Water Usage and Greenhouse Gas Emissions in the Transition from Coal to Natural Gas: A Case Study of San Juan County, New Mexico

Abstract

This study evaluates the trade-offs and environmental impacts of transitioning from coal to natural gas (NG) for electricity generation in San Juan County, with a focus on greenhouse gas emissions and water consumption. It addresses key questions, including how water use and emissions change as the county shifts from coal to natural gas. The research analyzes water usage and emissions of CO₂, NO_x, and SO₂ during both the extraction and combustion phases of coal and natural gas. Specifically, it compares water consumption and direct emissions from coal-fired and natural gas-fired power plants. The analysis utilizes ten years of combustion-phase data from the Four Corners (coal-fired) and Afton (natural gas-fired) power plants in New Mexico. Linear regression was applied to the historical data, and four transition scenarios were modeled: (1) 100% coal-generated electricity, (2) a 20% reduction in coal with a corresponding increase in NG, (3) a 50% reduction in coal with a corresponding increase in NG, and (4) a complete transition to NG. Regression analysis and scenario calculations indicate that switching to NG results in significant water savings and reduced emissions. Water savings in the combustion phase decrease by up to 2750 gallons per MWh, valued at USD 0.743 per MWh when electricity is generated 100% from NG. CO₂ emissions are substantially reduced, with the largest decrease being 0.6127 metric tons per MWh, valued at USD 61.26 per MWh. NO_x emissions in the combustion phase decline by 0.0018 metric tons per MWh, with an economic valuation of USD 14.61 per MWh, while SO₂ emissions decrease by 0.0006 metric tons per MWh, valued at USD 11.91 per MWh when electricity generation is 100% NG-based. The results highlight the environmental and economic advantages of transitioning from coal to NG. The findings underscore the environmental and economic advantages of transitioning from coal to natural gas. Water conservation is particularly vital in San Juan County’s semi-arid climate. Additionally, lower emissions support climate change mitigation, enhance air quality, and improve public health. The economic valuation of emissions reductions further highlights the financial benefits of this transition, positioning natural gas as a more sustainable and economically viable energy source for the region. Ultimately, this study emphasizes the need to adopt cleaner energy sources such as renewable energy to achieve long-term environmental sustainability and economic efficiency.

1. Introduction

The global shift from coal to alternative energy sources is a pivotal component of efforts to mitigate environmental degradation and address climate change. Recent studies underscore that this transition can substantially reduce greenhouse gas emissions, thereby contributing to climate mitigation. For instance, ref. [1] demonstrates that the energy transition also alleviates carbon inequality, with technological innovation serving as a key driver. Moreover, transitioning to cleaner energy enhances economic resilience by improving energy security and stabilizing energy prices [2]. Nonetheless, this shift demands strategic planning to account for the transboundary effects of climate change, which can influence international trade and economic stability.

San Juan County, New Mexico, has historically depended on coal-fired power plants to meet its electricity demands. However, growing environmental and economic concerns associated with coal have prompted a re-evaluation of the region’s energy policies. Coal combustion releases substantial amounts of carbon dioxide (CO₂), sulfur dioxide (SO₂), nitrogen oxides (NO_x), and particulate matter [3].

The emission of greenhouse gases from burning fossil fuels such as oil, gas, and coal is the primary driver of climate change and environmental degradation. Its consequences include the contamination and depletion of water resources, air pollution, and increased mortality rates [4]. Additionally, coal mining and combustion consume considerable amounts of water [5], exacerbating water scarcity in arid regions such as San Juan County. A study by [6] indicates that natural gas combined cycle (NGCC) plants consume less water than coal-fired plants, while a study by [7] shows that NG plants have a lower greenhouse gas footprint than coal plants.

To address these environmental concerns, climate policies have been implemented to reduce greenhouse gas emissions, improve air quality, and decrease water consumption. However, these policies often lack a detailed comparison of water use and emissions between coal and natural gas (NG) power generation. This study aims to fill that gap by evaluating changes in water consumption and pollutant emissions as San Juan County transitions from coal to natural gas. Four different scenarios were analyzed to understand the outcomes of regression analysis when transitioning from coal to NG: (1) producing electricity entirely from coal, (2) reducing coal-based electricity generation by 20% while increasing NG equivalently, (3) cutting coal-based electricity generation by 50% with an equivalent rise in NG usage, and (4) a complete shift to NG for electricity generation.

The results of this study indicate substantial environmental and economic benefits. Water consumption in electricity generation decreases by up to 2750 gallons per MWh when using 100% NG, emphasizing the importance of water conservation in semi-arid regions. CO₂ emissions are reduced by 0.6127 metric tons per MWh, underscoring the financial and environmental benefits of lowering greenhouse gas emissions. NO_x emissions decrease by 0.0018 metric tons per MWh, contributing to improved air quality and public health, while SO₂ emissions decline by 0.0006 metric tons per MWh, reducing acid rain formation and enhancing ecosystem health.

These findings highlight the significant advantages of transitioning from coal to NG for power generation in San Juan County, reinforcing the need for sustainable energy strategies to mitigate climate change, conserve water, and improve air quality.

2. Assumptions

In this research, the following assumptions have been defined:

• A linear relationship exists between inputs and outputs in both coal-fired and natural gas (NG) power plants.
• Electricity generation depends on coal, NG, and water consumption in coal-fired and NG power plants.
• Seasonal variations do not impact the amount of coal, NG, or water required to produce a kilowatt-hour (kWh) of electricity.
• Changes in water consumption and emissions production per unit of electricity generation remain consistent in coal-fired and natural gas power plants throughout the year, regardless of the season.
• Future electricity production is expected to align with historical production trends.
• The transition from coal to NG does not alter fuel combustion patterns.
• Water consumption patterns remain unchanged during the shift from coal to NG.
• A baseline of 1000 kilowatt-hours of electricity is used to estimate variations in water consumption and emissions across different transition scenarios.

3. San Juan County

This study evaluates the trade-offs between two energy development pathways in San Juan County, New Mexico, with a particular focus on their economic and environmental implications. As a major contributor to New Mexico’s energy portfolio, San Juan County plays a pivotal role in both fossil fuel extraction and renewable energy production.

Located in the northwestern corner of New Mexico, San Juan County shares borders with Arizona, Colorado, and Utah. The region features a semi-arid climate, moderate temperatures, and elevations ranging from 5000 to 7000 feet [8] (see Figure 1). It is also home to Native American tribes, including the Navajo, Ute, and Jicarilla Apache, whose rich cultural heritage significantly shapes the region’s traditions and artistic expression.

4. Methodology

This study draws on monthly data from the U.S. Energy Information Administration (EIA) and employs linear regression analysis to investigate the statistical relationships between electricity generation, water consumption, and emissions in coal-fired and natural gas (NG) power plants. The regression models produced equations that quantify water consumption and emission output per unit of electricity generated for both coal-fired and NG-fired facilities.

To evaluate the impact of transitioning from coal to natural gas, four scenarios were developed, each incorporating the respective water usage and emissions rates per unit of electricity generation. These scenarios offer insights into how varying degrees of coal-to-NG conversion could influence overall water consumption and emissions.

Two power plants were selected for data analysis: the Four Corners power plant (FCPP), representing coal-fired generation, and the Afton power plant, representing NG-based generation. Historical monthly data for both power plants were collected from the EIA for the period 2013–2022. The dataset includes key summary statistics such as electricity generation, total fuel consumption, water consumption, and emissions (including CO₂, SO₂, and NO_x), facilitating a comprehensive descriptive analysis.

This study specifically examines net electricity generation for both coal and NG power plants. A total of 115 observations were analyzed for the coal power plant, covering five operational units in 2013 and two units from 2014 onward. For the NG power plant, 113 observations were included in the analysis.

5. Results and Discussion

The linear regression analysis for the monthly data between 2013 and 2022 for generation (MWh), water consumption (million gallons), coal, NG (metric tons), SO₂, NO_x, and CO₂ (metric tons) in the FCPP and Afton power plant was conducted. Linear regression analyses were conducted separately for coal and natural gas (NG) power plants. In each model, electricity generation served as the dependent variable, while coal usage, natural gas usage, water consumption, and month were included as independent variables to estimate generation patterns for coal and NG plants. This research may have overlooked other factors that can affect electricity production. Economic factors such as electricity prices and fuel prices can influence power generation. Weather conditions, such as temperature and humidity, can also impact generation and consumption patterns. Technological advancements, improvements in power plant efficiency, and the adoption of new technologies are other parameters that affect generation. Moreover, changes in operational practices and evolving regulatory or policy frameworks—particularly those related to emissions standards, renewable energy adoption, and energy efficiency—can substantially impact generation output. Finally, plant-specific characteristics, including a facility’s age, capacity, and operational status, are important determinants of performance. In the following sections, the results of the linear regression for the coal and NG power plants are discussed.

5.1. Linear Regression Analysis for Water Consumption and Emissions from the Coal-Fired Power Plant

The linear regression analysis yielded equations modeling electricity generation, water consumption, and emissions production. These equations capture the relationships between the specified dependent and independent variables in each model. The steps used to derive these equations are outlined below. Firstly, after analyzing the data, it was noted that there was a noticeable difference in the amount of power generation during the summer months compared to other months of the year. This shows a correlation between summer months and increased power generation. As a result, the months were incorporated as a factor in the linear regression analysis for the coal-fired power plant. The value of one was assigned to the summer months (July, August, and September) and zero to the other months in a year when making the linear regression. Figure 2 shows the monthly variation in electricity generation (MWh) at the Four Corners power plant (2013–2022), with seasonal peaks during summer months.

Then, a linear regression analysis was performed to find the equation for the generation, and the following tables demonstrate the analysis results for the FCPP in San Juan County. The regression used 115 observations after removing five negative and zero values from the data.

Table 1. Regression statistics for the generation equation in FCPP.

Regression Statistics
Multiple R	0.568
R Square	0.323
Adjusted R Square	0.304
Standard Error	211,734
Observations	115

shows regression model statistics evaluating the relationship between generation (MWh) and predictors (coal, water, and month) for the Four Corners power plant (FCPP).

The regression statistics table provides several important metrics for evaluating the fit and performance of the regression model. The linear regression analysis showed a moderate positive relationship between the observed and predicted values, with a multiple R of 0.568. The R-squared value of 0.323 indicates that about 32.3% of the variance in the dependent variable is explained by the model. The standard error of the regression is 21,173. The standard error provides a way to assess the quality of the regression model. Smaller standard errors indicate more precise estimates of the coefficients, leading to more reliable inferences about the relationships between variables [9]. This analysis is based on 115 observations, which represent an adequate sample size.

