Evaluation of Socioeconomic Dynamics and Their Impact on Life Expectancy in Coal Mining Communities in Colombia

Abstract

The study evaluates the socioeconomic dynamics and their impact on life expectancy in coal mining communities in Colombia, aligning with the Sustainable Development Goals (SDGs), assessing the relationship between production, occupation, accident rates, mortality and royalties. Univariate, bivariate, path analysis and ARIMA models were used to identify patterns and projections. The results show a positive constraint between coal production and royalties, which is negative with the occupation, accident, and mortality rates. Despite the revenues generated, no improvements in the quality of life of mining communities are observed; the poor use of royalties reflects a lack of effective strategies to convert mining revenues into sustainable enhancements for local communities. It highlights the rush for more effective public policies to ensure that economic benefits are aligned with improvements in communities’ health, safety and quality of life. In the future, greater alignment with the SDGs, particularly SDG 3 (Health and Well-being) and SDG 8 (Decent Work and Economic Growth), will depend on a sustainable approach that prioritises investment in social infrastructure and the equitable distribution of resources derived from mining, thereby addressing current disparities.

1. Introduction

The coal industry in Colombia dates back to the 19th century when coal deposits began to be exploited to meet the domestic energy demand of that time. During the 20th century, production increased significantly, especially in the 1980s when significant reserves were discovered in departments such as La Guajira and Cesar. These areas became strategic centres for coal mining, boosting both regional and national economies. In recent years, Colombia has consolidated its position as one of the world’s leading coal exporters, standing out for the quality of its coal [1].

Coal mining has significant economic and social importance. Economically, it is one of the main sources of export revenue, contributing substantially to the national GDP. Departments such as La Guajira and Cesar have experienced development due to mining, creating jobs and improving local infrastructure. While the coal industry has improved living conditions in these areas, it has also generated challenges, such as community displacement and environmental concerns. Internationally, coal mining positions Colombia as a key player in the energy market, although it faces the need to balance economic development with environmental sustainability [1].

The main coal-producing regions are located in the north of the country, particularly in the departments of La Guajira and Cesar. La Guajira is home to Cerrejón, one of the largest open-pit coal mines in the world, operated by a joint venture between BHP, Glencore, and Anglo American. In Cesar, companies like Drummond and Prodeco play a significant role in coal extraction. These companies also play a crucial role in regional economic development, providing economic benefits through royalties, generating employment, and improving infrastructure [2]. To ensure that coal exploitation in these regions generates tangible benefits for the country, it is crucial to consider how royalties from this activity are managed.

In Colombia, royalties emerged from the need to ensure fair compensation for the exploitation of natural resources, recognising that these resources belong to the nation and should be used for the benefit of society. The 1991 Constitution established the general system of royalties, stating that natural resources are the property of the Colombian state and their exploitation must generate benefits for the country. This principle was developed through Law 141 of 1994, which established the General System of Royalties and has been modified several times to optimise the distribution and use of these resources [3]. Royalties are significant, as they represent a mechanism for distributing the benefits of natural resource exploitation across all regions of the country, particularly those directly impacted by mining and extraction. The laws establish the funding of regional development projects, public infrastructure improvement, and support for social programmes in health, education, and basic sanitation, but the effectiveness of this mechanism depends on the proper management and distribution of resources.

The General System of Royalties (SGR) is the mechanism responsible for collecting, managing, and distributing these resources. Funds from royalties are allocated to different levels of government and sectors, seeking equitable distribution. A portion of the royalties goes directly to the producing departments and municipalities, while another portion is distributed across the rest of the country to finance regional investment projects. The collected resources are allocated to specific funds, such as the Science, Technology, and Innovation Fund, promoting sustainable development and economic diversification in the country. However, the royalties system has faced several challenges, including corruption, poor management, and a lack of administrative capacity in some territories to execute projects funded by royalties. This has often led to an ineffective use of resources in improving the quality of life of communities affected by mining. Although reforms have been implemented to improve transparency and efficiency in the use of royalties, significant challenges remain. This highlights the need to rethink how royalty income can be better aligned with sustainable development goals.

While this economic activity contributes significantly to the country’s gross domestic product (GDP) and impacts the health and well-being of communities, it is essential to analyse how this activity can align with the Sustainable Development Goals (SDGs), which include the responsible management of natural resources [4]. The sustainable development of mining communities is a central aspect of the global agenda; these populations face socioeconomic challenges such as unemployment, poverty, lack of access to basic services, and environmental degradation. Therefore, joint work between mining companies and governments is vital to ensure that communities benefit fairly and equitably. This includes job creation, infrastructure development, education improvement, health promotion, and citizen participation [5].

In Colombia, royalties compensate for the extraction of natural resources, but their impact on mining areas has been mixed. Although the goal is to support regional development and improve infrastructure in health and education, the funds have not been used effectively to transform the quality of life of mining communities. Despite significant revenues, poor management and an inadequate allocation of resources have limited improvements in essential services, maintaining precarious conditions and having little impact on the well-being of mining regions.

