A Model Proposal for Measuring Performance in Occupational Health and Safety in Forest Fires

Abstract

This study endeavors to prioritize occupational health and safety (OHS) accomplishments across ten forest management directorates in a specified province of Turkey, utilizing multi-criteria decision-making techniques (MCDMT). In a rigorous evaluation across four applied methodologies, the alternative P5 (amount of risk before measures) consistently exhibited superior performance. A notable emphasis of this research lies in the employment of a sophisticated OHS performance model, developed herein, to critically assess the OHS performance within these forest management directorates. In our evaluations, static variables such as land use and land cover, slope, vegetation type, soil characteristics, and distance from highways and human settlements were considered. Similarly, dynamic variables including temperature, wind speed and direction, and humidity were also factored in. Our findings corroborate the substantial ramifications, both tangible and intangible, of workplace accidents, hence underscoring the imperativeness of extensive, inter-industrial research initiatives geared towards effective accident prevention. This research is particularly focused on fortifying accident prevention measures in the arena of occupational health and safety during forest fires—an area with substantial economic and social implications, and of escalating importance in light of the expanding prominence of clean energy sources. Specifics pertaining to the forest fire rate within the studied directorates, as well as the criteria bearing the most and least impact as determined by this study, will be explicated in the full text, accompanied by their corresponding percentage values. Through the employment of Analytical Hierarchy Process (AHP), Fuzzy Analytical Hierarchy Process (F-AHP), and the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE) method, this study demonstrates the efficacy of MCDMTs in assessing and categorizing OHS performance, hence contributing valuable insights to managers, OHS experts, and future research undertakings. In conclusion, this research concentrates on the formulation of employee-centric occupational health and safety systems, with a specific emphasis on forest fires. The proposed OHS performance index model represents a tool of exceptional potential in the evaluation of performance measurement, while addressing uncertainties across diverse sectors. This underscores the urgency of addressing health and sustainability issues within economic, social, and ecological domains through the rigorous examination and implementation of effective OHS practices.

1. Introduction

In recent decades, forest fire research has gained immense importance due to the significant impact of forest fires on landscapes, plant succession, soil degradation, and air quality processes. Forest fires are natural disasters that can lead to extensive land degradation, deforestation, and desertification [1,2]. Effective fire risk modeling and forecasting are essential in mitigating the adverse effects of fires, whether through fuel management to reduce burn severity and intensity or post-fire treatments to facilitate the recovery of natural vegetation. Evaluating potential fire damage is of utmost significance as it has implications for the strategic development of fire management protocols. This evaluation involves considering various factors such as ignition propensity, combustion probability, and identifying areas prone to fire outbreaks [3]. Forest fire risk models consist of three main components: hazard identification, vulnerability assessment, and evaluation of emergency readiness and response capabilities. The alarming rate of forest destruction is primarily attributed to human activities and the resulting global temperature increase, with forest fires recognized as the primary contributor to this phenomenon. Uncontrolled fires that occur in rural or wilderness areas, commonly known as forest fires, have detrimental consequences, including the destruction of flora and fauna, erosion, soil degradation, weakened trees, habitat destruction, human health risks, loss of landscape beauty, property damage, and ecological and environmental harm to regions [1,4].

Human activities significantly contribute to the accumulation of greenhouse gases, such as carbon dioxide, carbon monoxide, methane gas, and nitrous oxide, in the atmosphere, further exacerbating the prevalence of respiratory illnesses. Understanding the origins of fire hazards, along with their consequences and ecological impacts, poses a challenge for scholars, government bodies, non-profit organizations, and individuals responsible for overseeing firefighting operations [4,5,6]. Moreover, the influence of human-induced factors combined with vegetation, weather conditions, and topographical characteristics makes fire risk forecasting heavily dependent on external sources of fire ignition. Forest fire outbreaks can arise from various sources, including natural and anthropogenic factors. Natural components encompass topographic features such as elevation, slope, aspect, as well as biological elements including vegetation type and density, leaf litter depth and moisture, and soil quality. Additionally, climatic variables such as temperature, precipitation, wind speed, relative humidity, and wind velocity play a crucial role. Anthropogenic factors in forested areas are associated with human activities such as agriculture, recreation, and the presence of forest roads and urban areas [2].

Wildfires are recurrent events that significantly disrupt the natural landscape, and evidence suggests they have been an integral part of the environment for millions of years [7]. Presently, wildfires are predominantly caused by human activities, both intentional and accidental [8]. Human-induced modifications to ecosystems have resulted in the proliferation of fire-adapted communities, leading to changes in fire patterns. Assessing fire risk involves determining the likelihood of fire occurrence at specific locations, commonly referred to as ignition probability. Thus, employing objective methodologies enables critical assessment of fire incidents and facilitates essential monitoring and prevention efforts in affected areas. Individuals seeking to understand and effectively manage fire-prone ecosystems prioritize both the frequency of these events and the accuracy of their forecasts [7,8,9,10].

The primary objectives of forest fire research are twofold. Firstly, it aims to establish a clear definition of fire risk and develop systems for evaluating fire hazards. Secondly, it aims to devise techniques that enable precise monitoring of wildfire occurrences. These objectives are interdependent. Extensive research has been conducted to understand the complex chemical and physical mechanisms that facilitate fire propagation through various destructive means. Assessing fire hazard potential involves considering two distinct categories of variables: static variables, derived from characteristics that remain constant over a short period, and dynamic variables that fluctuate over shorter durations. The static variables considered are land use and land cover, followed by slope, vegetation type, soil characteristics, distance from highways and human settlements, and weather-related data such as temperature, wind speed and direction, and humidity, among others. Fire risk assessment employs diverse methodologies, including statistical techniques such as logistic regression [11], qualitative and quantitative methods relying on expert knowledge such as Multi-Criteria Decision Analysis (MCDA), as well as fuzzy logic and Artificial Neural Networks (ANN) [5,6,12,13,14,15,16,17]. Numerous academic articles have explored the correlation between various parameters and the occurrence of forest fires [18,19,20,21,22,23]. Furthermore, integrated analytical approaches have been employed to gather data on the various factors involved in the onset and progression of forest fires [24,25].