Table 2. Regression coefficients and statistics for the generation equation in FCPP.

Coefficients		Standard Error	t Stat	p-Value	Lower 95%	Upper 95%
Intercept	519,328.59	42,992.11	12.08	0.00	434,136.85	604,520.32
Month coefficient	110,062.05	46,887.26	2.35	0.02	17,151.82	202,972.29
Coal	0.06	0.01	4.76	0.00	0.03	0.08
Water	289.55	87.17	3.32	0.00	116.82	462.28

illustrates regression coefficients and statistical significance for the generation equation in the FCPP, using coal, water, and seasonal variation as predictors.

A multiple R value of 0.568 indicates a moderate positive correlation between the independent and dependent variables. The authors of [10] stated that a p-value below 0.05 typically implies the coefficient is significantly different from zero, signifying a statistically significant relationship between the predictor and the outcome variable. Based on the regression, the month variable has a significant positive impact, with a coefficient of 110,062.05. Both coal and water are also significant predictors, with coefficients of 0.06 and 289.55, respectively. All predictors have p-values less than 0.05, indicating their statistical significance. These results provided confidence in the model’s findings.

The generation at the FCPP during the summer months can be calculated using the following formula. This equation is derived from a linear regression analysis for the FCPP.

G = 629,390 + (0.0567 × C) + (289.55 × W) + 110,062

(1)

where G is generation in MWh, C is coal in metric tons, and W is water in million gallons. These units are the same for all the equations. In the linear regression analysis for this equation, G is the dependent variable, while C, W, and months are the independent variables.

For the generation in the other months, Equation (2) can be utilized. The coefficient for the summer months has been removed from the equation.

G = 519,328.6 + (0.0569 × C) + (289.5 × W)

(2)

Water consumption in the summer months in the coal-fired power plant can be calculated using the following equation:

W = −2174 − (0.000196 × C) + (G/289.55)

(3)

Water consumption in the FCPP during non-summer months can be determined using Equation (4).

Water = (G/289.55) − (0.000196 × C) − 1793.57

(4)

The coefficient for the summer months has been removed from the equation. Taking the partial derivative of the water consumption to electricity generation, the following formula for the coal-fired power plant can be obtained:

dW/dG = 1/289.55 = 0.003456

(5)

This means that for every additional unit of generation in MWh, there needs to be an additional 0.003456 units of water used in million gallons, which equals 3453.6 gallons.

Based on Equation (1), the formula for the coal required to generate electricity in the coal-fired power plant can be derived as follows:

C = −11,100,364 − (5106.7 × W) + (G/0.0567)

(6)

For the other months, we can use the following equation:

C = −9,159,234.6 − (5106.7 × W) + (G/0.0567)

(7)

The derivative of coal consumption to electricity generation, dC/dG (8), can be used to quantify how much the coal consumption changes for each unit change in electricity generation.

dC/dG = 1/0.0567 = 17.63

(8)

In other words, for every additional unit of generation in MWh, there needs to be an additional 17.63 metric tons of coal used.

A linear regression analysis was conducted to examine emissions from the FCPP. The formula considered emissions as a function of power generation. Presented below are the tables resulting from the linear regression of CO₂ emissions at the FCPP.

Table 3. Regression statistics for the CO₂ emission equation in FCPP.

Regression Statistics
Multiple R	0.975597629
R Square	0.951790734
Adjusted R Square	0.951364103
Standard Error	58,109.62892
Observations	115

shows regression summary statistics for modeling CO₂ emissions as a function of electricity generation at the FCPP.

Based on Table 3, the regression analysis results indicate a strong positive relationship between the independent (generation) and dependent variable (CO₂ emission), with a multiple R of 0.976. The model explains approximately 95.2% of the variance in the dependent variable, as shown by the R-squared value of 0.952, and the Adjusted R-squared of 0.951 suggests that the model is not overfitting. The standard error of 58,109.63 indicates the average distance of the observed values from the regression line, and with 115 observations, the sample size is sufficiently large to provide reliable results.

Table 4. Regression coefficients and statistics for the CO₂ emission equation in FCPP.

Coefficients		Standard Error	t Stat	p-Value	Lower 95%	Upper 95%
Intercept	−6079.1360	16,864.5966	−0.3604	0.7191	−39,490.944	27,332.6716
Generation	1.0127	0.02144	47.2329	3.03 × 10−76	0.97024	1.0552

shows regression coefficients and diagnostics for the CO₂ emissions equation at the FCPP, with generation (MWh) as the independent variable.

The linear regression analysis shows that generation is a significant variable in this analysis, with a coefficient of 1.0127. The very low p-value (3.03 × 10⁻⁷⁶) confirms the statistical significance of this relationship. A high p-value of 0.7191 suggests no significant difference from zero. The standard errors and confidence intervals provide additional context, indicating the precision and reliability of these estimates. CO₂ emissions in the FCPP can be estimated from the following formula:

CO₂ = −6079.1 + (1.012723 × G)

(9)

where CO₂ is in metric tons and G is the generation in MWh.

To obtain the rate of change in CO₂ emission concerning electricity generation, the derivative of CO₂ emission to electricity generation, dCO₂/dG (10), can be used, as follows:

dCO₂/dG = 1.012723

(10)

This means that for every additional unit of generation in MWh, there will be an additional 1.012723 metric tons of CO₂ emissions.

For the NO_x emission in FCPP, a linear regression analysis has also been performed, and the following tables are the results from the regression. In this analysis, NO_x has been studied as a function of power generation.

Table 5. Regression statistics for the NO_x emission equation in FCPP.

Regression Statistics
Multiple R	0.440698598
R Square	0.194215254
Adjusted R Square	0.187084416
Standard Error	953.6329047
Observations	115

illustrates regression statistics for modeling NO_x emissions for electricity generation at the FCPP.

The regression analysis indicates a moderate positive correlation between the independent (generation) and dependent (NO_x emission) variables, with a multiple R value of 0.440698598. However, the model’s explanatory power is limited, as reflected by an R-squared of 0.194215254 and an Adjusted R-squared of 0.187084416, suggesting it accounts for approximately 18.71% of the variance in the dependent variable. A total of 115 observations have been analyzed in this regression, which constitutes an adequate sample size.

Table 6. Regression coefficients and statistics for the NO_x emission equation in FCPP.

Coefficients		Standard Error	t Stat	p-Value	Lower 95%	Upper 95%
Intercept	−227.1055661	276.7637	−0.82058	0.413616	−775.424	321.2132
Generation	0.001836329	0.000352	5.218809	8.26 × 10−7	0.001139	0.002533

also demonstrates regression coefficients and statistical values for the NO_x emissions model in the FCPP.

Based on Table 6, the coefficient for generation is 0.001836329, indicating that for each unit increase in generation, the dependent variable increases by approximately 0.001836329 units. This coefficient is statistically significant (p-value = 8.26 × 10⁻⁷, which is much less than 0.05), suggesting a strong relationship between generation and the dependent variable (NO_x).

NO_x emissions in the FCPP can be estimated from the following formula:

NO_x = −227.105 + (0.001836 × G)

(11)

where NO_x is in metric tons and G is the generation in MWh.

To obtain the rate of change in NO_x emission concerning electricity generation, the derivative of NO_x emission to electricity generation d NO_x/dG (12), can be used, as follows:

d NO_x/dG = 0.001836

(12)

This means that for every additional generation unit in MWh, there will be an additional 0.001836 NO_x emissions in metric tons.

For the SO₂ emission in FCPP, a linear regression analysis has also been performed, and the following tables are the results from the regression. In this analysis, SO₂ has been studied as a function of power generation.

Table 7. Regression performance statistics for the SO₂ emission equation in FCPP.

Regression Statistics
Multiple R	0.516098623
R Square	0.266357789
Adjusted R Square	0.25986538
Standard Error	259.3811765
Observations	115

shows regression performance statistics for SO₂ emissions for generation at the FCPP.

Based on Table 7, the model shows a moderate positive correlation between the observed and predicted values, with about 26.64% of the dependent variable (SO₂) variability explained by the independent variable (generation). The R-squared value (0.2664) indicates that approximately 26.64% of the variability in the dependent variable can be explained by the independent variable in the model. This suggests that the model has moderate explanatory power. The analysis is based on 115 observations.

Table 8. Coefficients and statistics for the SO₂ emission equation in FCPP.

Coefficients		Standard Error	t Stat	p-Value	Lower 95%	Upper 95%
Intercept	−92.506960	75.277	−1.2288	0.22167	−241.64	56.6317
Generation	0.0006130	9.57 × 10−5	6.40515	3.56 × 10−9	0.00042	0.00080

shows regression coefficients and statistical diagnostics for SO₂ emissions modeled against generation in the FCPP.

Based on Table 8, the coefficient for generation is 0.0006130. This coefficient is statistically significant (p-value = 3.56 × 10⁻⁹, which is much less than 0.05), suggesting a strong relationship between generation and the dependent variable (SO₂).

SO₂ emissions in the FCPP can be estimated from the following formula:

SO₂ = −92.507 + (0.0006130 × G)

(13)

where SO₂ is in metric tons and G is the generation in MWh.

To obtain the rate of change in SO₂ emission concerning electricity generation, the derivative of SO₂ emission to electricity generation, dSO₂/dG (14), can be used, as follows:

dSO₂/dG = 0.0006130

(14)

This means that for every additional generation unit in MWh, there will be an additional 0.0006130 SO₂ emission in metric tons.

5.2. Linear Regression Analysis for Water Consumption and Emissions from the Natural Gas-Fired Power Plant

From 2013 to 2022, the monthly data for the natural gas-fired power plant indicated a noticeable increase in electricity generation at the Afton power plant during the summer compared to other months. Figure 3 shows the monthly variation in electricity generation (MWh) at the Afton natural gas power plant (2013–2022), indicating elevated output during summer months.

A linear regression analysis was performed to find the equation for the generation at the Afton NG power plant.

Table 9. Regression statistics for the generation equation in the Afton power plant.

Regression Statistics
Multiple R	0.994
R Square	0.988
Adjusted R Square	0.988
Standard Error	4074.391
Observations	113

demonstrates the results of this analysis. Table 9 demonstrates regression statistics for modeling generation at the Afton natural gas power plant based on fuel input (NG), water use, and month.

The linear regression analysis demonstrated an exceptionally strong positive relationship between the observed and predicted values, with a multiple R of 0.994. The R-squared and Adjusted R-squared values are both 0.988, indicating that 98.8% of the variance in the dependent variable is explained by the model, demonstrating an excellent fit. With 113 observations, the model is based on a substantial dataset, further supporting the reliability of these results.