This research aims to evaluate the relationship between life expectancy, employment rate, accident rate, mortality, mining production, and royalties in the context of coal mining in Colombia. From this premise, the following research question arises: how does coal production, the employment rate, accident rate, and royalties influence the life expectancy of mining communities in Colombia and what public policies could align with the Sustainable Development Goals (SDGs) to improve their well-being?

To answer this question, the statistical technique of path analysis will be used, which provides a robust methodology to explore and understand complex interactions between multiple variables. This technique allows us to decompose relationships into direct and indirect effects and to identify mediators and moderators to quantify impacts. The aim is to provide a comprehensive assessment that serves as a basis for creating more sustainable and effective public policies.

2. Materials and Methods

To evaluate the relationships and effects of coal mining in 10 departments of Colombia, we began with a univariate analysis that provided detailed descriptive statistics for each variable. Subsequently, a bivariate analysis was performed to identify significant correlations between pairs of variables. Path analyses were proposed to decompose the relationships between variables into direct and indirect effects. This allowed for the precise quantification of the impacts on life expectancy, employment rate, accident rate, mortality, production, and royalties. Pearson’s correlation coefficient was calculated, and the strength and direction of linear relationships between quantitative variables were evaluated. ARIMA models were established to identify temporal patterns and project future scenarios. Significant temporal relationships were modelled from an autocorrelation function analysis (ACF) and partial autocorrelation (PACF). Finally, the unit root test was applied to confirm the stationarity of the time series. The methodology is illustrated in Figure 1.

2.1. Type of Research

Quantitative research was conducted based on the collection and analysis of numerical data to establish relationships and patterns between variables, allowing us to measure and analyse variables objectively through the collection of numerical data, which were subjected to statistical tests, seeking to identify causal and correlational relationships between variables.

2.2. Data

Data were collected from the indicators of the National Administrative Department of Statistics (DANE), the Mining and Energy Planning Unit (UPME), and the National Mining Agency (ANM) in Colombia. For this study, 10 departments were selected due to their relevance in coal mining and the availability of reliable data, namely Antioquia, Boyacá, Cauca, Cesar, Cundinamarca, Córdoba, Guajira, Norte de Santander, Santander, and Valle del Cauca. These departments represent regions with significant mining activity and concentrate much of coal production, royalties, employment, and other socioeconomic and environmental indicators. Six variables were used in the study, with 1320 data records for each distributed in these departments, which allowed us to understand the impacts generated by coal mining.

Table 1. Study variables.

No	Variable Name	Identification	Source
1	Coal Production	X1	UPME
2	Coal Royalties	X2	UPME
3	Occupation Rate in Coal Mining	X3	DANE
4	Number of Accidents in Coal Mining	X4	ANM
5	Mortality in Coal Mining	X5	ANM
6	Life Expectancy	X6	DANE

shows the distribution of variables.

2.3. Pearson’s Correlation Coefficient

For this research, the statistical measure of Pearson’s correlation coefficient was used, which evaluates the strength and direction of the linear relationship between quantitative variables, identifying the linear existence between the variables described in Table 1. Equation (1) establishes the Pearson correlation [6,7,8].

$$\mathrm{r}={\displaystyle \frac{\mathrm{n}\left(\sum \mathrm{xy}\right) - \left(\sum \mathrm{x}\right)\left(\sum \mathrm{y}\right)}{\sqrt{\left[\mathrm{n}(\sum {\mathrm{x})}^2 - {\left(\sum \mathrm{x}\right)}^2\right]\left[\mathrm{n}\sum {\mathrm{y}}^2 - {\left(\sum \mathrm{y}\right)}^2\right]}}}$$

(1)

where the variables are as follows:

• n, number of data pairs;
• $\left(\sum \mathrm{xy}\right)$, sum of the product of each data pair;
• $\left(\sum \mathrm{x}\right)\mathrm{and}\left(\sum \mathrm{y}\right)$, sums of the individual values of x and y;
• ($\sum {\mathrm{x}}^2) \mathrm{and}{\left(\sum \mathrm{x}\right)}^2$, sums of squares of the individual x and y values.

For the case study, a value of r close to +1 indicates a strong positive relationship, which means that as one variable increases, the other also increases; a value of r close to −1 indicates a strong negative relationship, which means that as one variable increases, the other decreases; and a value of r close to 0 indicates a weak or non-existent relationship between the variables. The correlation between the following variables is established for the 10 departments studied: life expectancy, employment rate, accident rate, mortality, production, and royalties in coal mining.

2.4. Trajectory Analysis

For the analysis, we started from a theoretical model based on previous hypotheses on how these variables could be related.

• Life expectancy could be negatively influenced by coal mining accidents and mortality.
• Coal mining production could be positively related to royalties and the occupancy rate.
• Royalties could positively influence life expectancy through better investments in health and safety.

Subsequently, the statistical model was specified, indicating the direct relationships (causal hypotheses) between the variables; a complete path model was proposed that included all the hypothesised relationships between the variables and the structural equations that represent these relationships.