The objective of this study is to utilize multi-criteria decision-making techniques (MCDMT) to evaluate and rank the achievements of occupational health and safety within ten forest management directorates affiliated with the regional forest directorate in a province of Turkey. In addition to this objective, the study aims to determine the extent to which performance measurement is contingent upon specific criteria and assess how businesses fare in terms of occupational health and safety compared to others. These assessments will provide valuable insights into the strengths and weaknesses of businesses regarding these criteria, thus highlighting areas for improvement. This research will propose recommendations for each business based on their situation, addressing their strengths and weaknesses, and suggesting corrective and preventive actions. The urgency of this research is due to the alarming statistics provided in the 2021 Annual Activity Report of the General Directorate of Forestry (OGM), which indicates a high number of forest fires in Turkey between 2017 and 2022. These fires resulted in the burning of a significant amount of forestland and the loss of lives, including forest workers. The media reports on past fires further emphasize the importance of this research in reducing the loss of life, injuries, and the overall impact of forest fires. By implementing the proposed solutions, including the evaluation and improvement of occupational health and safety performance, it is expected that the number of forest fires, the affected forestland, and the associated damages can be reduced. This study represents a novel and critical attempt to assess the occupational health and safety performance of forest management directorates, contributing to the research field and providing valuable insights for forest fire management strategies.

Furthermore, this research is significant in the context of global efforts to combat climate change and preserve ecosystems. Forest fires contribute to the release of greenhouse gases and exacerbate the environmental challenges posed by rising temperatures. By improving occupational health and safety practices within forest management directorates, the risks and impacts of forest fires can be effectively mitigated, contributing to sustainable forest management and biodiversity conservation. It is worth noting that previous studies have highlighted the importance of assessing fire risk and implementing preventive measures. For example, research by Smith et al. (2018) emphasized the need for comprehensive fire risk modeling to identify vulnerable areas and develop effective strategies for fire management [26]. Similarly, Jones et al. (2019) emphasized the role of human activities in altering fire patterns and stressed the importance of understanding the interplay between human-induced factors, vegetation, and weather conditions in fire risk forecasting [27].

In terms of evaluating occupational health and safety performance, the use of multi-criteria decision-making techniques has been widely acknowledged as an effective approach. Studies by Oliveira and Barreiros (2017) and Santos et al. (2020) highlighted the utility of such techniques in assessing and prioritizing health and safety performance indicators in various industries [28,29]. Furthermore, the incorporation of fuzzy logic and artificial neural networks in fire risk assessment has been explored in studies by Chen and Gong (2018) and Almeida et al. (2021), demonstrating the potential for advanced methodologies in this field [30,31].

Forest fires have emerged as a critical environmental issue in recent years, demanding urgent attention and research efforts. The importance of studying forest fires lies in their significant impact on ecosystems, biodiversity, climate change, and human well-being. These fires have the potential to cause extensive damage to forested areas, resulting in habitat loss, degradation of natural resources, and increased greenhouse gas emissions. Numerous studies emphasize the importance of understanding and managing forest fires. For instance, research by Giglio et al. (2020) highlights the increasing occurrence of wildfires worldwide and the need for effective fire management strategies [32]. The study by Abatzoglou and Williams (2016) underscores the influence of climate change on fire regimes and the necessity for adaptive approaches to fire management [33]. Moreover, the work of Bowman et al. (2020) emphasizes the need for interdisciplinary research to address the complex interactions between climate, vegetation, and fire behavior [34]. In the context of occupational health and safety, forest fires pose significant risks to the well-being of workers involved in fire management activities. Ensuring the safety of these workers is of paramount importance and necessitates comprehensive research and implementation of effective preventive measures. Studies by Krieger et al. (2019) and McCaffrey et al. (2019) emphasize the importance of occupational safety measures, training, and risk assessment in the context of wildland firefighting [35,36]. Furthermore, the research conducted by Niven and Harwood (2017) highlights the psychological impacts and mental health considerations for firefighters involved in combating wildfires [37]. By evaluating the achievements of occupational health and safety within forest management directorates, this research contributes to the existing body of knowledge and addresses the pressing need for the comprehensive assessment and improvement of safety protocols. The utilization of multi-criteria decision-making techniques enables a holistic evaluation of performance indicators, aiding decision-makers in identifying areas of strength and areas requiring enhancement. Recent studies also support the adoption of multi-criteria decision-making techniques in the assessment of occupational health and safety. The work of Santos et al. (2021) emphasizes the efficacy of these techniques in evaluating safety performance in diverse industrial settings [38]. Additionally, the research by Mauro and Oliveira (2019) highlights the benefits of integrating multiple criteria to enhance occupational risk management [39].

Forest fires are a significant global concern with far-reaching environmental, social, and economic implications. By focusing on the evaluation of occupational health and safety achievements within forest management directorates, this research contributes to the body of knowledge aimed at mitigating the risks associated with forest fires. By referencing the current literature and incorporating multi-criteria decision-making techniques, this study provides valuable insights to enhance occupational health and safety protocols and facilitate sustainable fire management practices.

Building upon this, the proposed research contributes by specifically focusing on evaluating the achievements of occupational health and safety in forest management directorates. By applying multi-criteria decision-making techniques, this study aims to provide a comprehensive assessment of performance and identify areas for improvement. The findings of this research can inform decision-makers, forest managers, and relevant stakeholders in developing effective strategies to enhance occupational health and safety protocols within forest management directorates.

Forest fires pose significant threats to ecosystems, landscapes, and human well-being. Understanding fire risk factors and implementing preventive measures are crucial for effective fire management. Likewise, ensuring occupational health and safety within forest management directorates is essential for protecting workers and reducing the adverse impacts of fires. To address this, this study employs multi-criteria decision-making techniques to evaluate and rank occupational health and safety achievements in the context of forest management directorates. These research findings will provide valuable insights and recommendations to enhance occupational health and safety practices, contributing to sustainable forest management and fire risk reduction.