Table 10. Regression coefficients and statistics for the generation equation in Afton power plants.

Coefficients		Standard Error	t Stat	p-Value	Lower 95%	Upper 95%
Intercept	−3204.067	801.147830	−3.999345	0.000116	−4791.916	−1616.218
Months coefficient	−5373.99	1302.552390	−4.125740	0.000072	−7955.601	−2792.38
Fuel	0.004474	0.000092	48.8892	0.000000	0.004293	0.004655
Water	1409.2497	207.2736	6.798983	0.000000	998.440211	1820.06

shows regression coefficients and diagnostics for electricity generation at Afton, with natural gas and water as key inputs.

The linear regression results show that the intercept and all coefficients (months, fuel, water) are statistically significant, with very low p-values. The intercept is −3204.067, indicating the starting value of the dependent variable when other variables are zero. Fuel and water have positive coefficients of 0.004474 and 1409.2497, respectively, indicating that increases in these variables lead to increases in the dependent variable (generation).

Based on the analysis, the formula for generations in the summer months in the Afton power plant can be determined as follows:

Generation = −8578 + (0.00447 × NG) + (1409.25 × W)

(15)

where G is generation in MWh, NG is natural gas in metric tons, and W is water in million gallons. These units are the same for all the equations. By removing the coefficients for the summer months from Equation (15), the equation for the generation in the other months can be obtained as follows:

Generation = −3204.07 + (0.00447 × NG) + (1409.25 × W)

(16)

So, based on these equations, it is possible to find the amount of water and NG required to generate electricity in the Afton power plant. NG consumption in the summer months in the natural gas-fired power plant can be calculated using the following equation:

NG = 1,860,198 + (G/0.00447) − (299,840 × W)

(17)

and for other months, NG can be estimated using the following formula:

NG = 716,794 + (G/0.00447) − (299,840 × W)

(18)

The derivative of NG consumption to electricity generation, dNG/dG (19), can be used to quantify how much the NG consumption changes for each unit change in electricity generation.

dNG/dG = 1/0.00447 = 223.7

(19)

This means that for every additional unit of generation in MWh, there needs to be an additional 223.7 metric tons of NG used.

From Equation (15), water consumption in the summer months in the natural gas-fired power plant can be calculated using the following equation:

Water = 6.1 − (0.000003 × NG) + (G/1409.25)

(20)

For the other months, the following equation can be used:

Water = 2.27 − (0.000003 × NG) +(G/1409.25)

(21)

Taking the partial derivative of water consumption to electricity generation, the following formula for the natural gas-fired power plant can be obtained:

dW/dG = 1/1409.25 = 0.0007

(22)

This means that for every additional unit of generation in MWh, there needs to be an additional 0.0007 units of water used in million gallons, which equates to 700 gallons.

A linear regression was utilized to analyze emissions at the Afton power plant for each type of emission. The following tables are the results of the linear regression for CO₂ emissions.

Table 11. Regression statistics for CO₂ emissions in Afton power plants.

Regression Statistics
Multiple R	0.992689068
R Square	0.985431587
Adjusted R Square	0.98530034
Standard Error	1794.487072
Observations	113

demonstrates regression statistics for CO₂ emissions as a function of electricity generation at the Afton power plant.

The regression analysis indicates a strong linear relationship between the predicted and observed values, with a multiple R of 0.9927. The R-squared value of 0.9854 suggests that approximately 98.5% of the variability in the dependent variable is explained by the model. The adjusted R-squared value, which accounts for the number of predictors in the model, is also very high at 0.9853, confirming the model’s robustness. The standard error of 1794.4871 represents the average distance that the observed values deviate from the regression line, showing the model’s precision. Additionally, the sample size of 113 observations is substantial and adequate for this analysis.

Table 12. Regression coefficients and statistics for CO₂ emissions in Afton power plants.

Coefficients		Standard Error	t Stat	p-Value	Lower 95%	Upper 95%
Intercept	1836.9883	330.86588	5.5520635	0.0000002	1181.3555445	2492.6212
Generation	0.400042	0.0046168	86.65000	0.0000000	0.3908933	0.4091901

shows regression coefficients and evaluation metrics for modeling CO₂ emissions at the Afton NG power plant.

The linear regression results showed that both the interception and the generation variable are statistically significant, with very low p-values. The intercept is 1836.9883, indicating the starting value of the dependent variable when generation is zero. The generation coefficient is 0.400042, suggesting that for each additional unit of generation, the dependent variable (CO₂) increases by 0.400042 units. These findings highlight the significant positive impact of generation on the dependent variable (CO₂).

The CO₂ emissions in the NG power plant, based on the linear regression results, can be estimated from the following equation:

CO₂ = 1836.98 + (0.40004 × G)

(23)

where CO₂ is in metric tons and G is generation in MWh.

To obtain the rate of change in CO₂ emission concerning electricity generation, the derivative of CO₂ emission to electricity generation, dCO₂/dG (24), can be used, as follows:

dCO₂/dG = 0.40004

(24)

This means that for every additional unit of generation in MWh, there will be an additional 0.40004 metric tons of coal CO₂ emissions.

For the NO_x emission in the Afton power plant, a linear regression analysis has also been performed, and the following tables are the results from the regression. In this analysis, NO_x has been studied as a function of power generation.

Table 13. Regression statistics for the NO_x emission equation in the Afton power plant.

Regression Statistics
Multiple R	0.562057754
R Square	0.315908918
Adjusted R Square	0.309745936
Standard Error	2.265719175
Observations	113

shows regression statistics for modeling NO_x emissions at the Afton plant based on electricity generation.

The regression statistics indicate a moderate linear relationship between the predicted and observed values, with a multiple R of 0.5621. The R-squared value of 0.3159 means that approximately 31.6% of the variability in the dependent variable is explained by the model. The adjusted R-squared value, which adjusts for the number of predictors, is slightly lower at 0.3097, suggesting that the model’s explanatory power is somewhat reduced when accounting for the number of predictors. The standard error of 2.2657 represents the average distance that the observed values fall from the regression line, indicating the model’s precision. With 113 observations, the sample size is substantial, adding to the reliability of these results.

Table 14. Regression coefficients and statistics for the NO_x emission equation in the Afton power plant.

Coefficients		Standard Error	t Stat	p-Value	Lower 95%	Upper 95%
Intercept	1.80215	0.417751223	4.313942	3.49299 × 10−5	0.9743526	2.6299565
Generation	0.0000417	0.0000058291	7.159545	9.3192 × 10−11	3.0183 × 10−5	5.32846 × 10−5

demonstrates regression coefficients for the NO_x emissions equation at Afton, using generation as the independent variable.

The linear regression results showed that both the interception and the generation variable are statistically significant, with very low p-values. The generation coefficient is 0.0000417, suggesting that for each additional unit of generation, the dependent variable (NO_x) increases by 0.0000417 units. With 113 observations, the sample size is substantial, contributing to the reliability of these results.

NO_x emissions in the Afton power plant can be evaluated from the following formula:

NO_x = 1.80215 + (0.0000417 × G)

(25)

where NO_x is in metric tons and G is the generation in MWh.

To obtain the rate of change in NO_x emission concerning electricity generation, the derivative of NO_x emission to electricity generation d NO_x/dG (26), can be used, as follows:

d NO_x/dG = 0.0000417

(26)

This means that for every additional generation unit in MWh, there will be an additional 0.0000417 NO_x emissions in metric tons.

For the SO₂ emission in the Afton power plant, a linear regression analysis has been performed, and the following tables are the results from the regression. In this analysis, SO₂ has been studied as a function of power generation.

Table 15. Regression statistics for the SO₂ emission equation in the Afton power plant.

Regression Statistics
Multiple R	0.992607188
R Square	0.985269029
Adjusted R Square	0.985136318
Standard Error	0.009115096
Observations	113

shows regression statistics for SO₂ emissions modeled against generation in the Afton power plant.

The regression statistics indicate a strong linear relationship between the predicted and observed values, with a multiple R of 0.9926. The R-squared value of 0.9853 means that approximately 98.5% of the variability in the dependent variable is explained by the model. The adjusted R-squared value, which adjusts for the number of predictors, is also very high at 0.9851, confirming the model’s robustness. The standard error of 0.0091 represents the average distance that the observed values fall from the regression line, indicating the model’s precision. With 113 observations, the sample size is substantial, adding to the reliability of these results.

Table 16. Regression coefficients for SO₂ emissions as a function of electricity generation at Afton.

Coefficient		Standard Error	t Stat	p-Value	Lower 95%	Upper 95%
Intercept	0.0092033	0.001681	5.476077	2.73593 × 10−7	0.005873	0.0125
Generation	0.000002	2.34508 × 10−8	86.1635	1.6531 × 10−103	1.97413 × 10−6	2.06707 × 10−6

demonstrates regression coefficients for SO₂ emissions as a function of electricity generation at Afton.

The linear regression results showed that both the intercept and the generation variable are highly significant, with p-values much lower than 0.05. The intercept coefficient of 0.0092033 suggests that when all predictors are zero, the expected value of the dependent variable is 0.0092033. The generation coefficient of 0.000002 indicates that for each unit increase in generation, the dependent variable (SO₂) increases by 0.000002 units.

Based on the results from linear regression, SO₂ emissions in the Afton power plant can be estimated from the following formula:

SO₂ = 0.0092033 + (0.000002 × G)

(27)

where SO₂ is in metric tons and G is the generation in MWh.

To obtain the rate of change in SO₂ emission concerning electricity generation, the derivative of SO₂ emission to electricity generation, d SO₂/dG (28), can be used, as follows:

d SO₂/dG = 0.000002

(28)

This means that for every additional generation unit in MWh, there will be an additional 0.000002 SO₂ emissions in metric tons.

By applying formulas to different scenarios, one can determine changes in water usage and emissions when transitioning from coal to natural gas.

5.3. Utilizing Formulas in Different Scenarios to Switch from Coal to Natural Gas

Using the partial derivative obtained from the equations, the changes in the volume of water consumption and the level of emissions produced by each power plant in different scenarios can be determined.

Table 17. Partial derivative for coal, NG, CO₂, NO_x, and SO₂ considering generation.