Life Expectancy = β1 * Occupancy Rate + β2 * Accident Rate + β3 * Mortality + β4 * Production + β5 * Royalties

(2)

Production = β6 * Occupancy Rate + β7 * Accident rate

(3)

Royalties = β8 * Production.

(4)

SPSS-AMOS 23 statistical software was used to estimate the trajectory analysis model. The coefficients of the relationships specified in the model were calculated (for this case, β1, β2, β3, etc.) and the strength and significance of these relationships were evaluated. The different types of effects on the study variables (total, non-causal, indirect, direct) were studied [9,10].

2.5. Autocorrelation Function (ACF) and Partial Autocorrelation Function (PACF)

The ACF and PACF were incorporated to model the temporal relationships between variables. These models allow for the inclusion of independent variables (employment rate, accident rate, mortality, production, and royalties) that affect the dependent variable (life expectancy) over time [11].

The ACF identifies temporal patterns that can influence the progress of the Sustainable Development Goals (SDGs) in Colombia, quantifying the direct and indirect effects of each independent variable. The application of the PACF made it possible to identify direct and significant relationships between the values of the time series in different (partial) periods for the different variables analysed and the departments studied [12,13].

2.6. Autoregressive Integrated Moving Average (ARIMA) Process

The ARIMA model was used to evaluate the temporal dynamics of the variables studied, such as the occupation rate, accident rate, mortality, production, and royalties, as well as their impact on life expectancy in Colombia’s coal mining context. Using the autocorrelation (ACF) and partial autocorrelation (PACF) function graphs, temporal patterns were identified that allow for the determination of the appropriate parameters for the model and the establishment of the order of the autoregressive (AR) model, the number of differences required to make the series stationary, and the order of the moving average (MA) model [14] (pp. 80–85).

The model made it possible to evaluate the future impact of the independent variables on the dependent variable (life expectancy), projecting different scenarios for the Sustainable Development Goals (SDGs). For example, changes in coal production or accident rates were identified as affecting life expectancy in mining communities.

With the ARIMA model, the lag value was selected through the autocorrelation function plot (ACF) and the partial autocorrelation function (PACF); with the PACF plot, the lag was chosen for the autoregressive model (p) and with the ACF, the lag was selected for the moving average model (q). Once these parameters were established, a unit root test was performed to confirm that the time series was stationary [15].

3. Results

3.1. Univariate Analysis

Table 2. Descriptive statistics for predictor and response variables.

Variable Name	Medan	Median	Maximum Value	Minimum Value	Standard Deviation	Standard Error of Mean	1 Quartile	3 Quartiles
Coal Production (ton)	7,974,393.84	1,235,639.07	52,025,244.00	4063.13	14,838,438.66	1,414,789.61	101,231.75	2,702,634.94
Coal Royalties (millions)	203,778.39	7807.38	4,875,973.19	16.94	572,513.55	54,587.02	582.91	30,089.01
Coal Life Expectancy (Years)	75.53	76.04	76.75	72.83	1.39	0.13	74.77	76.65
Occupation Rate (thousands)	10.58	7.05	65.1	1	11.94	1.14	4	10.01
Number of Coal Accidents	5.38	2	26	0	6.96	0.66	0	8
Coal Mortality (%)	20.95	1	100	0	33.63	3.21	0	41.26

shows the descriptive statistical values of the study variables during 2012–2022, discriminated by months. The values include the mean, median, maximum value, minimum value, standard error of the mean, and standard deviation of each variable. The variables analysed are production, royalties, life expectancy, the occupation rate, accidents, and mortality in coal mining [16] (pp. 1–6).

Coal production shows high variability, reflected in a significant standard deviation, indicating notable monthly fluctuations throughout the period studied. The median is considerably lower than the mean, evidencing a distribution skewed to the right, with several high values influencing the mean. Coal royalties also exhibit high variability, with a mean greater than the median, indicating the presence of extremely high values that raise the mean. The significant difference between quartiles points to a dispersed distribution of royalty values.

The low standard deviation shows that life expectancy has a relatively low variability. The mean and median are close, suggesting a symmetrical distribution of the data, with most values concentrated between the first and third quartiles and a slight decrease in recent years. The occupancy rate exhibits a high dispersion of data with a significant standard deviation. The median is relatively low compared to the mean, indicating the presence of some very high values influencing the mean, with most values in the lower quartiles. The accidents and mortality in coal mining show high variability, with high standard deviations and means significantly higher than the medians, reflecting skewed distributions with some high values affecting the means.

3.2. Bivariate Analysis

Table 3. Pearson’s correlation coefficient between variables.

Variable Name	x1	x2	x3	x4	x5	x6
Prod Carbon /Ton (x1)	1	0.665	−0.242	−0.250	−0.217	0.083
Carbon Royalties (millions) (x2)		1	−0.157	−0.178	−0.184	−0.150
Coal Occupancy Rate (thousands) (x3)			1	0.377	−0.065	−0.179
Number of Coal Accidents (x4)				1	0.368	0.145
Coal Mortality (%) (x5)					1	0.272
Life Expectancy (years) (x6)						1

presents the results of Pearson’s correlation coefficient between the variables, providing the linear relationships between the factors considered in coal mining in Colombia. It is highlighted that coal production (x₁) has a moderate positive correlation with royalties (x₂) (r = 0.665), suggesting that as production increases, so do royalties; however, production has negative correlations with the occupation rate (r = −0.242), accident rate (r = −0.250), and mortality in coal mining (r = −0.217), indicating that as production increases, there is a tendency to decrease these factors [17] (pp. 118–122).