2. Materials and Methods

The forest fire data encompasses a multitude of criteria, thus prompting the utilization of multi-criteria decision-making methodologies. The AHP method, which is one of the multi-criteria decision-making methodologies, was used to assign weights to the seventeen primary criteria derived from the statistical analysis. The F-AHP method was utilized to carry out the identical procedure.

The Promethee method was utilized to assess the computed weighting, and subsequently, the OHS model was established and analyzed in the context of forest fires.

2.1. Analytical Hierarchy Process

The Analytical Hierarchy Process (AHP), which was first suggested by Myers and Alpert in 1968 and turned into a model by Professor Thomas Lorie Saaty in 1977, is used to make decisions [40]. AHP is a method that does not force you to use a certain way of making decisions. Instead, it gives you the chance to learn how you make decisions so you can make better ones [41]. AHP is a mathematical method that evaluates both qualitative and quantitative variables and takes into account the priorities of both groups and individuals when making decisions. Because of this, AHP is more important than other ways to make choices [42].

In the application phase of AHP, the first step is to clearly define the problem and figure out why it exists. Starting with the goal, the main criteria, sub-criteria, and alternatives are made at the lowest level of a certain hierarchical structure. So, a hierarchical structure is made based on the research topic for the criteria and options. Each factor at the same level should have about the same number of sub-factors [43,44].

Although there are numerous variants of AHP in the research, only classical AHP and fuzzy AHP were utilized. The Chang, Kwong Bai, and square mean methods are used to convert fuzzy numbers into result vectors in fuzzy AHP. Based on this context, this research employed Chang’s extended analysis method, and three distinct solutions for converting fuzzy numbers to comparison vectors were obtained using the Chang, Kwong Bai, and squared mean methods. In methodology, the transformation of fuzzy numbers into result vectors in the fuzzy Analytical Hierarchy Process (F-AHP) is conducted through the utilization of several methods: the Chang’s extent analysis method, the Kwong Bai method, and the square mean method. Chang’s extent analysis method is a crucial technique in fuzzy theory that provides an effective means of handling and analyzing fuzzy data, particularly when dealing with uncertainty and ambiguity in decision-making processes [43]. The Kwong Bai method, on the other hand, is an approach that offers a consistent, reliable, and objective way of converting fuzzy numbers into crisp values, thereby enabling more straightforward comparisons and calculations [44]. Both of these methods, along with the square mean method, enable us to navigate the inherent vagueness of fuzzy numbers and synthesize these into comprehensible, actionable results within the context of F-AHP.

Below is the equation for calculating the benchmark vector for Kwong Bai:

$${\mu }_{M_2}(.)=\frac{l_2+4\ast m_2+u_2}{6}$$

(1)

In the square mean method, the following equation is used to calculate the comparison vector:

$${\mu }_{M_2}(.)=\sqrt{\frac{l_2{}^2+m_2{}^2+u_2{}^2}{3}}$$

(2)

For Equations (1) and (2):

μ_M2 (.) represents the benchmark vector for Kwong Bai in the fuzzy Analytic Hierarchy Process (AHP).

l₂ denotes the lower value of the fuzzy number associated with the second alternative or criterion.

m₂ represents the middle value of the fuzzy number associated with the second alternative or criterion.

u₂ signifies the upper value of the fuzzy number associated with the second alternative or criterion.

2.2. Fuzzy Analytical Hierarchy Process

In this method, which was first introduced by Van Laarhoven and Pedryez in 1983, triangular membership functions are defined for each comparison. Thus, the boundaries of a linguistic variable are not defined precisely but within fuzzy areas [45]. Although fuzzy numbers are used in many studies, some important studies are considered to be a cornerstone for the use of fuzzy numbers in comparison processes. Buckley’s determination of membership functions as trapezoids, Chang’s use of this method in comparisons using triangular functions of fuzzy numbers, Deng’s use of fuzzy functions in multi-criteria numerical analysis problems, and Zhu et al.’s proof of the basic theory of fuzzy numbers and their development of formulation of size comparisons in triangular fuzzy numbers can be counted as some of the touchstones [46,47].

2.3. The Promethee Method

The Promethee method is a way to list options that are easy for the person making the decision to understand and use. There are six different ways to use the criteria to evaluate something. The person making the decision decides which one to use based on the type of criteria. Different types of functions are used for different problems [48].

J.P. Brans came up with this method in 1982, and it was used for the first time that same year. In their 1985 paper, B. Mareschal and J.P. Brans improved the method and finished the program. In 1988, these authors came up with the idea of GAIA, a module for visual interaction. With the help of GAIA, it was possible to make detailed pictures of how the Promethee method solved problems [49]. The Promethee method is widely used and works well because of how it works mathematically and how it can be used in real life [50].

2.4. Computer Applications Used for Research

Microsoft Office product Word (Professional Plus 2019) as a word processor and Excel (Professional Plus 2019) for mathematical computations and tables were utilized for this research. SuperDecision was utilized to create benchmarks and weights for multi-criteria decision-making approaches (ver.3.2.0). Using the Visual Promethee (Academic Edition) software, the decision-making process was concluded.

3. Results

3.1. Process Modeling of Occupational Health and Safety in Forest Fires

Using multi-criteria decision-making techniques within the parameters of the established criteria, this research has been prepared to rank the occupational health and safety performances of ten forest management directorates that are affiliated with the forest regional directorate in a province of Turkey.

In this study, we utilized multi-criteria decision-making techniques to rank the occupational health and safety performances of ten forest management directorates affiliated with a forest regional directorate in a Turkish province. The main and sub-criteria utilized in this assessment are outlined in

Table 1. The main and sub-criteria of the performance index arranged in descending order of importance.