Type of Power Plant	Coal/NG	Water	CO2	NOx	SO2
Coal-fired (FCPP)	dC/dG = 17.63	dW/dG = 0.003456	dCO2/dG = 1.012723	dNOx/dG = 0.001836	dSO2/dG = 0.0006130
Natural gas-fired (Afton power plant)	dNG/dG = 223.7	dW/dG = 0.0007	dCO2/dG = 0.40004	dNOx/dG = 0.0000417	dSO2/dG = 0.000002

summarizes the partial derivatives for coal, NG, CO₂, NO_x, and SO₂, considering generation in coal-fired and natural gas-fired power plants. Table 17 shows partial derivatives for water consumption and emissions (CO₂, NO_x, SO₂) for electricity generation (dG), and for coal-fired and NG-fired power plants.

5.4. Utilizing Formulas in the First Scenario

This research utilized water consumption and emission production rates per unit of electricity produced (as shown in Table 17) to calculate the changes in the volume of water consumption and the level of emissions production per unit of electricity during the transition from coal to natural gas in each scenario.

The first scenario considers producing 100% electricity from coal in the FCPP. So, for the first scenario, water consumption and emission production rates per unit of electricity produced in FCPP can be used when generating 100% electricity in FCPP as follows:

dW/dg (100% Coal) = 0.003456  million gallon/MWh

dCO₂/dG(100% Coal) = 1.012723  metric tons/MWh

dNO_x/dG (100% Coal) = 0.001836  metric tons/MWh

dSO₂/dG (100% Coal) = 0.0006130  metric tons/MWh

5.5. Utilizing Formulas in the Second Scenario

The second scenario considered decreasing electricity generation by 20% at the FCPP and increasing generation by the same amount at the Afton power plant. To calculate the water consumption and emission production rates per unit of electricity generated in this scenario, the rates at FCPP should be multiplied by 80%, and the rates at the Afton power plant by 20% as follows:

dW/dG (80% Coal) = 0.8 × 0.003456 = 0.0028  million gallon/MWh

dCO₂/dG (80% Coal) = 0.8 × 1.012723 = 0.8102  metric tons/MWh

dNO_x/dG (80% Coal) = 0.8 × 0.001836 = 0.00147  metric tons/MWh

dSO₂/dG (80% Coal) = 0.8 × 0.0006130 = 0.0005  metric tons/MWh

The calculations for generating 20% of electricity at the Afton power plant in the second scenario are as follows:

dW/dG (20% NG) = 0.2 × 0.0007 = 0.000141  million gallon/MWh

dCO₂/dG (20% NG) = 0.2 × 0.40004 = 0.080008  metric tons/MWh

dNO_x/dG (20% NG) = 0.2 × 0.0000417 = 0.00000834  metric tons/MWh

dSO₂/dG (20% NG) = 0.2 × 0.000002 = 0.0000004  metric tons/MWh

To determine the net impact on water consumption and emission production in the second scenario, the combined rates (80% coal and 20% natural gas) calculated in this scenario are deducted from the rates calculated in the first scenario (100% coal).

The net changes in water consumption in the second scenario can be determined from the following equation:

dW/dG (net change 80% Coal & 20% NG) = 0.003456 (dW/dG 100% coal) − (0.8 × 0.003456 + 0.2 × 0.0007) = 0.00055 million gallon/MWh

Which is equal to 550 gallons/MWh.

This signifies that transitioning from scenario one to scenario two leads to a saving of 550 gallons of water per MWh of electricity generated.

Using the same process, the changes in the volume of producing emissions, in the transition from scenario one to scenario two, can be determined as follows:

dCO₂/dG (net change 80% Coal & 20% NG) = 1.01723 (dCO₂/dG 100% coal) − (0.8 × 1.01723 + 0.2 × 0.40004) = 0.123438 metric tons/MWh

This suggests that the shift from scenario one to scenario two leads to a decrease of 0.123438 metric tons of CO₂ emissions per MWh of electricity generated.

dNO_x/dG (net change 80% Coal & 20% NG) = 0.001836 (dNO_x/dG 100% coal) − (0.8 × 0.001836 + 0.2 × 0.0000417) = 0.000359 metric tons/MWh

This suggests that the shift from scenario one to scenario two leads to a decrease of 0.000359 metric tons of NO_x emissions per MWh of electricity generated.

dSO₂/dG (net change 80% Coal & 20% NG) = 0.0006130 (dSO₂/dG 100% coal) − (0.8 × 0.0006130 + 0.2 × 0.000002) = 0.0001222 metric tons/MWh

This indicates that transitioning from scenario one to scenario two leads to a decrease of 0.0001222 metric tons of SO₂ emissions per MWh of electricity produced.

5.6. Utilizing Formulas in the Third Scenario

The third scenario considered decreasing electricity generation by 50% at the FCPP and increasing generation by the same amount at the Afton power plant. To calculate the water consumption and emission production rates per unit of electricity generated in this scenario, the rates at the FCPP and Afton power plant should be multiplied by 50% as follows:

dW/dG (50% Coal) = 0.5 × 0.003456 = 0.001728  million gallon/MWh

d CO₂/dG (50% Coal) = 0.5 × 1.012723 = 0.5064  metric tons/MWh

d NO_x/dG (50% Coal) = 0.5 × 0.001836 = 0.000918  metric tons/MWh

d SO₂/dG (50% Coal) = 0.5 × 0.0006130 = 0.000306  metric tons/MWh

The calculations for generating 50% of electricity at the Afton power plant in the second scenario are as follows:

dW/dG (50% NG) = 0.5 × 0.0007 = 0.00035  million gallon/MWh

dCO₂/dG (50% NG) = 0.5 × 0.40004 = 0.20002  metric tons/MWh

d NO_x/dG (50% NG) = 0.5 × 0.0000417 = 0.000021  metric tons/MWh

dSO₂/dG (50% NG) = 0.5 × 0.000002 = 0.000001  metric tons/MWh

To determine the net impact on water consumption and emission production in the third scenario, the combined rates (50% coal and 50% NG) calculated in this scenario are deducted from the rates calculated in the first scenario (100% coal).

The net changes in water consumption in the second scenario can be determined from the following equation:

dW/dG (net change 50% Coal & 50% NG) = 0.003456 (dW/dG 100% coal) − (0.5 × 0.003456 + 0.5 × 0.0007) = 0.001375 million gallon/MWh

Which is equal to 1375 gallons/MWh.

This signifies that transitioning from scenario one to the third scenario leads to a saving of 1375 gallons of water per MWh of electricity generated.

Using the same process, the changes in the volume of producing emissions in the transition from scenario one to the third scenario can be determined as follows:

dCO₂/dG (net change 50% Coal & 50% NG) = 1.01723 (dCO₂/dG 100% coal) − (0.5 × 1.01723 + 0.5 × 0.40004) = 0.306342 metric tons/MWh

This suggests that the shift from scenario one to the third scenario leads to a decrease of 0.306342 metric tons of carbon dioxide emissions per MWh of electricity generated.

dNO_x/dG (net change 50% Coal & 50% NG) = 0.001836 (dNO_x/dG 100% coal) − (0.5 × 0.001836 + 0.5 × 0.0000417) = 0.0008973 metric tons/MWh

This suggests that the shift from scenario one to the third scenario leads to a decrease of 0.0008973 metric tons of NO_x emissions per MWh of electricity generated.

dSO₂/dG (net change 50% Coal & 50% NG) = 0.0006130 (dSO₂/dG 100% coal) − (0.5 × 0.0006130 + 0.5 × 0.000002) = 0.0003055 metric tons/MWh

This indicates that transitioning from scenario one to the third scenario leads to a decrease of 0.0003055 metric tons of SO₂ emissions per MWh of electricity produced.

5.7. Utilizing Formulas in the Fourth Scenario

The last scenario investigated producing 100% electricity from NG in the Afton power plant. So, for the last scenario, water consumption and emission production rates per unit of electricity produced in the Afton power plant can be used when generating 100% electricity in this power plant as follows:

dW/dG (100% NG) = 0.0007  million gallon/MWh

dCO₂/dG(100% NG) = 0.40004  metric tons/MWh

dNO_x/dG (100% NG) = 0.0000417  metric tons/MWh

dSO₂/dG (100% NG) = 0.000002  metric tons/MWh

To determine the net impact on water consumption and emission production in the transition from the first scenario (100% coal) to the last scenario (100% NG), the following calculations can be utilized:

dW/dG (100% Coal) − dW/dG (100% NG) = 0.00345 − 0.0007 = 0.00275 million gallon/MWh = 2750 gallon/MWh

This indicates that transitioning from generating 100% of electricity from coal (first scenario) to producing 100% of electricity from NG (last scenario) can result in saving 2750 gallons of water per MWh.

dCO₂/dG(100% Coal) − dCO₂/dG(100% NG) = 1.012723 − 0.40004 = 0.612683 metric tons/MWh

This indicates that transitioning from generating 100% of electricity from coal (first scenario) to producing 100% of electricity from natural gas (last scenario) can result in a decrease of 0.612683 metric tons of CO₂ per MWh.

dNO_x/dG (100% Coal) − dNO_x/dG (100% NG) = 0.001836 − 0.0000417 = 0.0017946 metric tons/MWh

This indicates that transitioning from generating 100% of electricity from coal (first scenario) to producing 100% from natural gas (last scenario) can decrease 0.0017946 metric tons of NO_x per MWh.

dSO₂/dG (100% Coal) − dSO₂/dG (100% NG) = 0.0006130 − 0.000002 = 0.000611 metric tons/MWh

This indicates that transitioning from generating 100% of electricity from coal (first scenario) to producing 100% of electricity from natural gas (last scenario) can result in a decrease of 0.000611 metric tons of SO₂ per MWh.

Table 18. (a) Summary of net change for water, CO₂, NO_x, and SO₂ for 1000 MWh electricity generation. (b) Net change in water usage and emissions (CO₂, NO_x, SO₂) for each transition scenario, compared to a 100% coal baseline scenario. Summary of net change for water, CO₂, NO_x, and SO₂ for 1000 MWh electricity generation.

