknowledgements\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eI would like to thank Professor Lukas Althoff, Stanford University for his mentorship and guidance to me with my research.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbrigo MRM, Love I. Estimation of Panel Vector Autoregression in Stata. Stata J. 2016;16(3):778\u0026ndash;804.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAhern M, Hendryx M. Cancer Mortality Rates in Appalachian Mountaintop Coal Mining Areas. J Environ Occup Health. 2012;1(2):63\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAhern M, Mullett M, MacKay K, Hamilton C. Residence in Coal-Mining Areas and Low-Birth-Weight Outcomes. Matern Child Health J. 2011;15(7):974\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAndrews DWK, Lu B. Consistent model and moment selection procedures for GMM estimation with application to dynamic panel data models. J Econ. 2001;101(1):123\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eArel-Bundock V. modelsummary: Data and Model Summaries in R. J Stat Softw. 2022;103:1\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBetz MR, Partridge MD, Farren M, Lobao L. Coal mining, economic development, and the natural resources curse. Energy Econ. 2015;50:105\u0026ndash;16.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCao P, Jeon J, Tam J, Fleischer NL, Levy DT, Holford TR, et al. Smoking Disparities by Level of Educational Attainment and Birth Cohort in the U.S. Am J Prev Med. 2023;64(4):S22\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCasetta B, Videla AJ, Bardach A, Morello P, Soto N, Lee K, et al. Association Between Cigarette Smoking Prevalence and Income Level: A Systematic Review and Meta-Analysis. Nicotine Tob Res. 2017;19(12):1401\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChristian WJ, Huang B, Rinehart J, Hopenhayn C. Exploring Geographic Variation in Lung Cancer Incidence in Kentucky Using a Spatial Scan Statistic: Elevated Risk in the Appalachian Coal-Mining Region. Public Health Rep. 2011;126(6):789\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCortes-Ramirez J, Naish S, Sly PD, Jagals P. Mortality and morbidity in populations in the vicinity of coal mining: a systematic review. BMC Public Health. 2018;18(1):721.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEsch L, Hendryx M. Chronic Cardiovascular Disease Mortality in Mountaintop Mining Areas of Central Appalachian States. J Rural Health. 2011;27(4):350\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eEscobedo LG, Peddicord JP. Smoking prevalence in US birth cohorts: the influence of gender and education. Am J Public Health. 1996;86(2):231\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFinkelman RB, Orem W, Castranova V, Tatu CA, Belkin HE, Zheng B, et al. Health impacts of coal and coal use: possible solutions. Int J Coal Geol. 2002;50(1):425\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFlunker JC, Sanderson WT, Christian WJ, Mannino DM, Browning SR. Environmental exposures and pulmonary function among adult residents of rural Appalachian Kentucky. J Expo Sci Environ Epidemiol. 2024;34(6):981\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGarrett BE. Socioeconomic Differences in Cigarette Smoking Among Sociodemographic Groups. Prev Chronic Dis [Internet]. 2019 [cited 2025 Jun 29];16. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/pcd/issues/2019/18_0553.htm\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/pcd/issues/2019/18_0553.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGasparotto J, Da Boit Martinello K. Coal as an energy source and its impacts on human health. Energy Geoscience. 2021;2(2):113\u0026ndash;20.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGopinathan P, Subramani T, Barbosa S, Yuvaraj D. Environmental impact and health risk assessment due to coal mining and utilization. Environ Geochem Health. 2023;45(10):6915\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHarrison H. Health Impact of Mountaintop Removal and Federal Air Quality-related Policies: A Study on Respiratory Disease and Lung Cancer Mortality Rates in KY. MPA/MPP/MPFM Capstone Projects [Internet]. 2021; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://uknowledge.uky.edu/mpampp_etds/364\u003c/span\u003e\u003cspan address=\"https://uknowledge.uky.edu/mpampp_etds/364\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHendryx M. The public health impacts of surface coal mining. Extractive Industries Soc. 2015;2(4):820\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eHendryx M, Zullig KJ, Luo J. Impacts of Coal Use on Health. Annual Review of Public Health. 2020;41(Volume 41, 2020):397\u0026ndash;415.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003ePless-Mulloli T, Howel D, Prince H. Prevalence of asthma and other respiratory symptoms in children living near and away from opencast coal mining sites. Int J Epidemiol. 2001;30(3):556\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSigmund M, Ferstl R. Panel vector autoregression in R with the package panelvar. Q Rev Econ Finance. 2021;80:693\u0026ndash;720.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSilventoinen K, Piirtola M, Jelenkovic A, Sund R, Tarnoki AD, Tarnoki DL, et al. Smoking remains associated with education after controlling for social background and genetic factors in a study of 18 twin cohorts. Sci Rep. 2022;12(1):13148.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eThurston GD, Rice MB. Air Pollution Exposure and Asthma Incidence in Children: Demonstrating the Value of Air Quality Standards. JAMA. 2019;321(19):1875\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTrapletti A. Hornik [aut K, cre, code) BL (BDS test. tseries: Time Series Analysis and Computational Finance [Internet]. 2024 [cited 2025 Jul 10]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/web/packages/tseries/index.html\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/web/packages/tseries/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWalker E, Payne D. Health Impact Assessment of Coal and Clean Energy Options in Kentucky.