Towards responding to climate change: practices in Iranian healthcare centers

Abstract

The aim of this study was to investigate the prevailing practices suitable for Iranian healthcare centers to address climate change and to rank them in terms of priority. This paper, a mixed-methods study conducted in 2023, comprised a scoping review and a thematic analysis covering the period from 2000 to 2023 as the first phase of the study. In this regard, PubMed, Scopus, ProQuest, and the Cochrane Database of Systematic Reviews were searched. In the second phase, the authors engaged a group of experts to prioritize the most important practices suitable for Iranian healthcare centers to address climate change using the Grey MEREC-MABAC integrated approach. The scoping review resulted in 38 studies, and the thematic analysis revealed 11 primary categories of practices employed to address climate change. Subsequently, the Grey MEREC-MABAC approach indicated that the themes of 'Green Environment' (CC = 0.0929), 'Green Equipment, Waste, and Water Management' (CC = 0.0677), and 'Green Energy' (CC = 0.0653) were identified as having the highest priority among the practices deemed most important in the literature for addressing climate change within Iranian healthcare centers. The study proposed that practices or activities promoting biodiversity and environmental sustainability, such as organic food farming and tree planting, should be considered paramount approaches in Iranian healthcare centers to combat climate change.

Introduction

Throughout the twentieth century, a panel of climate change experts presented a proposition to a committee in the United States. They asserted that the observed trend of global warming and the associated rise in temperatures is not a natural phenomenon. Instead, they attributed this trend to the accumulation of carbon dioxide and other greenhouse gases in the atmosphere [1, 2]. Contemporary climate change is now acknowledged to be primarily driven by anthropogenic influences, particularly industrial activities. These activities result in the emission of greenhouse gases, such as carbon dioxide and methane, which exacerbate the issue [3, 4].

Climate change, chemical pollution, and unsustainable resource use have led to an increase in diseases worldwide. These environmental health issues have further strained health and treatment systems that were already under pressure [5]. Research indicates that human health and mental well-being have been adversely affected by climatic phenomena, including high-temperature periods, tropical storms, and river floods. Similarly, Iran, a Middle Eastern nation, is facing climate change challenges such as rising temperatures, frequent extreme weather events, declining air quality, increased diseases, mental health impacts, and a growing trend of natural disasters [6].

According to a report, healthcare centers in England are responsible for 3.2% of the country’s carbon dioxide emissions, amounting to 18 million tons. This figure is equivalent to 30% of the greenhouse gases emitted by England’s public sector [7]. Meanwhile, healthcare centers in the United States consume energy equivalent to an average of 8.5 billion US dollars annually, nearly double the energy consumption of other government agencies in the country [8]. Additionally, healthcare centers in Brazil account for 10.6% of the energy consumed for commercial purposes [9].

The World Health Organization (WHO) has formulated a document, grounded in its constitution and the contributions of member states, to develop programs for health and treatment organizations aimed at reducing greenhouse gas emissions [10]. According to the WHO, a green healthcare center is one that adapts to local climatic conditions and optimizes energy usage. It endeavors to minimize its environmental impact and its contribution to the disease burden by employing sustainable and efficient designs, materials, and systems [11].

To mitigate the environmental impacts of climate change, healthcare centers can implement several fundamental measures. These measures include enhancing the design of healthcare centers, adopting waste reduction strategies and sustainable management practices, efficiently utilizing natural resources such as water and energy, and procuring and using products and chemicals with minimal environmental side effects [12]. Environmental technology is employed to design and regulate the planning, structure, and operation of existing healthcare centers with the objective of achieving a desirable level of sustainability, renewable resources, and systems to reduce energy consumption and carbon emissions. This approach leads to improved building performance and the well-being of its occupants [13].

The existing literature on practices to combat climate change within healthcare centers appears to be diverse and sporadic in methodology and objectives. In a 2014 study conducted in Malaysia, aimed at developing an index for assessing the 'greenness' of a government healthcare center building, existing tools and guidelines were examined and analyzed [14]. Furthermore, a 2017 study, with the objective of identifying the dimensions and sub-dimensions of the ‘greenness’ level of a public healthcare center in Malaysia, conducted a review. Through thematic analysis, the dimensions and sub-dimensions were identified and introduced as an index for evaluating the 'greenness' level of public healthcare centers in Malaysia [15]. Similarly, a 2019 study, aimed at identifying the key factors for the development of a green healthcare center in Malaysia, undertook an initial review of research texts and identified a set of factors. Subsequently, a questionnaire was prepared and presented to experts, who were asked to assess the importance of each identified factor. The identified factors included dimensions such as operationalizing environmental stewardship, social responsibility, and economic development [16].