(a)
Scenarios	Coal Power Plant					NG Power Plant
	Coal Utilized	H2O Utilized	NOx	SO2	CO2	NG Utilized	H2O Utilized	NOx	SO2	CO2
1000 MWh (100% coal)	17,630.0	3.450	1.836	0.613	1012.723	0.000	0.000	0.000	0.000	0.000
1000 WMh (80% coal/20% NG)	13,888.0	2.760	1.469	0.490	810.179	44,740.0	0.140	0.008	0.0004	80.008
1000 WMh (50% coal/50% NG)	8680.0	1.725	0.918	0.307	506.362	111,850.0	0.350	0.021	0.001	200.020
1000 MWh (100% NG)	0.000	0.000	0.000	0.000	0.000	223,700.0	0.700	0.042	0.002	400.040
(b)
Scenarios	Difference Based on 1000 MWh Power Generation from 100% Coal
	H2O Utilized			NOx		SO2		CO2
1000 MWh (100% coal)	0.000			0.000		0.000		0.000
1000 WMh (80% coal/20% NG)	−0.5500			−0.359		−0.1222		−123.438
1000 WMh (50% coal/50% NG)	−1.3750			−0.8973		−0.3055		−306.342
1000 MWh (100% NG)	−2.7500			−1.7946		−0.6110		−612.683

summarizes the net change for each variable in each scenario compared to the first scenario. As indicated in the table, the net change in water consumption and emission production has been calculated based on the generation of 1000 MWh of electricity. Table 18 shows the water consumption, emissions, and fuel input required for generating 1000 MWh under four coal-to-NG transition scenarios. Values are broken down by power plant type.

Based on Table 18, transitioning from scenario one to the second scenario can result in a saving of 0.5500 million gallons (550,000 gallons) of water and a reduction of 0.3589, 0.1222, and 123.438 metric tons of NO_x, SO₂, and CO₂, respectively, when generating 1000 MWh of electricity. Also, transitioning from scenario one to the third scenario can result in a saving of 1.3750 million gallons (1,375,000 gallons) of water and a reduction of 0.8973, 0.3055, and 306.342 metric tons of NO_x, SO₂, and CO₂, respectively, when generating 1000 MWh of electricity. Finally, transitioning from scenario one to the last scenario can result in a saving of 2.7500 million gallons (2,750,000 gallons) of water and a reduction of 1.7946, 0.6110, and 612.683 metric tons of NO_x, SO₂, and CO₂, respectively, when generating 1000 MWh of electricity.

Table 19. Water usage and emission production in mining coal and natural gas in different scenarios.

Scenarios	Coal Power Plant					NG Power Plant
	Coal Utilized	H2O Utilized	NOx	SO2	CO2	NG Utilized	H2O Utilized	NOx	SO2	CO2
1000 MWh (100% coal)	500	0.2642	1200	1600	900,000	0	0	0	0	0
1000 WMh (80% coal/20% NG)	400	0.2113	960	1280	810.179	40	0.0264	60	N/A	90,000
1000 WMh (50% coal/50% NG)	250	0.1321	600	800	450,000	100	0.066	150	N/A	225,000
1000 MWh (100% NG)	0	0	0	0	0	200	0.1321	300	N/A	450,000

demonstrates the water usage and emission production in mining coal and natural gas in different scenarios for mining operations. In this table, for 1000 MWh energy production based on energy sources such as coal and natural gas, we investigated and analyzed several impressive factors, including coal required, CO₂, SO₂, and NO_x emissions, as well as water usage for mining. Then, we calculated the results in four scenarios for each power plant. The units in this table for coal, natural gas, and emissions are metric tons, and for water, the units are millions of gallons. Table 19 [11,12,13,14,15,16] demonstrates water use and emissions (CO₂, NO_x, SO₂) associated with coal and natural gas fuel extraction (mining phase) for different transition scenarios producing 1000 MWh.

This research evaluates the environmental impacts of transitioning from coal to NG for electricity generation in San Juan County, New Mexico, focusing on the FCPP plant and the Afton power plant.

The results for changing the rates for water consumption (dW), carbon dioxide emissions (dCO₂), nitrogen oxide emissions (dNO_x), and sulfur dioxide emissions (dSO₂) per unit of generation (dG) for each scenario are calculated and compared.

5.8. Water Usage (dW)

In terms of water consumption in the combustion phase, the trend in shifting from coal to natural gas indicates that NG power plants are more water-efficient compared to coal-fired power plants. The significant reduction in water consumption highlights one of the major environmental benefits of transitioning to NG.

The water consumption per unit of generation decreases as the generation transitions from coal to NG. The values are as follows:

Scenario 2: 0.00055 million gallons/MWh (550 gallons/MWh) decrease in comparison with scenario one.

Scenario 3: 0.001375 million gallons/MWh (1375 gallons/MWh) decrease in comparison with scenario one.

Scenario 4: 0.00275 million gallons/MWh (2750 gallons/MWh) decrease in comparison with scenario one.

These reductions align with findings from previous studies. For example, the authors of [17] reported that natural gas (NG) power plants typically consume less water than coal-fired plants, primarily due to differences in cooling technologies and fuel characteristics. Similarly, research by [18] demonstrated that natural gas combined cycle (NGCC) plants significantly reduce water usage compared to coal plants, owing largely to their higher thermal efficiency and the implementation of dry cooling systems. This decrease in water consumption represents a critical environmental advantage, particularly in arid regions such as San Juan County, where water resources are scarce. The transition to NGCC not only promotes water conservation but also alleviates pressure on local water supplies, an essential factor for maintaining ecological stability and meeting community needs. Furthermore, reduced water use can lead to lower water treatment costs and decreased competition among water users, thereby offering substantial benefits to both the environment and surrounding populations.

5.9. Carbon Dioxide Emissions (dCO₂)

These outcomes from this study demonstrate the substantial reduction in CO₂ emissions when shifting from coal to NG, with the most considerable decrease observed in Scenario 4. These results are in [19], which state that NG power plants emit approximately 50–60% less CO₂ than coal plants.

The marked decrease across the scenarios is shown as follows:

Scenario 2: 0.123438 metric tons/MWh decrease in comparison with scenario one.

Scenario 3: 0.306342 metric tons/MWh decrease in comparison with scenario one.

Scenario 4: 0.612683 metric tons/MWh decrease in comparison with scenario one.

The substantial decrease in emissions in different scenarios indicates that NG could play a crucial role in reducing the carbon footprint of the FCPP in San Juan County. This transition is also supported by New Mexico’s Energy Transition Act, which promotes a shift from coal to more sustainable energy sources [20].

The results of this study demonstrate a significant decrease in NO_x emissions. The reduction in NO_x emissions across the scenarios is consistent with the cleaner combustion process of NG compared to coal. The marked decrease in NO_x emissions noted in Scenario 4 indicates that a full shift to NG could result in major enhancements in air quality. The values are as follows:

Scenario 2: 0.000359 metric tons/MWh decrease in comparison with scenario one.

Scenario 3: 0.0008973 metric tons/MWh decrease in comparison with scenario one.

Scenario 4: 0.0017946 metric tons/MWh decrease in comparison with scenario one.

These findings align with the existing literature that underscores the environmental benefits of NG over coal. For instance, a study conducted by [21] showed that switching from coal to NG could reduce NO_x emissions by approximately 50%. This is due to NG’s lower nitrogen content and more effective combustion processes.

5.10. Sulfur Dioxide Emissions (SO₂)

In shifting from coal to NG, SO₂ emissions show a decreasing trend, with the lowest values observed in Scenario 4, emphasizing the environmental benefits of switching to NG.

The values are as follows:

Scenario 2: 0.0001222 metric tons/MWh decrease in comparison with scenario one.

Scenario 3: 0.0003055 metric tons/MWh decrease in comparison with scenario one.

Scenario 4: 0.000611 metric tons/MWh decrease in comparison with scenario one.

The findings support studies that have shown significant decreases in SO₂ emissions when transitioning from coal to NG. A study by [22] suggests that using NG instead of higher sulfur fuels can effectively reduce SO₂ levels, particularly during specific times of the year when emissions are higher due to increased fuel combustion for heating or cooling.

The transition from coal to NG for electricity generation in San Juan County yields considerable environmental benefits, as evidenced by the reductions in water consumption, CO₂, NO_x, and SO₂ emissions. The findings indicate that NG is a cleaner and more water-efficient alternative to coal, offering a path to substantial environmental improvements. These results are crucial for policymakers and stakeholders in San Juan County as they consider strategies for sustainable energy transition. This research highlights the importance of reducing coal dependency and increasing NG utilization to mitigate climate change and promote environmental sustainability.

5.11. The Economic Aspect of Transferring Energy Production from Coal to Natural Gas

This section focuses on an economic evaluation of transitioning from coal to NG across various scenarios, focusing on the reduction in water consumption, CO₂, NO_x, and SO₂ emissions. It should be emphasized that these economic results are only relevant to the two power plants studied in this research.

5.11.1. Water Savings

Scenario 2: 0.00055 million gallons/MWh (550 Gallons/MWh) will save in comparison with scenario one.

Scenario 3: 0.001375 million gallons/MWh (1375 Gallons/MWh) will save in comparison with scenario one.

Scenario 4: 0.00275 million gallons/MWh (2750 Gallons/MWh) will save in comparison with scenario one.

The cost of water in New Mexico, as indicated by the Office of the State Engineer (OSE), is estimated at USD 88 per acre-foot. This estimate is based on the agreement with the state [23], which outlines that the cost per acre-foot can range from USD 88 to USD 190, depending on the amount of water leased and the consumer price index [23]; based on this report, the economic value of saving water to generate each MWh in each scenario would be as follows:

Scenario 2 = 550 (gallons)/325,851 × 88 = USD 0.1485

Scenario 3 = 1375 (gallons)/325,851 × 88 = USD 0.3713

Scenario 4 = 2750 (gallons)/325,851 × 88 = USD 0.743

In this calculation, one acre-foot is equal to 325,851 gallons, and the calculations are based on the minimum water value of USD 88 per acre-foot.

5.11.2. CO₂ Emissions Reduction

Based on the study results, the CO₂ reduction in each scenario is as follows:

Scenario 2: 0.123438 metric tons/MWh decrease in comparison with scenario one.

Scenario 3: 0.306342 metric tons/MWh decrease in comparison with scenario one.

Scenario 4: 0.612683 metric tons/MWh decrease in comparison with scenario one.

A report by [24] suggests that the health-related advantages of CO₂ reduction could be significant, with conservative estimates exceeding USD 100 per ton of CO₂ mitigated in high-income nations and USD 50 per ton in middle-income countries. Based on this report, the economic value of the deduction of CO₂ to generate each MWh in each scenario would be as follows:

Scenario 2 = 0.123438 (metric tons) × 100 = USD 12.34

Scenario 3 = 0.306342 (metric tons) × 100 = USD 30.63

Scenario 4 = 0.612683 (metric tons) × 100 = USD 61.26

These calculations are based on the high-income nations’ benefit of reducing one ton of CO₂, which equals USD 100.

5.11.3. NO_x Emissions Reduction

Based on the study results, the NO_x reduction in each scenario is as follows:

Scenario 2: 0.000359 metric tons/MWh decrease in comparison with scenario one.

Scenario 3: 0.0008973 metric tons/MWh decrease in comparison with scenario one.