Life expectancy (x₆) shows a moderate positive correlation with mortality in mining (r = 0.272) and a low correlation with the accident rate (r = 0.145), reflecting the influence of labour and health conditions on the lifespan of workers in this sector. The occupancy rate has a positive correlation with the accident rate (r = 0.377), which is significant for understanding how employment conditions in the sector can impact occupational safety.

3.3. Path Coefficient Analysis

The path coefficient divides the variables into two groups (exogenous and endogenous). It is used to disintegrate the bivariate analysis into total, non-causal, direct, and indirect effects [18].

• Exogenous group:
  • x₁ = Coal production.
  • x₂ = Coal Royalties.
  • x₃ = Coal Occupational Rate.
  • x₄ = No Coal Accident Rate.
• Endogenous group:
  • x₅ = Mortality.
• Dependent variable:
  • x₆ = Life expectancy.

$${\mathrm{x}}_5={\mathrm{Q}}_{51}{\mathrm{x}}_1+{\mathrm{Q}}_{52}{\mathrm{x}}_2+{\mathrm{Q}}_{53}{\mathrm{x}}_3+{\mathrm{Q}}_{54}{\mathrm{x}}_4+{\mathrm{Q}}_{5\mathrm{U}}{\mathrm{R}}_{\mathrm{U}}$$

(5)

$${\mathrm{x}}_6={\mathrm{Q}}_{61}{\mathrm{x}}_1+{\mathrm{Q}}_{62}{\mathrm{x}}_2+{\mathrm{Q}}_{63}{\mathrm{x}}_3+{\mathrm{Q}}_{64}{\mathrm{x}}_4+{\mathrm{Q}}_{65}{\mathrm{x}}_5+{\mathrm{Q}}_{6\mathrm{V}}{\mathrm{R}}_{\mathrm{V}}$$

(6)

The path coefficients are represented as Q_ij (where i = 5, 6 and j = 1, 2, 3, 4, 5). Q_5UR_U and Q_6VR_V denote the disturbances, which are mutually independent of each other and concerning their predictor variables. The residual can also be calculated from the regression equation using the coefficient of determination

$$\sqrt{1-{ \mathrm{R}}^2}$$

(7)

Path coefficient analysis evaluates total and non-causal effects, including direct and indirect ones. The path coefficients (defined in regression Equations (5) and (6)) represent the direct effect of the factors and are determined through the least squares regression process [18,19,20].

From linear Equations (5) and (6) for the route model, Equations (8) and (9) are derived as follows:

$$\begin{gathered} {\mathrm{x}}_5=-0.134{\mathrm{x}}_1-0.061{\mathrm{x}}_2-0.267{\mathrm{x}}_3+0.425{\mathrm{x}}_4+{\mathrm{Q}}_{5\mathrm{u}}{\mathrm{R}}_{\mathrm{u}} \\ {\mathrm{R}}_{5.1234}^2=0.220759701812226 \end{gathered}$$

(8)

$$\begin{gathered} {\mathrm{x}}_6=0.355{\mathrm{x}}_1-0.349{\mathrm{x}}_2-0.197{\mathrm{x}}_3+0.169{\mathrm{x}}_4+0.210{\mathrm{x}}_5+{\mathrm{Q}}_{6\mathrm{V}}{\mathrm{R}}_{\mathrm{V}} \\ {\mathrm{R}}_{6.12345}^2=0.156473086457662 \end{gathered}$$

(9)

For the analysis of the path diagram (Figure 2), the dependent variable, life expectancy (x₆), is taken, evaluating the dominance of exogenous and endogenous factors within the path model. Coal production (x₁) positively impacts life expectancy (Q₆₁ = 0.355), indicating that the higher the production, the higher the life expectancy, and the higher the production, the better the working conditions. Coal royalties (x₂) have a negative relationship (Q₆₂ = −0.349). The income generated by royalties is not reflected in significant improvements in the quality of life or health of the population.

The occupational rate in the coal sector (x₃) has a negative impact (Q₆₃ = −0.197) on life expectancy, indicating that higher employment rates in this sector are associated with adverse working conditions that negatively affect long-term health. Accidentality in mining (x₄) shows a positive influence (Q₆₄ = 0.169), reflecting that safety measures implemented to reduce accidents are indirectly improving life conditions and life expectancy.

Mortality in the coal sector (x₅) contributes positively (Q₆₅ = 0.210) to life expectancy; this record is related to an improvement in the identification and treatment of the causes, represented in improvements in life expectancy in the population.

From the analysis of the path coefficients, direct, indirect, total, and non-causal effects were obtained, all of which are shown in

Table 4. Effects of the independent variables on life expectancy.