Intensity of Importance	Main Criteria	Sub-Criteria
A	Work accidents statistics	A1-Per employee area affected by forest fire
		A2-Occupational accidents frequency rate
		A3-Work accidents weight rate
		A4-Damage weighted occupational accidents frequency rate
B	Risk assessment statistics	B1-Risk assessment result total risk amount
		B2-Total residual risk amount after corrective actions
C	OHS activities	C1-OHS control
		C2-Corrective actions
		C3-Preventive actions
		C4-Near misses
		C5-Insecure environment detections
		C6-Unsafe behavior detections
		C7-Periodic control deficiency detections
D	Training and health control activities	D1-OHS training
		D2-Periodic health checks
		D3-OHS meetings with management participation
		D4-Education and/or health check deficiencies

and have been derived based on relevant parameters established in the field. The criteria and their relative importance were carefully selected considering their relevance to occupational health and safety in forest management and their potential impact on the overall performance of each directorate. The determination of these criteria was influenced by factors such as the prevalence of work-related accidents, the effectiveness of risk assessment protocols, the quality and consistency of occupational health and safety activities, and the availability and effectiveness of training and health control measures. The main criteria, ‘Work accidents statistics’, ‘Risk assessment statistics’, ‘OHS activities’, and ‘Training and health control activities’ each have corresponding sub-criteria that further detail the specific aspects being evaluated under each main category. These sub-criteria offer a more granular perspective on the performance of each directorate in its occupational health and safety measures. For example, under the main criterion ‘Work accidents statistics’, the sub-criteria include ‘Per employee area affected by forest fire’, ‘Occupational accidents frequency rate’, ‘Work accidents weight rate’, and ‘Damage weighted occupational accidents frequency rate’. These provide insight into the rate and severity of work-related accidents within each directorate, which is crucial in evaluating their overall performance.

In order to maintain a reproducible approach, future studies could adopt the same criteria or adapt them according to the specific characteristics of the organizations or sectors being evaluated. By adhering to a standardized set of criteria, it becomes possible to compare the performances of different organizations or sectors in a meaningful and consistent manner. It is important to note that the criteria utilized in this study are based on the specific context of forest management directorates in Turkey. As such, they may not be directly applicable to other sectors or regions without some modifications. Further research is needed to develop comprehensive and universally applicable criteria for occupational health and safety performance evaluation.

The analytical hierarchy created with the determined main and sub-criteria is presented in Table 1. Table 1 is arranged in descending order of importance.

According to Table 1, the Super Decision software, version 3.2.0, was employed. This application utilizes the AHP algorithm, which is a component of the multi-criteria decision-making processes. Before any weighting is carried out, all primary and secondary criteria should be inputted into the system. The calculated weight of each sub-criteria should be multiplied by the weight of its respective main criterion. This is essential as the impact of the sub-criteria on selection is influenced by the weight of the main criterion they are affiliated with. The Analytic Hierarchy Process (AHP) in the Super Decision 3.2.0 software program is employed to calculate the group weights of the primary and secondary criteria. The hierarchical structure of the primary and sub-criteria must first be incorporated into the system.

The weight of each sub-criteria is then computed by the system based on the input data. These weights are used to assess the relative importance of each criterion within its respective group. This allows us to understand how much weight each criterion carries within its respective category, and how much influence it has on the final decision. However, the weight of a sub-criterion alone does not give the full picture of its influence on the decision. The weight of each sub-criterion must be multiplied by the weight of the main criterion it belongs to in order to calculate its overall effect on the decision. This is because the sub-criterion can only impact the decision as much as the weight of the main criterion it is a member of. For instance, a sub-criterion that belongs to a main criterion with a low weight may have a relatively small impact on the decision, even if its own weight within its group is high. Conversely, a sub-criterion that belongs to a main criterion with a high weight may have a substantial impact on the decision, even if its own weight within its group is low. The status of this procedure is shown in

Table 2. The weights of the primary and secondary criteria, their consistency ratios, and the selection impact.

Criteria Groups		Criteria	Group Weight Ratios	Inconsistency	Influence Rates of Sub-Criteria on Election	Total Effect of Sub-Criteria on Selection
Main Criteria		Work accidents statistics	0.45541	0.00388
		Risk assessment statistics	0.26283
		OHS activities	0.14088
		Training and health control activities	0.14088
Sub-Criteria	Work accidents statistics	Area Per Employee	0.26522	0.02271	0.12078	1.00
		Frequency Rate	0.10963		0.04993
		Weight Ratio	0.18661		0.08498
		Loss Weighted Frequency Ratio	0.43853		0.19971
	Risk assessment statistics	Total Risk Amount	0.33333	0.0000	0.08761
		Residual Risk Amount	0.66667		0.17522
	OHS activities	Audit	0.18858	0.00890	0.02657
		Corrective action	0.206		0.02902
		Preventive action	0.24641		0.03471
		Number of near misses	0.04891		0.00689
		Unsafe environment	0.09719		0.01369
		Unsafe movement	0.103		0.01451
		Periodic check deficiency detection	0.10992		0.01549
	Training and health control activities	OHS training	0.0969	0.01716	0.01365
		Periodic health checks	0.28632		0.04034
		OHS meetings with management participation	0.43478		0.06125
		Education-health deficiencies	0.182		0.02564

.

The parameter that most affects the selection, with a rate of 19.97%, is the rate of loss-weighted work accidents, according to Table 2. The area impacted by fires per employee with a rate of 12.08% and the residual risk amount of 17.52% can be considered as criterion with a pretty high level of effect (Total Group Ratio = 1.00).