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYang R, An X, Chen Y, Yang X. The Knowledge Analysis of Panel Vector Autoregression: A Systematic Review. SAGE Open. 2023;13(4):21582440231215991.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCoal Data Browser. - Aggregate coal mine production for all coal [Internet]. [cited 2025 Jun 29]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.eia.gov/coal/data/browser/\u003c/span\u003e\u003cspan address=\"https://www.eia.gov/coal/data/browser/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCompressed Mortality File on CDC WONDER [Internet]. [cited 2025 Jun 29]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wonder.cdc.gov/mortsql.html\u003c/span\u003e\u003cspan address=\"https://wonder.cdc.gov/mortsql.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCounties | FRED |. St. Louis Fed [Internet]. [cited 2025 Jun 29]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://fred.stlouisfed.org/categories/28340\u003c/span\u003e\u003cspan address=\"https://fred.stlouisfed.org/categories/28340\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCounty-level Data. Sets - Education [Internet]. [cited 2025 Jun 29]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://data.ers.usda.gov/reports.aspx?ID=4026\u003c/span\u003e\u003cspan address=\"https://data.ers.usda.gov/reports.aspx?ID=4026\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eElectricity generation, capacity, and sales in the United States - U.S. Energy Information Administration (EIA) [Internet]. [cited 2025 Jun 29]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.eia.gov/energyexplained/electricity/electricity-in-the-us-generation-capacity-and-sales.php\u003c/span\u003e\u003cspan address=\"https://www.eia.gov/energyexplained/electricity/electricity-in-the-us-generation-capacity-and-sales.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKentucky Profile [Internet]. [cited 2025 Jun 29]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.eia.gov/state/print.php?sid=KY\u003c/span\u003e\u003cspan address=\"https://www.eia.gov/state/print.php?sid=KY\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eSearch the Coal Production Database [Internet]. [cited 2025 Jun 29]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://kgs.uky.edu/kgsweb/DataSearching/Coal/Production/prodsearch.asp\u003c/span\u003e\u003cspan address=\"https://kgs.uky.edu/kgsweb/DataSearching/Coal/Production/prodsearch.asp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eU.S. production of all types of coal has declined over the past two decades - U.S. Energy Information Administration (EIA) [Internet]. [cited 2025 Jun 29]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.eia.gov/todayinenergy/detail.php?id=64924\u003c/span\u003e\u003cspan address=\"https://www.eia.gov/todayinenergy/detail.php?id=64924\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eUse of coal - U.S. Energy Information Administration (EIA). [Internet]. [cited 2025 Jun 29]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.eia.gov/energyexplained/coal/use-of-coal.php\u003c/span\u003e\u003cspan address=\"https://www.eia.gov/energyexplained/coal/use-of-coal.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eWhere our coal comes from - U.S. Energy Information Administration (EIA) [Internet]. [cited 2025 Jun 29]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.eia.gov/energyexplained/coal/where-our-coal-comes-from.php\u003c/span\u003e\u003cspan address=\"https://www.eia.gov/energyexplained/coal/where-our-coal-comes-from.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"coal production, chronic respiratory disease, vector autoregression, policy simulation, Kentucky","lastPublishedDoi":"10.21203/rs.3.rs-7558480/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7558480/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCurrent public health literature has strongly associated coal mining with negative health and environmental impacts, ranging from chronic conditions to air pollution. However, existing research hasn\u0026rsquo;t explored the effects of coal mining on specific health indicators over an extended period (\u0026gt;\u0026thinsp;30 years) at the county level, data otherwise necessary to support policymakers when designing effective energy policies to transition away from coal. My paper aimed to bridge this gap by creating a Panel Vector Autoregression model and subsequent impulse response functions in R to model the impact of increases in coal production on chronic respiratory mortality rates in Kentucky. My findings suggest that increased coal production may exacerbate chronic respiratory illnesses in the short term (within 2 years), after which respiratory mortality rates plateau, possibly due to other factors (e.g., genetics or inadequate health insurance). These findings underscore the need for policymakers to expedite the transition away from coal and refrain from permitting the construction of new mines or facilities that would increase coal production. Furthermore, my model\u0026rsquo;s strong impulse response functions lay the foundation for modeling the short and long-term health effects of changes in coal production, thereby assisting legislators and researchers in designing similar models for energy policy proposals.\u003c/p\u003e","manuscriptTitle":"Effect of Coal Production on Chronic Respiratory Disease Mortality Rates in Kentucky Between 1980 and 2016","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-29 12:45:39","doi":"10.21203/rs.3.rs-7558480/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"abcbb43a-e9e4-45c9-be1e-f20214762cd6","owner":[],"postedDate":"September 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-29T12:45:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-29 12:45:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7558480","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7558480","identity":"rs-7558480","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}