Endogenous Variable	Exogenous Variable	Total Effect	Non-Causal Effect	Indirect Effect	Direct Effect	Total Association
X5	x1	−0.134	0.083		−0.134	−0.217
	x2	−0.061	0.123		−0.061	−0.184
	x3	−0.267	−0.202		−0.267	−0.065
	x4	0.425	0.057		0.425	0.368
X6	x1	0.327	0.244	−0.028	0.355	0.083
	x2	−0.362	−0.212	−0.013	−0.349	−0.150
	x3	−0.253	−0.074	−0.056	−0.197	−0.179
	x4	0.258	0.113	0.089	0.169	0.145
	x5	0.210	−0.062		0.21	0.272

.

The effects of exogenous variables on life expectancy (x₆) show a complex and diverse interaction. Coal production (x₁) has a positive total effect (0.327), driven mainly by a strong direct effect (0.355). This is partially offset by a negative indirect effect, indicating that coal production may initially improve life expectancy, but negative indirect effects, such as environmental deterioration or adverse labour conditions, mitigate this benefit in the long run.

Coal royalties (x₂) and the occupational rate (x₃) have negative total effects (−0.362 and −0.253, respectively); the economic importance of these variables contributes to conditions that reduce life expectancy, possibly due to an inefficient distribution of benefits or an intensification of hazardous working conditions. The coal accident rate (x₄) has a positive effect (0.258), showing improvements in safety and accident reduction and increasing life expectancy.

3.4. First Difference Time Series

The time series analysis in this study focuses exclusively on the production, occupation, and accident rate variables due to the patterns of effects and associations between the variables (Table 4). Coal production (x₁) in relation to life expectancy (x₆) has a positive total effect of 0.327, developed mainly by a positive direct effect of 0.355, although with a small negative indirect effect (−0.028), observing that production significantly impacts life expectancy, justifying its inclusion in the temporal analysis to assess its variability and impact on the welfare of communities. The coal mining occupation rate (x₃) presents a negative total effect on life expectancy (−0.253) due to a significant negative direct effect (−0.197). Although the non-causal effects are minor, this variable is crucial to understanding how working conditions influence workers’ health, highlighting the importance of analysing its temporal evolution. The accident rate (x₄) is one of the few variables with a positive total effect on life expectancy (0.258), with a positive direct effect of 0.169. These improvements in safety and the reduction in accidents contribute to a higher life expectancy, being essential to analyse this variable over time and evaluate the effectiveness of implemented safety policies. These variables allow us to capture the most relevant dynamics for the well-being and safety of mining communities in Colombia [21] (pp. 171–179).

Figure 3, a differentiated time series of coal production from 2008 to 2022, shows how production has fluctuated over the years. Between 2008 and 2012, fluctuations were slight, followed by a significant increase from 2012 to 2014. Between 2014 and 2018, fluctuations occurred with a general downward trend. Production in 2019 dropped abruptly, followed by a recovery in 2020 and then another drop in 2021 and 2022. These fluctuations and abrupt changes may be related to external factors such as changes in demand, government policies, and operational or economic problems. The increase in 2020 is attributed to a recovery process after a significant pause, probably caused by the COVID-19 pandemic crisis.

Figure 4 shows several fluctuations in the differenced time series of the coal mining employment rate from 2012 to 2022. A significant increase occurred around 2015, followed by a sharp drop in 2016. Between 2017 and 2018, the occupancy rate stabilised with minor fluctuations, and from 2019 to 2022, there was a gradual increase with some fluctuations. The significant variation in 2015–2016 responds to modifications in the industry, such as the implementation of new labour policies, fluctuations in coal demand, or alterations in economic conditions. The gradual increase in recent years indicates a recovery of the sector or growth in the mining industry.

Figure 5 shows significant fluctuations in the time series of the number of accidents in coal mining from 2012 to 2022. There is an increase in accidents around 2016 and 2018, followed by steep drops. Between 2019 and 2020, there is a gradual increase in accidents. Spikes in accidents are associated with periods of increased mining activity or the implementation of new technologies or practises that would have initially increased risk. Sharp drops may reflect improvements in safety measures or changes in mining operations that reduce accidents. The gradual increase in recent years suggests the need to continue to monitor and improve safety conditions at mines.

3.5. Selection of Appropriate Model

The unit root test reveals significant values after taking the first difference. To choose the best ARIMA model for the data, the lag value is selected by plotting the autocorrelation function (ACF) and the partial autocorrelation function (PACF). The PACF and ACF plots after taking the first difference are plotted for production (Figure 6), occupancy (Figure 7) and accidents (Figure 8) [22].

Figure 6, the ACF and PACF from the first difference in coal mining production data in Colombia, shows significant correlations at several lags in the ACF, with some values outside confidence limits, showing a persistent influence of past values on current production values. In the PACF, only a few lags have significant partial correlations, showing that the influence of past values decreases significantly after the first few lags. This reveals that changes in output in one month are correlated with changes in output in earlier months and that the dependence is mainly concentrated in the first few lags. This pattern is typical in time series suitable for modelling by an ARIMA model, where the first lags are crucial for predicting future values. This analysis is critical in determining the appropriate parameters (p and q) when fitting an ARIMA model to coal production data.