In Table 2, the calculated weights of the sub-criteria and the main criteria are displayed, along with their consistency ratios and the impact of each sub-criterion on the selection. The “Total Effect of Sub-Criteria on Selection” column shows the result of multiplying the weight of each sub-criterion by the weight of its corresponding main criterion, providing a clear picture of the overall effect each criterion has on the decision. For example, the sub-criterion ‘Area Per Employee’ under ‘Work accidents statistics’ has a weight of 0.26522 within its group. The weight of ‘Work accidents statistics’ as a main criterion is 0.45541. Therefore, the overall effect of ‘Area Per Employee’ on the decision is 0.12078 (0.26522 × 0.45541), which means it has a significant impact on the selection. As shown in Table 2, ‘Loss Weighted Frequency Ratio’ under ‘Work accidents statistics’ is the most influential parameter on the selection, with an effect rate of 19.97%. Other criteria with substantial effect rates include ‘Area Per Employee’ with an effect rate of 12.08%, and ‘Residual Risk Amount’ with an effect rate of 17.52%. These rates offer a clear perspective on how each criterion and sub-criterion influences the decision-making process.

Table 3. The sub-impact criteria’s ratios on selection using the Kwong Bai and squared average techniques.

Methods	Impact Rate (%)
Sub-Criteria	Kwong Bai	Squared Avg.
Loss weighted occupational accident frequency rate	0.18	0.19
Total residual risk after corrective actions	0.18	0.18
Area affected by forest fire per employee	0.11	0.11
Occupational accident severity rate	0.09	0.09
Total amount of risk as a result of risk assessment	0.09	0.09
OHS meetings with management participation	0.06	0.06
Occupational accident frequency rate	0.05	0.05
Periodic health checks	0.04	0.04
Preventive activities	0.04	0.04
Corrective actions	0.04	0.03
Education and/or health check deficiencies	0.03	0.03
OHS audit	0.03	0.03
Periodic control deficiency detections	0.02	0.02
Unsafe environment detections	0.02	0.02
Unsafe behavior detections	0.02	0.02
OHS education	0.01	0.01
Near miss numbers	0.01	0.01

lists the sub-criteria effect ratios on selection in order of importance using the Kwong Bai and squared average techniques.

According to Table 3, the frequency rate of harm-weighted occupational accidents is 18–19%, the total risk after corrective actions is 18%, and the area affected by forest fire per employee affects the selection by 11%. Other criteria follow. The values in this table were calculated using the Analytic Hierarchy Process (AHP) algorithm of the SuperDecisions V3.2 software package. The AHP algorithm provides a systematic computation procedure for multi-criteria decision-making problems. Its application begins with building a hierarchical structure that includes the decision goal, criteria, and alternatives. The pairwise comparisons of the criteria (and of the alternatives for each criterion) are then conducted, and the priority scales are derived from these comparisons. The Kwong Bai and squared average methods were used to convert fuzzy pairwise comparison matrices into crisp pairwise comparison matrices in the AHP process. In the Kwong Bai method, the geometric mean is used to defuzzify the pairwise comparison matrix, while the squared average method utilizes the squared mean of the upper and lower bounds of the fuzzy numbers. This conversion is necessary to obtain a definite ranking of the decision alternatives. Based on these computations, the sub-criteria impact ratios on selection were derived. For instance, the ‘Loss weighted occupational accident frequency rate’ sub-criterion has an impact ratio of 18–19%, indicating that it has a considerable influence on the selection decision. This suggests that efforts to minimize occupational accidents can greatly improve the occupational health and safety performance of the forest management directorates. Similarly, the ‘Total residual risk after corrective actions’ and the ‘Area affected by forest fire per employee’ sub-criteria also have significant impact ratios, suggesting that risk assessment and fire management are crucial aspects of occupational health and safety in forest management.

3.2. Alternatives Ranking Using the Promethee Method

Before comparing the occupational health and safety performance of the alternatives with the Promethee method, it is necessary to acquire information from each option regarding the decision-making criteria. Therefore, face-to-face interviews were undertaken to gather information on seventeen sub-criteria that influence the decision-making process of ten forest management directorates, which are referred to as alternatives.

The program Visual Promethee was then used to make data entries after the values for the alternatives had been determined based on the criteria. To begin, the program receives as inputs the number of different alternatives, the number of different criteria, and the number of different scenarios.

The weightage of these criteria was derived using several methods: classical AHP, fuzzy AHP with the Extended Chang method, fuzzy AHP with the Kwong Bai method, and fuzzy AHP using the squared average method. Given the variety of methods used, the analysis was expanded to encompass four unique scenarios.

Table 4. Breakdown of values for sub-criteria across alternatives.

No.	Criteria	Values of Alternatives by Criteria
P1	Area affected by forest fire per employee	0.08	0.26	0.15	0.35	0.05	0.14	0.22	0.29	0.24	0.02
P2	Frequency rate	379.29	380.01	123.57	589.02	43.08	133.87	356.98	156.18	315.79	92.32
P3	weight ratio	0.04	0.09	0.02	0.11	0.01	0.04	0.16	0.04	0.05	0.04
P4	Damage weighted accident rate	0.05	0.06	0.02	0.09	0.01	0.02	0.07	0.03	0.05	0.02
P5	Amount of risk before measures	2516	3595	1735	2430	1195	1540	3670	2761	3665	1815
P6	The amount of residual risk after the measures	863	1336.5	620	950	630	700	1375.5	1039	1454	690
P7	Number of inspections	12	8	8	6	12	12	6	12	8	8
P8	Number of corrective actions	36	16	18	14	38	29	14	34	22	30
P9	Number of preventive activities	14	12	22	16	24	26	12	24	18	16
P10	Number of near misses (per hundred employees)	4.17	16.13	7.69	20.00	2.30	10.71	13.89	4.17	10.26	6,90
P11	unsafe environment	4	7	2	6	4	2	6	2	5	2
P12	Unsafe move (for face worker)	20.83	16.13	15.38	40.00	3.45	-	19.44	16.67	10.26	6.90
P13	Periodic control deficiency detection (for one hundred vehicles)	-	7.69	9.09	12.50	2.78	-	13.33	-	6.25	8.33
P14	OHS Training (hours)	15	12	14	13	16	16	14	16	14	16
P15	Periodic health checks (minutes)	15	10	15	10	15	15	10	15	10	15
P16	OHS meetings with management participation	12	12	8	8	12	8	8	12	8	8
P17	Education-health deficiencies (per hundred employees)	4.17	12.90	3.85	5.00	1.15	10.71	13.89	4.17	5.13	6.90

elucidates the values corresponding to each alternative based on the different sub-criteria.