Scenario 4: 0.0017946 metric tons/MWh decrease in comparison with scenario one

According to the EPA [25], the health benefits per ton of NO_x reduced in the oil and NG sectors are valued at approximately USD 8140 per ton.

Based on this report, the economic value of the deduction of NO_x to generate each MWh in each scenario would be as follows:

Scenario 2 = 0.000359 (metric tons) × 8140 = USD 2.92

Scenario 3 = 0.0008973 (metric tons) × 8140 = USD 7.30

Scenario 4 = 0.0017946 (metric tons) × 8140 = USD 14.61

5.11.4. SO₂ Emissions Reduction

Based on the study results, the SO₂ reduction in each scenario is as follows:

Scenario 2: 0.0001222 metric tons/MWh decrease in comparison with scenario one.

Scenario 3: 0.0003055 metric tons/MWh decrease in comparison with scenario one.

Scenario 4: 0.000611 metric tons/MWh decrease in comparison with scenario one.

According to the EPA [17], the health benefits per ton of SO₂ reduced in the oil and natural gas sector are valued at approximately USD 19,500 per ton.

Based on this report, the economic value of the deduction of SO₂ to generate each MWh in each scenario would be as follows:

Scenario 2 = 0.0001222 (metric tons) × 19,500 = USD 2.38

Scenario 3 = 0.0003055 (metric tons) × 19,500 = USD 5.96

Scenario 4 = 0.000611 (metric tons) × 19,500 = USD 11.91

6. Potential Impact of the Study’s Findings on Local Communities and Ecosystems

6.1. Health Improvements

A study by [23] indicates that air pollution, which includes a variety of pollutants such as particulate matter (PM), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), and ozone (O₃), poses significant health risks. These pollutants can cause a wide range of health problems, particularly affecting the respiratory and cardiovascular systems. They are known contributors to conditions such as asthma, bronchitis, heart attacks, and strokes. Reducing emissions of CO₂, NO_x, and SO₂ substantially enhances air quality, which in turn is closely associated with lower rates of respiratory and cardiovascular diseases. This improvement in air quality not only promotes better public health but also alleviates the burden on healthcare systems, ultimately leading to reduced healthcare costs for both individuals and communities.

6.2. Economic Opportunities

The economic benefits of transitioning from coal to natural gas are multifaceted. First, the savings from reduced water usage and lower emissions may lead to lower energy costs for consumers and businesses. This can stimulate economic growth by freeing up resources for other investments. Additionally, the natural gas sector can create new job opportunities, both directly in natural gas extraction and indirectly in related industries. Programs to retrain workers from the coal industry can help ensure that the local workforce adapts to the new economic landscape, providing stability and growth for the community.

6.3. Ecosystem Benefits

According to [24], air pollution negatively impacts terrestrial ecosystems by contributing to acid rain and nutrient imbalances. Acidic precipitation increases soil acidity, mobilizes toxic metals, and depletes essential nutrients, all of which impair plant health, reduce agricultural productivity, and disrupt forest ecosystems. Lower emissions of pollutants such as SO₂ reduce the incidence of acid rain. Consequently, the transition to natural gas, which emits fewer pollutants, plays a role in mitigating acid rain and protecting local flora and fauna—ultimately fostering healthier ecosystems. This shift benefits both terrestrial and aquatic life by promoting biodiversity and supporting habitat restoration. Moreover, the considerable water savings achieved by using natural gas instead of coal can bolster local agriculture and water supply systems. In semi-arid regions like San Juan County, consistent and sufficient water resources are crucial for sustaining ecosystem balance and ensuring that plants, animals, and human communities have the water they need to thrive.

7. Integrating Research Findings with Sustainable Development Goals and Energy Policies

7.1. Alignment with Sustainable Development Goals (SDGs)

SDG 7 (Affordable and Clean Energy): shifting from coal to natural gas aids in achieving SDG 7 by encouraging the use of cleaner energy sources, thereby lowering emissions and enhancing air quality.

SDG 13 (Climate Action): The marked decrease in CO₂ emissions supports SDG 13, aiding initiatives to address climate change and its impacts.

7.2. Policy Recommendations

Incorporation into Local and State Energy Policies: This study’s outcomes can inform policy development at local and state levels by illustrating the environmental and economic advantages of adopting natural gas. Such data can guide policymakers in framing regulations and incentives promoting natural gas over coal.

Facilitating Renewable Energy Integration: Though natural gas is a cleaner substitute for coal, it can also serve as a transitional fuel that facilitates the incorporation of renewable energy sources into the energy grid. Developing infrastructure for natural gas under supportive policies can ease the transition to a more sustainable energy portfolio.

8. Discussion on the Economic Implications and the Impact on Long-Term Energy Supply

Although natural gas is a cleaner and more cost-effective alternative to coal, its prices can be volatile due to market fluctuations, geopolitical influences, and changes in supply and demand. These instabilities can, in turn, impact electricity generation costs and consumer energy prices. To mitigate such risks, it is advisable to adopt long-term contracts, implement hedging strategies, and diversify the energy portfolio by integrating renewable sources such as wind and solar power to promote greater price stability. The transition from coal to natural gas also necessitates consideration of the long-term availability of gas reserves, potential future supply constraints, and substantial investments in infrastructure, including pipelines and storage facilities, to ensure a secure and reliable energy supply. Furthermore, incorporating renewable energy into the grid can support a more sustainable energy future by reducing dependence on natural gas and enhancing overall energy security.

9. Short Analysis About the History and Future of Energy for San Juan County from Different Aspects

9.1. Expansion of Mitigate Climate Change Technologies for the U.S. and the Whole World

It has been well established that reducing carbon dioxide (CO₂) emissions positively impacts climate change, and carbon capture technologies are one of the key methods to achieve this reduction [25]. Among the various strategies available to industries that emit CO₂—particularly considering increasing global warming regulations—carbon neutrality has emerged as a leading approach. This transition involves a shift toward green and low-carbon development. The United States has implemented numerous initiatives to curb global warming and mitigate climate change through the adoption of advanced technologies. In this context, carbon capture and storage (CCS) technologies, which capture CO₂ emissions from industrial and power plant sources, have been extensively developed and deployed. Research since 2019 indicates that, due to long-standing governmental support, the United States is home to over half of the world’s large-scale CCS facilities [26]. In support of future green energy development, Sandia National Laboratories has established a strong partnership with the renewable energy sector in New Mexico [11]. Another good instance is China, which has impressive strategies using technological advancements that were successful in a project with a capture thermal consumption of 2.35 GJ/tCO₂ and an electrical consumption of 51.5 kWh/tCO₂ over 400 consecutive days [12].

In addition, the development of sustainable and efficient systems is continuing through the expansion of sustainable renewable energy supply networks by the EU by 2050. This supply network is a multi-system based on a mixed model with the objective of maximizing the sustainable net present value, considering different renewable energy resources such as biomass, hydrogen, renewable electricity, and bioproducts for food, and using advanced types of technologies that could achieve the goal of carbon neutrality without compromising food production [13]. On the other hand, a robust life cycle assessment (LCA) has demonstrated a positive effect on avoided GHG emissions [14]. For example, optimizing hydrogen production based on California’s 2035 goals through electrified steam methane reforming with carbon capture and storage has led to an 82 percent reduction in carbon dioxide emissions [15]. Moreover, a preliminary assessment of the post-combustion capture of carbon dioxide at the San Juan Generating Station is another LCA activity in the U.S., especially in the area studied [16].

9.2. Energy History of San Juan County

San Juan County significantly contributes to New Mexico’s energy resources, particularly fossil fuels and renewable energy. The U.S. Energy Information Administration (EIA) reports that the county has abundant oil, natural gas, and coal reserves deep beneath its surface. In 2022, San Juan County produced over 80% of New Mexico’s natural gas and over 40% of its oil [27]. Figure 4 demonstrates the locations of coal mines and oil and NG basins in New Mexico [28].

In 2023, coal represented 19% of New Mexico’s total electricity generation, a significant decrease from nearly 90% two decades earlier [29]. Historically, New Mexico’s energy sector has depended significantly on coal, oil, and natural gas. As of 2010, approximately 50% of the state’s energy needs were met by coal power, which accounted for over 75% of all electricity generated in New Mexico [30].

Most of New Mexico’s coal reserves are located in the San Juan and Raton Basins, with the San Juan Basin serving as the state’s largest coal-producing region. In San Juan County, approximately 94.79% of electricity is generated from coal, while natural gas contributes 3.98% [31]. The county is home to two coal-fired power plants: the San Juan Generating Station and the Four Corners Power Plant. The San Juan Generating Station, situated near Waterflow, New Mexico—between Farmington and Shiprock—originally comprised five units [32]. Units 2 and 3 were retired in 2017, while Unit 1 was decommissioned in June 2022 [33]. Unit 4 was officially taken offline in October 2022. In 2018 and 2019, the plant generated electricity at a cost of approximately USD 45 per megawatt-hour (MWh).

The Four Corners power plant is in the Navajo Nation near Farmington City in New Mexico. It is a coal-fired facility with a capacity of 1.5 gigawatts (GW), as reported by NS Energy in 2019. This means that it can produce up to 1.5 GW of electrical power. Based on ref. [34], reported that there are currently two functioning coal-fired units out of the five that were initially planned for this power station, and they were commissioned between 1963 and 1970. Four Corners power plant units 1, 2, and 3 were permanently closed in December 2013 in preparation for decommissioning. Units 1, 2, and 3 have a combined capacity of 560 megawatts (MW). The power plant’s operational units 4 and 5, each with a capacity of 770 MW, are projected to keep producing electricity until 2031 [34].

9.3. Transition Fuel and Alternatives in New Mexico and San Juan

The development of alternative fuel is one of the most important targets of the San Juan area. While renewable energy systems have expanded, natural gas (NG) is also utilized. Below, we considered the emission of NG, methane leaks, investment in NG infrastructure, and alternative energy sources.

• Natural gas can cut CO₂ emissions by approximately 50% compared to coal. For instance, coal produces 200 pounds of CO₂ per million BTUs, while natural gas produces only 117 pounds [35].
• Globally, natural gas reserves are estimated at 6802 trillion cubic feet, ensuring a supply for approximately 50 years at current consumption rates [36].
• The current infrastructure for natural gas, including 3 million miles of pipelines globally, facilitates efficient transportation and distribution [37].
• Methane leaks, particularly during extraction and transportation, contribute significantly to global warming, as methane is 86 times more potent than CO₂ over a 20-year timeframe [38].