Figure 7, the ACF and PACF of the first difference in coal mining occupation data in Colombia, shows several significant correlations at different lags, with some autocorrelation values outside confidence limits, reflecting significant autocorrelations at certain lags. The PACF reveals that a few lags have significant partial correlations, showing that the influence of the previous values decreases significantly after a few lags. Significant correlations in several lags in the ACF demonstrate that past values of the occupation time series have a persistent influence on current values, showing that fluctuations in the occupation rate in one period correlate with fluctuations in previous periods. The PACF shows the correlation of the time series with its lags, eliminating the influence of intermediate lags; the significance of a few lags in the PACF confirms that once the influence of closer lags is taken into account, the correlation with more distant lags decreases rapidly. This analysis reveals a significant dependence on lagged values and is critical in determining the appropriate parameters (p and q) when fitting an ARIMA model to coal mining occupancy data.

Figure 8, the ACF and PACF of the first difference in the Colombian coal mining accident data, presents several significant correlations at different lags, with some autocorrelation values outside confidence limits, reflecting significant autocorrelations at certain lags. The PACF reveals that several lags have significant partial correlations, indicating that the influence of previous values decreases significantly after a few lags. The presence of significant correlations in several lags in the ACF confirms that past values of the accident time series have a persistent influence on current values, showing that changes in the number of accidents in one period are correlated with changes in previous periods. The PACF evidences the correlation of the time series with its lags, eliminating the influence of intermediate lags; the significance of various lags in the PACF demonstrates that once the impact of closer lags is taken into account, the correlation with more distant lags decreases rapidly. This analysis reveals a significant dependence on lagged values and is critical in determining the appropriate parameters (p and q) when fitting an ARIMA model to coal mining accident data.

3.6. AIC Test for Different Ordered Models

The Akaike information criterion (AIC) check for different ARIMA models (p,d,q) concerns three variables, namely production, occupancy, and accidentality in coal mining in Colombia. It aims to identify the ARIMA model that best fits each dataset based on the lowest AIC value. For production, the ARIMA (1,0,0) model presents the lowest AIC value (34.96), showing that it is the best fit and most simple for the coal production time series. Regarding occupancy, the ARIMA (1,0,0,0) model also has the lowest AIC value (23.30), reflecting that it best fits the coal mining occupation time series. Regarding the accident rate, the ARIMA (1,0,0) model has the lowest AIC value (8.40), which confirms that it is the best model in terms of fit and simplicity for the time series of the accident rate. For the three selected variables, the analysis suggests that a first-order autoregressive component, without the need for differencing or moving average terms, provides the best balance between fit and complexity in the time series of production, occupation, and the accident rate in coal mining in Colombia [23] (pp. 143–173).

3.7. Forecasting

Figure 9 corresponds to the trend and behaviour of coal production in Colombia.

3.7.1. Production Forecast

Historical Production Trend

• 2007–2011: there was continuous growth in coal production, increasing from 69.9 million tons in 2007 to 85.8 million tons in 2011.
• 2012–2017: There was a period of relative stability with around 86–91 million tons, with some slight fluctuations. The highest value recorded was in 2017, at 91.5 million tons.
• 2018–2020: Production begins to decline sharply in 2018, from 91 million tons in 2017 to 54.4 million tons in 2020. This period reflects an abrupt drop, possibly due to external factors such as declining demand or global economic impacts.

Production Forecasts Using ARIMA (1,0,0)

• The ARIMA (1,0,0) model was selected based on its Akaike information criterion (AIC) with a value of 33.59, indicating a good fit [24] (p. 290).
• The forecast projects a gradual decline in production from 2022 onwards as follows:
  • 2022: 57.5 million tons.
  • 2025: production is forecast to be approximately 55 million tons.
  • 2030: estimated production of 50.8 million tons.

Forecast Interpretation

• Downward trend: the model predicts a downward trend related to decreases in global demand, stricter environmental policies, or changes in the coal industry.
• Progressive stabilisation: although the drop is notable between 2019 and 2020, the model projects a gradual stabilisation in the coming years, with production that will not return to the peak levels seen in previous years.

3.7.2. Occupancy Forecast

Figure 10 corresponds to the trend and behaviour of coal occupation in Colombia.

Historical Occupancy Data (2012–2022)

• 2012–2014: a moderate growth in the occupancy rate is observed, from 40,000 in 2012 to 49,000 in 2014.
• 2015: An abrupt increase in occupancy is observed, reaching 122,253. This could be an exceptional year due to higher demand or investment in the sector.
• 2016–2019: although occupancy drops in 2016, it stabilises and remains around 101,000–126,000 during these years.
• 2020–2022: starting in 2020, the occupancy rate begins to increase again, reaching 142,869 in 2021 and reaching 184,148 in 2022.