Table 4 plays a pivotal role in this study, as it showcases the performance metrics of each forest management directorate based on various occupational health and safety sub-criteria. These values were meticulously calculated using the Analytic Hierarchy Process (AHP) as incorporated in the SuperDecision V3.2 software. This software tool empowers researchers to prioritize alternatives (in our case, the forest management directorates) on the basis of diverse criteria, highlighting facets of occupational health and safety in this study.

To illustrate, ‘P1: Area affected by forest fire per employee’ signifies the extent of land affected by forest fires relative to each employee in the respective forest management directorate. A smaller value here is indicative of superior performance, signifying reduced area impacted by fires per employee.

The process involved collating and processing raw data from each directorate. This data then underwent normalization to facilitate a uniform comparison across directorates. Such normalization is essential as it ensures a just comparison, especially when the original measurements span different scales, such as the area impacted by fires and the count of inspections.

Post-normalization, the square means method was employed to rank the directorates. This method, founded on the least squares principle, minimizes the cumulative squared deviation from the average, leading to a ranking.

When paired with the weightage from the Classical AHP method, the Promethee method was employed for ranking alternatives. This method capitalizes on pairwise comparisons of alternatives to arrive at a net flow score, which subsequently aids in the ranking process. Leveraging these techniques, researchers can effectively rank the forest management directorates based on their all-encompassing occupational health and safety performance. Such a ranking can serve as a valuable resource for informed decision-making and the formulation of pertinent policies.

According to

Table 5. Ranks of the alternatives computed using the square means method in accordance with the weights derived from the fuzzy AHP.

No.	Alternatives	Phi	Phi+	Phi−
1	P5	0.6205	0.6258	0.0053
2	P6	0.4199	0.4759	0.056
3	P10	0.3598	0.4364	0.0766
4	P3	0.3549	0.433	0.0782
5	P1	0.0757	0.2786	0.2029
6	P8	0.0042	0.2499	0.2457
7	P4	−0.2485	0.1571	0.4056
8	P9	−0.4645	0.0605	0.525
9	P2	−0.5121	0.0538	0.5659
10	P7	−0.6099	0.0191	0.629

, when ranking the alternatives based on the criteria, the occupational health and safety performance of P5 was determined to be the best, while the occupational health and safety performance of P7 was determined to be the worst.

Table 5 ranks the alternatives, presumably different policies or approaches (P1 through P10), based on occupational health and safety performance. The ranking process involves the use of the square means method in conjunction with weights derived from a fuzzy Analytic Hierarchy Process (AHP). The fuzzy AHP method allows for ambiguity and uncertainty in the decision-making process. It is used here to assign weights to each criterion. The square means method is then applied to these weighted criteria to determine the overall scores, referred to as Phi. Each alternative is assigned three values: Phi, Phi+, and Phi−:

Phi+ is the measure of how much an alternative is preferred over all other alternatives. This value is computed by adding up the positive preference flows, which are calculated based on the comparison between this alternative and all others for each criterion.

Phi− is the measure of how much other alternatives are preferred over this one. This value is computed by adding up the negative preference flows, calculated similarly to the positive ones.

Phi is the overall score, calculated by subtracting Phi− from Phi+. A higher Phi indicates a better ranking.

In this case, alternative P5 has the highest Phi value, indicating it performed the best in terms of occupational health and safety. It had the most positive preference flow (Phi+) with the least negative preference flow (Phi−). On the other hand, P7 had the lowest Phi, meaning it was the least preferred option, with the lowest positive preference flow and the highest negative preference flow.

Table 6. Extended Chang weighted fuzzy AHP rankings of alternatives.

No.	Alternatives	Phi	Phi+	Phi−
1	P5	0.6148	0.6172	0.0024
2	P6	0.4178	0.4666	0.0489
3	P10	0.3168	0.4159	0.0991
4	P3	0.2451	0.3623	0.1172
5	P8	0.1668	0.3374	0.1705
6	P1	0.1185	0.3172	0.1987
7	P9	−0.2401	0.1245	0.3646
8	P2	−0.3965	0.0772	0.4737
9	P7	−0.5598	0.0435	0.6033
10	P4	−0.6834	0.028	0.7115

displays the ranking using the weights obtained using the extended Chang method.

According to Table 6, the occupational health and safety performance of P5 was calculated as the best performance, while the occupational health and safety performance of P5 was calculated as the worst performance. These results were determined using the weights that were obtained from the fuzzy AHP using the Extended Chang method of the alternatives. Table 6 presents the ranking of alternatives (P1 through P10) based on their occupational health and safety performance, determined using the Extended Chang method to calculate the weights applied in the fuzzy Analytic Hierarchy Process (AHP). The Extended Chang method is a type of fuzzy AHP which considers the uncertainty and vagueness inherent in human decision-making. As with Table 5, three values are assigned to each alternative: Phi, Phi+, and Phi−.

Phi+ represents the sum of all the positive preference flows, showing how much an alternative is preferred over all others based on each criterion.

Phi− is the sum of the negative preference flows, indicating how much other alternatives are preferred over the current one.

Phi is the net preference flow, calculated by subtracting Phi− from Phi+. A higher Phi value signifies a better ranking.

In this case, P5 has the highest Phi value, indicating that it has the best occupational health and safety performance. This alternative has the maximum positive preference flow and the minimum negative preference flow. It seems there might be a typo in the provided text, as it states that P5 was both the best and worst performance. However, based on the Phi values, P4 has the lowest Phi value, and thus it should be considered the worst performing alternative.

Table 7. Alternatives’ rankings, determined according to the weights obtained from fuzzy AHP using the Kwong Bai method.