In addition, the responsibility for outlining the delivery of natural gas as a transitional fuel to San Juan lies with the New Mexico Gas Company (NMGC), a utility provider serving multiple counties across the state. The natural gas is sourced from El Paso Natural Gas [39]. Over the past several years, the development of renewable energy has posed a significant challenge for U.S. policymakers. During this time, political actors with ties to the oil and gas industries often worked to protect the interests of their private sector allies, frequently obstructing the advancement of renewable energy alternatives [11].

But during these years, and despite negative pressure from some policymakers, the approach of the new government changed to the development of renewable energy, especially in San Juan, New Mexico [40].

Research and practical projects, such as the New Mexico renewable development study [41], Committing to Renewables in New Mexico [42], Carbon free energy mandates for New Mexico [43], High Energy Passive Solar System Design in Santa Fe, New Mexico [44], and assessment of repurposing oil and gas wells for enhanced geothermal systems, have shown the serious intention of the government for investments in the expansion of renewable energy in New Mexico [45].

It can be added that the policymakers who were advocates of the expansion of renewable energy, and who presented designs to the government and community, struggled to create a successful, sustainable economic task force to chart a transition to a clean energy future. This shows that renewable energy development is one of the most important targets in San Juan in the future [46]. In this regard, for New Mexico, visions of the future of renewable energy based on local, state, and regional governments, non-government organizations (NGOs), public utility companies, and others have been presented [47].

However, historically, New Mexico’s energy sector has relied heavily on oil, natural gas, and coal. Based on the Energy Transition Act (ETA) in New Mexico’s counties, which became law in 2019, there is a mandate to transition to 100% carbon-free energy by 2045 for investor-owned utilities and 2050 for rural electric cooperatives, with a focus on renewable energy sources instead of coal generation. But fortunately, the San Juan generating station in New Mexico, which is a major coal-fired power plant near Farmington, is undergoing a shift from coal to renewable energy sources, and this will have a good effect in the future from an environment point of view [48,49].

In 2023, the San Juan Solar and Storage Project, located in San Juan County, New Mexico, which is interconnected to the grid, is the first phase of a larger project that is expected to deliver 400 MWac of power, and with the goal of having emissions-free electricity generation by 2040. In addition, based on the facility, it is expected to generate clean energy to power approximately 52,400 homes each year [50]. Moreover, San Juan County will have new renewable energy projects, such as the Bailer Hill microgrid project (community solar and battery storage site with 1 MW of battery storage on average, roughly powering 650 homes for 4 h) and agri-solar projects, with a 2.7 MW solar array with 5260 panels, which has impressive effects for farmers [51].

Based on a report by EIA, New Mexico ranks seventh in electricity generation from wind power. The electricity produced by wind in 2023 was 38% of New Mexico’s total electricity net generation; in comparison to 2015, it was more than seven times greater [52]. Overall, the policymakers who were advocates of the expansion of renewable energy, and who presented designs to the government and community, struggled to create a successful, sustainable economic task force to chart a just transition to a clean energy future. This shows that renewable energy development is one of the most important targets in San Juan in the future.

9.4. Broader Social and Economic Considerations Based on Impressive Aspects Such as Employment Impacts, Economic Diversification, and Community Resilience

The shift from coal to natural gas in San Juan County, especially at the Four Corners and Afton power facilities, holds major consequences for jobs, economic diversification, and community strength. The closure of coal-fired plants like the San Juan Generating Station (SJGS) has led to job losses. Approximately 450 jobs were eliminated with the closure of the SJGS [53]. However, switching to renewable energy sources and natural gas may open up new job opportunities. For example, the construction of a 450 MW solar photovoltaic plant at the SJGS site might create a substantial amount of tax money and sustain thousands of employees during construction. Economic diversification is crucial for mitigating the impacts of transitioning away from coal. The Energy Transition Act (ETA) in New Mexico aims to support this by directing investments into renewable energy projects and providing funding for economic development. The ETA allocates USD 40 million for economic development and severance packages for affected workers, and it mandates the construction of replacement power generation in San Juan County [54]. This shift towards natural gas and renewable energy sources like solar and wind energy is expected to provide more stable and sustainable economic growth compared to the boom-and-bust cycles of coal [55]. Preparing for and adjusting to changes, particularly those resulting from energy transitions, is an essential part of building community resilience. As part of its comprehensive plan update, San Juan County is creating a Climate Action Plan (CAP) to address climate change and its effects [56]. The CAP aims to reduce greenhouse gas emissions and enhance the community’s ability to withstand and recover from climate-related challenges. This includes fostering a diversified economy that is less dependent on a single industry, thereby enhancing overall community resilience [57]. These efforts collectively aim to ensure that the transition from coal to natural gas and renewable energy sources not only mitigates immediate economic impacts but also sets the foundation for a more resilient and diversified local economy.

9.5. Importance of Utilization of Natural Gas Along with Renewable Energy in the Line of Energy Sustainability and Drawing Attention to the Methane Leakage Issue

Although natural gas (NG) produces fossil fuels, removing natural gas (NG) like liquified natural gas (LNG) because it is a fossil fuel is not a good solution, because natural gas burns cleaner than other types of fossil fuels and is the most environmentally friendly fossil fuel. In power plants, and in comparison with regular oil or coal-fired power plants, natural gas emits 50 to 60 percent less carbon dioxide (CO₂); this shows that NG is the cleanest fossil fuel. Therefore, the utilization of NG rather than other fossil fuels can lead to a reduction in CO₂ [58]. Also, it can be added that burning NG is a good helper for improving air quality, because in comparison to other fossil fuels, natural gas produces negligible amounts of SO₂ and emits less CO₂ and NO_x [59]. To complete this, it can be added that the transition toward a sustainable energy future of gas-fueled solutions has many challenges from economic, social, political, technical, and geographical points of view, and these challenges should be considered comprehensively. For example, regarding the technical barriers, the lack of infrastructure, like large storage units, is a big concern for using only renewable energy sources. This means that removing NG from the cycle is not an appropriate solution. In addition, since natural gas is an important complementary transition fuel to support renewable energy (RE), it can be mentioned that NG can be a small supporter for RE in the short- and medium-term transition phases. Thus, utilization of upgraded technology in the low-carbon fossil fuel sector is an appropriate solution.

Solutions such as carbon capture and storage (CCS), the development and use of novel cycles, combined cycle gas turbines (CCGTs), and increases in the efficiency of domestic appliances are effective means of using natural gas more economically and safely. In fact, the operating costs of natural gas-based equipment are generally lower than those of other energy sources. These include combined cooling, combined cycle gas turbines (CCGTs), distributed generation (DG), heating and power (CCHP) production, and other efficient cycles. With these methods, various types of gaseous fuel can be produced, such as LNG, H2, biogas, LPG, CNG, ssLNG, and syngas [60]. Moreover, it can be emphasized that although NG has relative environmental advantages over coal, any continued use needs to be analyzed in the scope of a larger decarbonization strategy that aligns with climate objectives.

Issue of Methane Leakage, and Solutions to Reduce and Remove This Major Issue

However, the risks and challenges of methane leakage are a great GHG emissions risk for NG that should be considered. Indeed, fugitive emissions of methane are the major concern for the increase in the use of natural gas as a transitional fuel. Thus, having some estimates of methane leakage is necessary. In this regard, using advanced technology and solutions for the mitigation of methane leakage from NG, such as leak detection and repair (LDAR) programs using optical gas imaging (OGI)-based surveys, is routinely used to mitigate fugitive emissions or leaks [61,62].

9.6. A Comprehensive Discussion Regarding Energy Transition Pathways Based on Energy Transition Assessment (ETA)

While natural gas is often viewed as a “bridge fuel” for the transition, its future is complex and depends heavily on the specific pathways chosen and the pace of decarbonization.

In this regard, attention to the most important factors, such as global warming issues, electrification, policy and regulation in different countries, renewable energy integration (RE), investment, market dynamics, infrastructures, and carbon capture and storage (CCS), is important. In this regard, the role of hydrogen, particularly renewable-based hydrogen, can also be considered as an important part of the transition for reducing NG [63,64].

(a)

Importance of natural gas (NG) in transitioning.

At this moment, natural gas is one of the most appropriate of the viable options to address renewable energies’ intermittency by providing secure and reliable energy even at peak demands, with its flexible on–off cycles. This means that renewable energy technologies are important for balancing and providing help from other technologies to provide reliable energy; in this regard, NG is a suitable source, which can assist in all of the sub-categories. Achieving net zero emissions is a complex global challenge that demands innovative solutions and multidisciplinary expertise, but it is not easy. However, intelligently combining NG with renewable energy resources (RE) could be a good solution. For example, the Solar Energy Industry of America (SEIA) reported that 70% of all installed solar capacity, or 2.7 GWdc, was at utility-scale in Q3 of 2020 [65] This trend is likely to continue as the country increases its efforts to reach its CO₂ reduction targets. The more that the energy transition is delayed, the more pressing the need for utility-scale projects will become. For instance, DNV GL, which is active in Australia, is an integrated energy company producing energy from thermal power, wind power, natural gas, solar energy, hydroelectricity, gas storage, coal, and battery storage across Australia. In addition, decarbonization works differently depending on the decarbonization strategy and the circumstances. Some industries work best with alternative fuels in engines. Others use solar panels or wind energy directly. Generally, substituting fossil fuels with renewable energy is needed [65,66].

(b)

Direct and indirect effects of natural gas as a transition fuel.

Natural gas plays a pivotal role in supporting renewable energy technologies and replacing more polluting fossil fuels. Wind and solar energy sources are inherently intermittent, relying on environmental conditions that are not consistently available. For instance, solar panels can only generate electricity when sufficient sunlight is present. While energy storage solutions (such as sustainable energy carriers) and innovations in grid infrastructure offer potential remedies to this intermittency, these technologies are not yet consistently viable from commercial, technological, or environmental standpoints. Another challenge is the high costs of renewable energy sources. Natural gas stands out as a transition fuel because of its economic viability compared to emerging renewable technologies and its less polluting effects compared to other fossil fuels. Overall, natural gas can directly affect the energy transition positively by helping renewables by providing uninterrupted energy and reducing emissions by replacing coal. According to a 2014 report, coal accounted for 41.1% of global electricity generation, while natural gas contributed 21.92%, and renewables only 6.1%. Redirecting investment from coal to natural gas has the potential to yield significant environmental benefits. As natural gas increasingly replaces coal, global annual emissions could be substantially reduced. In the longer term, zero-carbon technologies can gradually replace natural gas as countries move toward climate neutrality. Intermittency refers to the characteristic of providing variable energy outputs. Wind and solar power produce energy when there is resource availability. Generated but unused renewable energy cannot be utilized unless it is stored or transported. Furthermore, abrupt fluctuations in resource availability directly affect the electricity generation output throughout the day. This makes it even harder to anticipate the electricity generation potential, to ensure the grids’ stability, and thus to satisfy electricity demand reliably. Natural gas plants, however, can be levelled to an energy demand if needed. Thermal plants can reduce their electricity generation levels to secure the grid’s stability when the energy demand drops. Hence, natural gas plants can accompany renewable plants to balance the intermittent electricity generation. Therefore, an appropriate energy policy aiming towards a low-carbon energy system should consider the composition of “natural gas + renewable energies + energy efficiency” [60,67,68].