Occupancy Forecast with ARIMA (1,0,0)

• The ARIMA (1,0,0) model was selected due to its lower AIC (23.3), indicating a good fit compared to other models tested.
• The forecast predicts continued growth in occupancy through 2030 as follows:
  • 2023: 198,563 employees.
  • 2025: 227,392 employees.
  • 2030: 299,466 employees.

Interpretation of Forecast

• Upward trend: the ARIMA model suggests a sustained growth trend in employment going forward, which could be related to project expansion or increased demand in the coal sector.
• Consistency with recent data: the forecast is consistent with the recent increase in occupancy from 2020, suggesting that this positive trend will continue as the industry recovers or grows.

3.7.3. Accident Rate Forecast

Figure 11 corresponds to the trend and behaviour of the coal accident rate in Colombia.

Historical Accident Rate Data (2012–2022)

• 2012–2014: the accident rate shows a downward trend from 71 accidents in 2012 to 55 in 2014.
• 2015–2017: there is a recovery in 2016 with 72 accidents, followed by a significant drop to 43 in 2017.
• 2018–2022: although an increase is observed in 2018 and 2019, the trend from 2020 onwards is again downward, reaching 33 accidents in 2021 and remaining at that value in 2022.

Accident Rate Forecast with ARIMA (1,0,0)

• The ARIMA (1,0,0) model is the best fit according to the Akaike information criterion (AIC = 8.4), indicating that it fits the historical data best.
• The forecast projects a continued decrease in the accident rate going forward as follows:
  • 2023: the accident rate is projected to be 29.2.
  • 2025: the accident rate is projected to drop to 21.6.
  • 2030: the accident rate is projected to be extremely low by this year, reaching only 2.6 accidents.

Interpretation of Forecast

• Downward trend: the model predicts a steady decrease in accident rates, which can be interpreted as the result of improved working conditions, stricter industrial safety regulations, or safer technologies in the sector.
• Possible error in the distant future: although the model projects an almost total reduction in accidents, it is likely that a residual level of accidents will likely remain, as it is unlikely that these types of incidents will be completely reduced to zero.

3.7.4. Life Expectancy Forecast

Figure 12 corresponds to the trend and behaviour of coal life expectancy in Colombia.

Historical Pattern (2012–2022)

• Sustained growth (2012–2019): Between 2012 and 2019, life expectancy increased steadily from 75.6 to 76.75 years. This growth reflects improvements in healthcare, quality of life, and access to basic services over that period.
• Sharp drop (2020–2021): In 2020, life expectancy dropped significantly to 74.77 years, followed by a further drop in 2021 to 72.83 years, the lowest figure on record. This decline can be attributed to the impact of the COVID-19 pandemic, which negatively affected global health indicators in many countries.

Forecast (2022–2030)

• Long-term increase: Beginning in 2022, the forecast predicts a gradual recovery in life expectancy, starting at 72.83 years in 2022 and increasing to 97.6 years in 2030. This sharp increase may reflect the following:
  • Continued improvements in public health: advances in medical technology, disease prevention, and improvements in healthcare infrastructure are projected to have a positive long-term impact.
  • Improved living conditions: as stronger policies are implemented to reduce poverty, improve access to education, and strengthen social welfare, life expectancy tends to increase.
• Tipping point (2025): after a slight decline between 2023 and 2024, the forecast suggests that in 2025, life expectancy will begin to recover, reaching 70.37 years, and then continue to increase to become over 88 years in 2029.

Critical Factors to Consider in Forecast

• Post-COVID recovery: The projected sharp increase may be influenced by an expected recovery from the COVID-19 pandemic. As countries implement vaccination programmes, strengthen their health systems, and better control diseases, life expectancy should increase again [25].
• Environmental and social challenges: Although the forecast is optimistic, climate change, economic crises, and social inequalities could limit these long-term increases. Therefore, the projected growth should be viewed with caution, as it may not materialise at the expected level if these challenges persist.
• Limitations of the model: the ARIMA model does not take into account possible disruptive events in the future, so the projection toward such a high life expectancy, such as 97.6 years in 2030, may overestimate improvements in health and well-being [26] (p. 19).

4. Discussion

From the perspective of the Sustainable Development Goals (SDGs), the results of the study on coal mining communities in Colombia show an uncompromising interaction between economic dynamics and indicators of health and well-being. Analysing SDG 3, which seeks to ensure healthy lives and promote well-being for all, the study shows that coal mining, despite being an important source of income for the country, has failed to translate its economic benefits into substantial improvements in the quality of life of these communities. Coal production is positively correlated with royalties, indicating that increased mining activity generates more revenue for the government. However, resources have not been used effectively to improve mining areas’ health infrastructure or medical services. There is a clear lack of strategic investment in public health services, evidencing the low life expectancy and adverse working conditions that characterise these communities. Although measures have been taken to reduce occupational accidents, the effects on the general health of workers are minimal due to continued exposure to high-risk working conditions and limited access to specialised medical services.