In alignment with global trends, Iran, a nation situated in the Middle East, has been contending with numerous challenges precipitated by climate change. These challenges have had profound impacts on its environment, public health, and susceptibility to infectious diseases [6, 17–20]. The significant impacts of climate change in Iran pose substantial threats to public health infrastructure and may result in population displacement. These climatic events not only exacerbate existing health issues but also introduce new challenges for healthcare delivery systems. Furthermore, the psychological repercussions of climate-related disasters, including anxiety and depression, are increasingly recognized as critical public health concerns [6, 21]. The mental health impacts of such disasters can manifest in various forms, including post-traumatic stress disorder (PTSD), adjustment disorders, and heightened levels of anxiety [6]. This situation underscores the urgent need for the country’s policymakers to devise and implement strategies to address this crisis.

This study aimed to identify existing practices to combat climate change and improve sustainability levels in healthcare centers, with the objective of formulating these practices into a structured questionnaire. The goal was to prioritize the most important practices in Iranian healthcare centers to enhance efficiency and sustainability in response to climate change. The authors of this study have acknowledged that, to their knowledge, only a few studies in the existing literature have prioritized practices within the healthcare industry based on their importance. This unique focus distinguishes this study from others in the field and contributes to the novelty of this paper.

Methods

This paper, a mixed-methods study conducted in 2023, employed a scoping review and thematic analysis to identify existing practices aimed at combating climate change and improving sustainability levels in healthcare centers, as documented in the literature. This constituted the first phase of the study. In the second phase, the authors engaged a group of experts to prioritize the most important practices in Iranian healthcare centers addressing climate change, utilizing the Grey MEREC-MABAC integrated approach (Fig. 1). Employing such a mixed-methods approach enables the authors to provide evidence-based information and implications for future directions aimed at mitigating climate change in healthcare centers, particularly within the Iranian healthcare system, which possesses distinct geological and socio-economic characteristics.

Fig. 1 [Images not available. See PDF.]

Study design

Phase one: scoping review

The research question for this phase of the study was formulated as: “What practices are implemented within healthcare centers in response to climate change?”.

Data resources and search strategy

The authors conducted a scoping review to identify relevant articles published between January 2000 and April 2023. The search commenced with the Cochrane Database of Systematic Reviews to identify pertinent systematic reviews on the topic. Subsequently, the search was extended to the databases of Scopus, PubMed, and ProQuest.

In the next step, the authors employed a 'backward tracing' approach, examining the references cited within the articles to identify studies that were not retrieved by the initial search strategy but were relevant to the topic. Multiple authors verified the results at each step of the search process to ensure reliability and minimize potential bias.

The authors initiated the search with relatively broad terms to increase sensitivity, using synonymous words with the "OR" operator. To maintain specificity and reduce irrelevant studies, the "AND" operator was employed within the search strategy. Table 1 presents the search strategy.

Table 1. The search strategy used for the scoping review

1	"green initiative" OR "green healthcare center" OR "climate change" OR "green management" OR "Environmental footprint" OR "sustainable performance"
2	"healthcare center" OR hospital*
#	#1 AND #2 AND [title/abstract/ keyword] [English] [type: research article] (2000–2023)

*The asterisk is primarily used as a truncation symbol, allowing researchers to search for any number of letters at the end of a word

^#Each domain or group of keywords within the search strategy

Inclusion and exclusion criteria

The authors applied the following inclusion criterion: articles in English published after 2000. Additionally, the following exclusion criteria were applied: articles without full texts or with unavailable full texts; duplicate articles; articles that did not address the concepts, strategies, and practices to enhance efficiency and sustainability in healthcare centers in response to climate change; and articles related to conferences and letters to editors.

Screening and data extraction

In the first stage, two authors reviewed all the articles from the databases multiple times. In the second stage, the abstracts of the selected articles and reports were reviewed. Subsequently, the full texts of the selected articles were thoroughly studied and evaluated. Finally, articles with adequate credibility were selected.

The authors extracted the necessary information from the included articles using a form that recorded the author, year of publication, country, and a summary of the study. In the next step, the data were entered and checked by the author multiple times. Excel software was used for the forms.