(c)

The responsibility of natural gas as a transition fuel is well-defined in the literature.

Moreover, the role of natural gas as a transition fuel is well established in the literature: natural gas can be effectively integrated with renewable technologies until they become a viable option for providing affordable and reliable energy. This synergy between natural gas and renewables can alleviate the societal and infrastructural burdens associated with energy transition. However, crowding-out effects may also occur due to both technological evolution and social dynamics, such as user adoption patterns and institutional inertia. If natural gas continually redirects resources because of its growing socio-technical influence, it may inhibit the development of renewable technologies and reinforce a lock-in effect favoring fossil fuel-based systems. Natural gas is typically regarded as a bridging fuel that facilitates the shift from high-emission fossil fuels to zero-carbon technologies. Natural gas plants are expected to be utilized only until zero-carbon alternatives become commercially viable, given that natural gas combustion still results in greenhouse gas emissions. Consequently, investments in natural gas represent a short- to medium-term solution for the energy transition. As emissions reduction targets become more stringent, investment priorities will need to shift accordingly. Overall, natural gas provides an affordable transition pathway in the short term but may increase the costs of transitioning to zero-carbon technologies in the medium and long term, depending on the timing of the transition. In contrast, an immediate shift to renewables typically results in higher electricity generation costs in the short term. As a mature technology, natural gas has undergone more extensive evolutionary development compared to renewables, enabling it to directly support renewable integration through functions such as balancing intermittency, ensuring a reliable energy supply, and offering cost-effective investments and consumer prices. Without such support from the transition of fuel, the restructuring of energy systems toward renewables could become infeasible, prohibitively expensive, and, critically, significantly delayed. However, taking advantage of a transition fuel also comes with challenges. Initial investments in a potential transition fuel such as natural gas could lock out emerging renewable technologies for extended periods. Technology lock-ins occur as distant and delayed responses from the system and amplify the complexity of transition management [69,70].

(d)

The role of renewable energy sources in reducing NG in transitioning

To achieve carbon neutrality and meet climate reduction targets, transitioning to sustainable renewable energy supply networks is essential for countries worldwide to reach net-zero carbon emissions by 2050. Wind turbines and solar photovoltaics are two of the most promising technologies with high potential to generate the necessary electricity. These renewable energy sources can serve as effective alternatives to fossil fuels, particularly natural gas [71]. For example, the share of energy from renewable energy sources (RESs) in the transport sector, as one of the biggest sectors consuming energy, reached 8.9% in 2019. Also, the share of energy from RESs in final electricity is 34% and 22%, respectively, for heating and cooling consumption in Jordan [72]. It is predicted that the share of RESs for electricity generation will increase from 0.1 TWh in 2015 to 110.7 TWh in 2050, with a solar photovoltaic share of 92% [73]. In this regard, different countries have many plans to increase the share of RESs for the future. In a country like Brazil, considering the estimated electric energy demand for this country in 2025 at 800 TWh will result in a contribution of 12.3% of the energy by solar photovoltaic sources [74]. As well, many other countries like Turkey [75], Denmark [76], India [77], South Africa [78] etc., have a serious plan to increase producing electricity by RESs; this means that in the future, the consumption of NG will be reduced in these countries and whole of world if RESs are selected as alternative fuels instead of fossil fuels.

10. Limitations and Future Research

Our work in the future will include a more complex analysis than linear regression; our reliance on linear regression can be explained clearly and precisely by the non-linearity of the underlying environmental and economic impacts of the energy transition from coal to NG. This would be interesting and effective for all readers who are investigating this work in the first stage. The method and analysis should cover all the testing around model stability, heteroskedasticity, outliers, and a misspecification test, considering the dynamics of data over time.

Assessing the broader economic impact beyond energy prices, including impacts on longer-term energy supply, changes in fuel prices, and the impact of related policies, will provide a better understanding of the energy transition. In addition, incorporating external parameters (i.e., seasonality and regulation) into future work may provide added rigor and immediate real-world applicability.

In addition, considering key factors such as economic, social, and environmental factors would be significant for the area studied, which we will present in our future work.

11. Conclusions

This study assessed the potential impacts of transitioning from coal to natural gas (NG) for electricity generation in San Juan County, New Mexico, by analyzing data from the Four Corners (coal-fired) and Afton (natural gas-fired) power plants. The primary focus was on combustion-phase water consumption and emissions, specifically CO₂, NO_x, and SO₂. Although this analysis is limited to these two facilities, they serve as representative examples within the county’s energy infrastructure, providing insights into broader trends related to energy consumption and environmental effects associated with fuel switching in the region.

Water usage.

Regression and scenario-based analyses indicate that replacing coal with natural gas could yield significant water savings. The greatest estimated reduction is approximately 2750 gallons per megawatt-hour (MWh) when comparing a fully coal-powered scenario to one powered entirely by natural gas. Valued at regional water prices, this reduction translates to an economic saving of approximately USD 0.74 per MWh. These findings highlight the potential for water conservation in arid regions under specific fuel transition scenarios, assuming similar operational conditions.

CO₂ emissions.

The analysis further suggests that a complete transition to natural gas could reduce CO₂ emissions by up to 0.61 metric tons per MWh. Applying conservative valuation estimates, this reduction corresponds to a financial benefit of about USD 61.26 per MWh. These results reflect the emissions profiles of the two power plants studied and indicate that fuel switching may contribute to emissions mitigation goals when implemented under comparable conditions.

NO_x emissions.

A reduction of up to 0.00179 metric tons of NO_x per MWh was also observed in the full transition scenario. The associated economic benefit of this reduction is estimated at USD 14.61 per MWh. Reducing NO_x emissions can improve air quality and decrease ground-level ozone formation, potentially providing indirect public health benefits. However, these outcomes are specific to the plant configurations and assumptions employed in this analysis.

SO₂ emissions.

The transition from coal to natural gas (NG) leads to a maximum reduction of 0.000611 metric tons of SO₂ emissions per megawatt-hour (MWh), which corresponds to an estimated economic benefit of USD 11.91 per MWh. Reduced SO₂ emissions contribute to lower acid deposition and diminished ecological damage, potentially yielding health benefits as well. Although this analysis does not model the downstream effects, it underscores the potential advantages associated with decreased SO₂ emissions from combustion. Beyond combustion-related impacts, this study also evaluates upstream resource use and emissions during fuel extraction, as summarized in Table 19. For a generation scenario producing 1000 MWh, coal mining (requiring 500 metric tons of coal) was associated with consumption of 0.2642 million gallons of water and emissions of 1200 metric tons of NO_x, 1600 metric tons of SO₂, and 900,000 metric tons of CO₂. In contrast, natural gas production to generate the same energy output (using 200 metric tons of NG) consumed 0.1321 million gallons of water and emitted 300 metric tons of NO_x and 450,000 metric tons of CO₂. Data on SO₂ emissions from NG extraction were unavailable. This study provides a scenario-based assessment of the environmental and economic trade-offs involved in transitioning from coal to natural gas within a specific region. The findings are based on historical operational data and assume consistent plant-level characteristics. While the results suggest potential reductions in water use and emissions, they do not advocate for a universal energy pathway. Instead, the objective is to inform regional energy planning by presenting quantified outcomes derived from clearly defined data and modeling assumptions.

• Explanations for Appendix A

As part of our data cleaning process, we identified and removed a few zero values from the relevant variable before modeling. However, we did not encounter any nega-tive values in our dataset. To ensure the robustness of our analysis, we conducted thorough data validation and diagnostic checks, including tests for misspecification, stability, and heteroskedasticity as can be seen in the

Table 20. Breusch–Pagan test results for four models of the coal dataset.

Model	Breusch–Pagan Test Value	p-Value
Generation~Coal + Water	BP = 3.514	p-value = 0.07856
CO2~Generation	BP = 2.367	p-value = 0.13724
SO2~Generation	BP = 4.783	p-value = 0.05946
NOx~Generation	BP = 5.231	p-value = 0.05156

and

Table 21. Breusch–Pagan test results for four models of the natural gas dataset.

Model	Breusch–Pagan Test Value	p-Value
Generation~Fuel + Water	BP = 3.250	p-value = 0.0834
CO2~Generation	BP = 3.4006	p-value = 0.0651
SO2~Generation	BP = 3.4152	p-value = 0.0646
NOx~Generation	BP = 1.0499	p-value = 0.3055

. Our findings confirmed that none of these assumptions were violated. In response to the reviewer’s suggestion, we have summarized these steps and provided the corresponding diagnostic plots for transparency. Residuals vs. Fitted: Checks the linearity assumption and identifies potential non-linearity in the data (Appendix A Figure A1, Figure A2, Figure A3, Figure A4, Figure A5 and Figure A6).

Normal Q-Q: Assesses whether the residuals are normally distributed, a key assumption for many regression models.

Scale Location: Checks the assumption of homoscedasticity (constant variance of residuals).

Cook’s Distance: Identifies outlier data points that could disproportionately affect the model.

We have created visualizations comparing observed values with predicted values from our models. The plotted points closely align with the fitted line, indicating that the model predictions are accurate. In response to the reviewer’s suggestion, we have included these plots to clearly illustrate the agreement between predicted and observed values. These visualizations provide transparency and help assess the model’s fitness. The corresponding plots can be found below for reference.

In addition, Breusch–Pagan tests for all four models for the natural gas dataset yielded p-values greater than 0.05, indicating no evidence of heteroscedasticity. This supports the earlier diagnostic plot results, suggesting that the assumption of constant residual variance is reasonable for these models. The limitations section puts non-linear analysis in the bucket of future work and does not talk about potential non-linearity in the current data set.

Breusch–Pagan tests for all four models of the coal dataset yielded p-values greater than 0.05, indicating no evidence of heteroscedasticity. This supports the earlier diagnostic plot results, suggesting that the assumption of constant residual variance is reasonable for these models.