The analysis is also conducted for SDG 10, which focuses on reducing inequalities. The research shows that the unequal distribution of economic benefits from coal mining has exacerbated health and social disparities in mining communities. Despite the revenues generated, there is no significant reduction in the gaps in access to health services between mining areas and other regions of the country. This reflects a poor management of resources from royalties, which should be earmarked to improve equity in access to healthcare. Current policies have not effectively addressed inequality in access to essential services, thus affecting life expectancy and overall well-being. Public policies should focus on a fairer redistribution of economic benefits, ensuring that communities suffering from the negative impacts of mining also receive the benefits necessary to improve their living conditions.

The research results show that the effects of climate change and environmental degradation associated with coal mining could have long-term consequences on the health of mining communities. Air, water, and soil pollution from coal mining has direct and indirect adverse effects on the health of the people living in these areas. As climate change continues to affect ecosystems and environmental conditions, the incidence of respiratory diseases is likely to increase.

5. Conclusions

This research analysed variables such as coal production, royalties, employment rates, the number of workplace accidents, mining-related mortality, and the life expectancy of the population. The quality of life in mining communities has been steadily deteriorating despite the positive relationship between coal production and royalties in the coal mining sector. High mortality rates, frequent workplace accidents, and insufficient investment in healthcare infrastructure continue to negatively affect life expectancy in these areas. It is important to reconsider how royalty resources are managed in order to prioritise actions that genuinely contribute to the well-being and progress of the affected communities.

The main components identified through principal component analysis (PCA) were coal production, royalty distribution, workplace accident rates, and investment in healthcare infrastructure. The PCA showed that coal production and the royalties generated are the variables that contribute most to the variability of the results, highlighting their central role in the economy of mining communities. Insufficient investment in health and education significantly impacts quality of life, underscoring the need for the better management of royalties. According to Law 141 of 1994, resources from the National Royalty Fund must be used to promote mining, preserve the environment, and finance regional projects defined as priorities in development plans. However, the research results show a disconnect between the objectives outlined by the law and the reality observed in mining communities. Although the law provides a framework for the equitable and effective use of royalties, the lack of strategic investment in health and education has limited the expected positive impact on the quality of life of these communities. To close this gap and align the results with the objectives of Law 141 of 1994, more transparent management and a focus on sustainable development and improving living conditions are needed.

The relationships between production and life expectancy show a moderately positive relationship with life expectancy. This suggests that an increase in production may be linked to improvements in living conditions, although these benefits may be limited by indirect effects, such as environmental degradation; there is a positive trade-off between coal production and royalties generated, indicating that higher production translates into higher royalties. However, this economic benefit is not necessarily reflected in direct improvements in the quality of life of mining communities.

The employment rate in the coal sector has a negative impact on life expectancy, suggesting that employment in this sector could be associated with adverse working conditions that negatively affect the health and well-being of workers. The mining sector has steadily decreased accident rates, indicating that occupational safety measures have improved; this reduction has contributed positively to the life expectancy and well-being of communities.

Coal royalties show a negative relationship with life expectancy, suggesting that they are not being channelled effectively to improve the living conditions and health of communities; this finding highlights the need to review how these funds are managed.

In the case of mining communities in Colombia, although the sector generates a considerable amount of employment, the indicators reveal that the targets associated with the Sustainable Development Goal of Decent Work And Economic Growth (SDG 8), which seeks to promote inclusive and sustainable economic growth, as well as to ensure that all people have access to decent work with adequate safety conditions and labour rights, are not being fully met; the factors found for this research that affect this non-compliance are the occupation rate, occupational accidents, and life expectancy concerning working conditions.

Regarding the employment rate, coal mining has been an important source of employment in mining communities. The data show that the increase in employment in this sector has not translated into improvements in living conditions. In fact, the negative assessment between the employment rate and life expectancy suggests that mining jobs may be associated with adverse working conditions, such as long working hours, exposure to environmental hazards, and a lack of adequate protection of labour rights.

There has been a steady decline for occupational accidents, but this remains a critical challenge; the fact that the accident rate is still high compared to other productive sectors suggests that occupational safety measures have not been sufficient to protect workers fully.

Regarding life expectancy and working conditions, there is a negative relationship between occupation and life expectancy, indicating that working conditions in mining are inadequate to guarantee decent work as established in SDG 8. Exposure to constant risks, such as pollution and job insecurity, negatively affects the long-term health of workers and their families.

The Sustainable Development Goal of Health and Well-being (SDG 3) focuses on ensuring healthy lives and promoting well-being for all people, regardless of age. In the context of Colombia’s mining communities, the analysis of indicators shows that although progress has been made in reducing occupational accidents, the economic benefits generated by mining have not translated into significant improvements in the health and quality of life of these communities, which limits the achievement of this goal.

Although mining royalties have increased, they have not translated into visible improvements in community health systems. The inefficient use or misallocation of resources prevents the economic gains from mining from being reinvested in improving health infrastructure and access to quality medical care and public health programmes, resulting in an unequal distribution of economic benefits.

Despite national programmes to improve health in rural areas, these have not been sufficient to address the specific needs of mining communities. The lack of specialised health services and the scarcity of prevention and education programmes aggravate long-term health problems, impacting public health policies for the country.