Data analysis

The objective of this phase was to ascertain the main themes of existing practices to combat climate change and improve sustainability levels in healthcare centers in response to climate change. The authors performed a thematic analysis on the items extracted from the contents of the final articles retrieved through the scoping review, adhering to Boyatzis's code development approach [22]. Boyatzis's approach consisted of the following stages:

(1) Two authors independently extracted practices from the final articles that could enhance the efficiency and sustainability of healthcare centers in response to climate change. (2) The authors familiarized themselves with the data. (3) The authors independently generated codes and themes from the data, and the thematic analysis provided several sub-themes for each theme based on the framework of the research question. (4) The authors repeated the thematic analysis steps to enhance the validity and reliability of the outcomes, to minimize any risk of error or bias within the analysis, and to adjust the results if necessary. During the process, if the two authors disagreed on any aspect of the stages, they consulted with the other authors to resolve the issue.

Phase two: prioritizing the practices using grey MEREC-MABAC integrated approach

The research question for this phase of the study was formulated as: "How are the existing practices, proposed for healthcare centers in response to climate change, prioritized in the view of Iranian experts?".

Data resources and sample size

During this phase, the perspectives of several experts were examined and incorporated through a questionnaire derived from the results of the thematic analysis conducted in the scoping review of the preceding phase. The experts included specialists and authorities in this field, such as healthcare center administrators, professors of health management, and researchers. Each expert had at least three years of experience in the field. The process of sampling and gathering information involved a sample size of seven experts, in accordance with previous studies in this context [23, 24]. In addition, such sample size was employed due to the complexity of the decision-making process and the multitude of criteria involved.

Inclusion and exclusion criteria

The inclusion criteria encompassed elements such as the experts’ familiarity with the concept of climate change and practices addressing climate change within healthcare centers. Conversely, the exclusion criteria were confined to their lack of consent to participate in the research at any stage.

Data gathering

The data collection process was executed through the design and distribution of a paper-based questionnaire, derived from the thematic analysis of the data from the scoping review conducted in the previous phase. The details of the questionnaire are presented in Appendix 2 (Questionnaire). Upon completion of the questionnaire responses by the authors, the data were subsequently transferred into an Excel file for analysis.

Data analysis

To analyze the data gathered through the distribution of the questionnaire among the experts, the Grey MEREC-MABAC integrated approach was utilized. This stage of the study was conducted by one of the authors using Microsoft Office Excel 2019. To carry out the analysis, the authors consulted with each other to identify the most significant climate indicators within the context, ultimately selecting seven indicators from the World Bank [25]. The key climate indicators are delineated in Table 2 within the results section.

Table 2. The results of the thematic analysis

No	Category	Sub-category	Code	Reference(s)
1	Green climate	Cooling and heating	Cooling system retrofitting	[32–35]
			Sensible heat recovery system	[36]
		Air conditioning	Variable air volume (VAV) systems	[33, 34]
			Variable Water Volume (VWV) systems	[33, 37]
2	Green energy	Solar energy	Solar photovoltaic (PV) technology	[37–52]
		Wind energy	Wind-turbine	[44, 50]
		Hybrid energy	Hybrid diesel-PV	[44, 53, 54]
			Hybrid diesel-wind	[44, 53, 54]
			Hybrid PV-wind	[44, 53]
		CNG	–	[43]
3	Green lightning	Automatic lighting control system	–	[55]
		LED lightning	–	[34, 35, 43, 46, 55, 56]
4	Green construction	Insulation	Exterior insulation finishing system (EIFS) 5 cm insulation thickness	[55]
			Two concrete block walls with a 10 cm polyester insulation	[55]
			Two red clay block walls with a 10 cm polyester insulation	[55]
			ACC block (density 400) 35 cm thickness	[55]
			Two volcanic block walls with a 10 cm polyester insulation	[55]
			Exterior insulation finishing system (EIFS) 10 cm insulation thickness	[55]
			Insulation of walls and roofs	[52]
		Light coloring	Light color of walls and roofs	[52]
		Underground construction	–	[57]
		Window optimization	Reflective double glass window	[34, 52, 55]
			Smart windows	[34, 38, 52]
			Window shading	[34, 39, 52, 58]
5	Green medicine	Telemedicine and telehealth	–	[59–62]
6	Green equipment waste and water management	High-temperature thermophilic anaerobic digester	–	[46]
		Decontamination and reusability of equipment	–	[35, 63]
		Recycling of waste	–	[37, 46, 50]
		Biogas cofire	–	[38]
		Electronic waste system	–	[64]
		Autoclaving of waste	–	[48]
		Harvesting and recycling of water	–	[37, 43, 46]
7	Green transportation	Electric transportation	–	[43, 46]
		CNG vehicles for transportation	–	[46]
8	Green Environment	Organic food farms	–	[46]
		Planting trees	–	[43, 46]
9	Green Information technology (IT)	Retrofitting IT system	–	[64]
		Big data	–	[64, 65]
		Mobile computing	–	[64, 65]
		Could computing	–	[64, 65]
		Cloud storage	–	[35]
		Internet of things(IoT)	–	[66]
10	Green Radiology	Usage of ultrasounds in radiology	–	[35]
		Usage of AI for MRI protocols	–	[35]
11	Green organizational Management	Education and training	Climate for health ambassador training program	[35]
			Auditing, training and staff education	[35, 43, 56]
		Benchmarking	–	[56]
		Creation of an environmental greening team	–	[67]