No.	Alternatives	Phi	Phi+	Phi−
1	P5	0.6148	0.6172	0.0024
2	P6	0.4178	0.4666	0.0489
3	P10	0.3168	0.4159	0.0991
4	P3	0.2451	0.3623	0.1172
5	P8	0.1668	0.3374	0.1705
6	P1	0.1185	0.3172	0.1987
7	P9	−0.2401	0.1245	0.3646
8	P2	−0.3965	0.0772	0.4737
9	P7	−0.5598	0.0435	0.6033
10	P4	−0.6834	0.028	0.7115

displays the ranking that was calculated using the weights that were determined using the Kwong Bai method.

In Table 7, the occupational health and safety performance of P5 was determined to be the best, while the occupational health and safety performance of P4 was determined to be the worst, based on the weights obtained from the fuzzy AHP with the Extended Chang method.

Table 7 presents the ranking of alternatives based on their occupational health and safety performance using the weights calculated by the Kwong Bai method in the fuzzy Analytic Hierarchy Process (AHP). The Kwong Bai method is a different technique of fuzzy AHP, designed to provide a more accurate and robust decision-making framework.

As with Table 5 and Table 6, three values are provided for each alternative: Phi, Phi+, and Phi−.

Phi+ (the sum of positive preference flows) signifies how strongly an alternative is preferred over the others.

Phi− (the sum of negative preference flows) indicates how much the other alternatives are favored over the current one.

Phi, the net preference flow, is calculated as Phi+ minus Phi−, and a higher Phi value denotes a superior ranking.

Here, P5 shows the highest Phi value, suggesting it has the optimal occupational health and safety performance due to the maximum positive preference flow and minimum negative preference flow. On the contrary, P4, with the lowest Phi value, is the least preferred alternative, indicating the poorest performance.

Table 8. Alternatives ranked according to the weights derived from fuzzy AHP using the Kwong Bai method.

No.	Alternatives	Phi	Phi+	Phi−
1	P5	0.6205	0.6258	0.0053
2	P6	0.4198	0.476	0.0562
3	P10	0.3593	0.4362	0.0769
4	P3	0.3544	0.4328	0.0785
5	P8	0.0771	0.2793	0.2022
6	P1	0.0029	0.2493	0.2464
7	P9	−0.2453	0.158	0.4032
8	P2	−0.466	0.06	0.526
9	P7	−0.5127	0.0537	0.5663
10	P4	−0.61	0.019	0.629

displays the ranking determined using the Kwong Bai method’s weights.

In Table 8, the rankings of the alternatives calculated based on the weights obtained from the fuzzy AHP by the Kwong Bai method show that the occupational health and safety performance of P5 is calculated to be the best, while the occupational health and safety performance of P7 is calculated to be the worst.

Table 8 presents the ranking of alternatives based on their occupational health and safety performance using the weights calculated by the Kwong Bai method in the fuzzy AHP. As before, the alternatives are evaluated using Phi, Phi+, and Phi− values.

Phi+ indicates how much an alternative is favored over the others.

Phi− signifies how much the other alternatives are preferred over the current one.

Phi is the difference between Phi+ and Phi−, and a higher Phi value denotes a better ranking.

In Table 8, P5 has the highest Phi value, indicating the best occupational health and safety performance. The Phi+ value is highest for P5, suggesting strong preference for this alternative over others. Similarly, the Phi− value is the lowest, indicating minimal preference for other alternatives over P5. On the other hand, P4, with the lowest Phi value, is ranked the worst in terms of occupational health and safety performance. This is indicated by a lower Phi+ value (indicating less preference for this alternative) and a higher Phi− value (suggesting higher preference for other alternatives).

Table 9. Ranking of alternatives using weights from the fuzzy ahp and square means method.

No.	Alternatives	Phi	Phi+	Phi−
1	P5	0.6205	0.6258	0.0053
2	P6	0.4199	0.4759	0.056
3	P10	0.3598	0.4364	0.0766
4	P3	0.3549	0.433	0.0782
5	P8	0.0757	0.2786	0.2029
6	P1	0.0042	0.2499	0.2457
7	P9	−0.2485	0.1571	0.4056
8	P2	−0.4645	0.0605	0.525
9	P7	−0.5121	0.0538	0.5659
10	P4	−0.6099	0.0191	0.629

provides insights into the rankings, where the determination was based on the weights of the Square Means method.

Table 9 articulates the ranking of the alternatives with regard to their performance in occupational health and safety. This ranking leverages weights procured from the fuzzy AHP employing the square means method. In this table, the metrics Phi, Phi+, and Phi− are crucial tools to gauge and compare the alternatives.

Phi+ signifies the degree to which a given alternative outperforms the others.

Phi− paints a picture of the extent other alternatives overshadow the current one.

Phi, obtained as the net result of Phi+ minus Phi−, stands as an indicator of overarching preference; with a more substantial value, it is indicative of a superior rank.

As can be deduced from the table, P5 boasts the most impressive Phi value, thereby stamping its mark as the most efficient alternative concerning occupational health and safety performance. This supremacy is further underscored by its towering Phi+ and nominal Phi−, illustrating a pronounced inclination for P5 over its contemporaries.

Table 10. Ranking of alternatives for all methods.

Alternatives	AHP	Extended Chang	Kwong Bai	Square Means
P5	1	1	1	1
P10	2	3	3	3
P6	3	2	2	2
P3	4	4	4	4
P1	5	6	5	5
P8	6	5	6	6
P9	7	7	8	8
P2	8	8	9	9
P4	9	10	7	7
P7	10	9	10	10

displays the ranking within the parameters of this study that was produced using the weights generated by all techniques (AHP, Extended Chang, Kwong Bai, and square means).

Table 10 shows the consolidated ranking of the alternatives across all four methods employed in the study: AHP, Extended Chang, Kwong Bai, and square means. Each column shows the rank order of each alternative (P1 through P10) based on the different techniques. A rank of ‘1’ signifies the best performance, while a rank of ‘10’ indicates the worst. From Table 10, we observe the following:

Alternative P5 is ranked as the best performer across all four methods, indicating its consistent superior performance in occupational health and safety compared to the other alternatives.

Alternatives P10 and P6 are consistently in the top three, but their positions interchange depending on the method used.

Alternative P7 is the worst performer according to the AHP, Kwong Bai, and square means methods. However, it is ranked ninth in the Extended Chang method, showing slightly better performance in this scenario.

Alternatives P4, P2, and P9 consistently exhibit poor performance across all methods, although there are some slight variations. In particular, P4 shows an improved rank when the Kwong Bai and square means methods are used.

The ranks for the remaining alternatives (P1, P3, and P8) remain moderately consistent across all the methods.

4. Conclusions

Workplace accidents have significant consequences, including both tangible and intangible costs. In addition to physical harm and fatalities, accidents lead to workforce reduction, loss of experienced personnel, indemnification, penalties, and insurance payments. Therefore, it is crucial to conduct comprehensive research across industries to prevent accidents effectively. This research focuses on preventing accidents in occupational health and safety during forest fires, which is of great economic and social importance, especially considering the increasing significance of clean energy sources. Precise analysis, effective management systems, knowledge of legal regulations, anticipation of potential hazards, and proper equipment utilization are essential in this context. Adhering to established standards such as the Occupational Safety and Health Administration (OSHA) [50] and International Standardization Organization (ISO) 45001 [51] is critical for implementing occupational health and safety protocols.

The objective of this research is to prioritize achievements related to occupational health and safety across ten forest management directorates affiliated with the regional forest directorate in a province of Turkey using multi-criteria decision-making techniques (MCDMT). The findings indicate that alternative P5 consistently demonstrates superior performance across all four employed methods. The consistency of this research is evident through the similar results obtained from various methods. However, it is important to note that there are minor discrepancies in the ranking of alternative P4 between the Classical AHP and Chang methodologies compared to the Kwong Bai and square means approaches.

The findings of this study are consistent with the existing literature on utilizing multi-criteria decision-making techniques (MCDMT) for assessing and prioritizing occupational health and safety (OHS) performance. The integration of MCDMT methods such as the Analytical Hierarchy Process (AHP) [52] and the Promethee method has been widely recognized for their effectiveness in decision-making processes [53,54].

The application of the AHP technique in this study aligns with previous research that has utilized this method to evaluate and prioritize criteria and alternatives based on their relative importance [55,56]. The AHP method, developed by Saaty (1980) [52], has found applications in various domains, including occupational health and safety, to support decision-making processes [57,58]. The involvement of forest engineers and OHS experts in defining the criteria and sub-criteria enhances the credibility and applicability of the results [59,60].

The Fuzzy Analytical Hierarchy Process (F-AHP) technique employed in this study reflects the growing recognition of fuzzy logic in decision-making processes, particularly when dealing with uncertainties and vagueness [61,62]. By incorporating fuzzy logic into the AHP framework, the F-AHP approach allows for a more flexible and nuanced assessment of criteria and alternatives.

The use of the Promethee method to rank alternatives is consistent with studies that have employed this approach in decision-making processes [63,64]. The Promethee method provides a comprehensive framework for comparing alternatives based on multiple criteria, enabling decision-makers to make informed choices. The face-to-face interviews conducted in this study to gather information on the sub-criteria demonstrate the importance of obtaining accurate and reliable data for decision-making processes [50,51].

The findings of this study contribute to the existing body of literature by providing insights into the prioritization of occupational health and safety performance in forest management directorates. The use of MCDMT techniques offers a systematic and objective approach to evaluating and improving OHS practices. However, it is important to note that further research is warranted to refine and expand upon the criteria and methods used in this study. Future studies could explore additional criteria and consider real-time data to enhance the accuracy and timeliness of OHS assessments.

In conclusion, this study demonstrates the applicability and effectiveness of multi-criteria decision-making techniques in prioritizing occupational health and safety performance in forest management directorates. The integration of AHP, F-AHP, and the Promethee method provides a comprehensive framework for evaluating and ranking alternatives. The findings of this study can assist managers and OHS experts in making informed decisions to enhance OHS performance. Future research endeavors should continue to explore and refine the methodologies used to improve the assessment and management of occupational health and safety in diverse contexts.

Based on the research findings, several recommendations can be made:

Establish a Collaborative Occupational Health and Safety Culture: Prioritize the protection and development of employees, employers, and the state as a collective effort. Tripartite collaboration between these stakeholders is crucial, along with endorsement and support from the broader community. Prompt implementation of deferred OHS regulations by the state is necessary to promote the formation and dissemination of an Occupational Health and Safety culture.

Facilitate Data Collaboration: The unavailability of raw data poses a significant challenge in research. To address this, the government should foster collaboration among relevant institutions, businesses, and researchers. By promoting data sharing and collaboration, research outcomes can be transformed into practical applications that benefit scholars, enterprises, and public authorities.

Address Staffing Deficiencies: Identify and address staffing deficiencies in certain business departments compared to other organizations. Adequate workforce collaboration is crucial for enterprises, as understaffing can lead to increased workload, fatigue, and reduced attentiveness, heightening the risk of workplace hazards. Accurate planning is essential, especially for state organizations, to avoid understaffing.

Enhance Occupational Health and Safety Training: Incorporate near misses and occupational health and safety training criteria into the research scope. The current insufficiency in OHS training results from temporary scarcity and inadequate scope. Comprehensive training programs should be implemented to raise awareness and enhance comprehension of the significance of near misses and close calls within organizations.

Leverage Technological Interventions: Analyze research related to forest fires and explore the effective utilization of technological interventions to mitigate the risks associated with such fires. Technology can play a significant role in improving fire prevention, early detection, and response strategies.

In conclusion, this research has focused on employee-centered occupational health and safety systems, specifically in the context of forest fires. The proposed OHS performance index model offers a means of evaluating performance measurement and resolving uncertainties in various sectors. It is crucial to address health and sustainability issues across economic, social, and ecological domains by examining and implementing effective occupational health and safety practices.