Grey MEREC

The MEREC (Method based on the Removal Effects of Criteria) methodology, initially introduced in 2021, is designed to determine the weight of various criteria by assessing the impact of each criterion’s removal on the performance of alternatives [26]. This method is the most recently developed technique for assigning weights to indicators in the decision matrix, and the results it provides are more stable and reliable compared to other techniques such as CRITIC and Entropy. The integration of this novel technique with grey theory, first proposed in Esangbedo and Tang's research paper in 2023, offers an exceptional approach to weighting indicators in uncertain situations [27]. In this methodology, criteria that significantly affect performance are assigned higher weights. The performance of alternatives is calculated using a logarithmic measure, which highlights the disparity between the overall performance of an alternative and its performance upon the removal of a criterion. Furthermore, grey system theory focuses on addressing the uncertainty issues associated with small data sets and poor information quality. The Grey MEREC, a hybrid MCDM weighting method, involves obtaining a grey normalized decision matrix. The kernel is then calculated to measure the logarithmic performance of the alternatives. The steps to implement the Grey MEREC method are as follows [27]:

Step 1: The construction of the grey decision matrix is the initial step. Given ‘m’ alternatives and ‘n’ criteria, the grey decision matrix is derived based on the grey performance values (xij) of alternative-i and criterion-j obtained (Eq 1).

1

$$\otimes X=\left[\begin{array}{c}\begin{array}{ccc}\otimes x_{11}& \otimes x_{12}& \dots \end{array}\begin{array}{ccc}\otimes x_{1j}& \dots & \otimes x_{1n}\end{array}\\ \begin{array}{ccc}⋮& ⋮& \ddots \end{array}\begin{array}{ccc}⋮& \ddots & ⋮\end{array}\\ \begin{array}{ccc}\otimes x_{i1}& \otimes x_{i2}& \dots \end{array}\begin{array}{ccc}\otimes x_{\mathit{ij}}& \dots & \otimes x_{\mathit{in}}\end{array}\\ \begin{array}{ccc}⋮& ⋮& \ddots \end{array}\begin{array}{ccc}⋮& \ddots & ⋮\end{array}\\ \begin{array}{ccc}\otimes x_{m1}& \otimes x_{m2}& \dots \end{array}\begin{array}{ccc}\otimes x_{\mathit{mj}}& \dots & \otimes x_{\mathit{mn}}\end{array}\end{array}\right]$$

Step 2: Normalization of the grey decision matrix (⊗N). Normalization of both benefit and cost indicators is performed using Eq. 2. In the context of this research, it should be noted that the first and fourth indicators represent higher emission and pollution (negative), while the remaining indicators are associated with less emission and pollution (positive).

2

$$\otimes n_{\mathit{ij}}^x=\left[{\underline{n}}_{\mathit{ij}}^x,{\overline{n}}_{\mathit{ij}}^x\right]=\left\{\begin{array}{c}\left[\frac{\begin{array}{c}min{\underline{x}}_{\mathit{lj}}\\ l\end{array}}{{\overline{x}}_{\mathit{ij}}},\frac{\begin{array}{c}min{\underline{x}}_{\mathit{lj}}\\ l\end{array}}{{\underline{x}}_{\mathit{ij}}}\right]ifj\in \mathrm{B}\\ \\ \left[\frac{{\underline{x}}_{\mathit{ij}}}{\begin{array}{c}max{\overline{x}}_{\mathit{lj}}\\ l\end{array}},\frac{{\overline{x}}_{\mathit{ij}}}{\begin{array}{c}max{\overline{x}}_{\mathit{lj}}\\ l\end{array}}\right]ifj\in C\end{array}\right)$$

Step 3: Calculation of the kernel of the normalized decision matrix. Through utilization of the kernel, the gray matrix turns into a crisp matrix. In other words, the grey values are transformed into white values through the application of the kernel, as described in Eq. 3.

3

$$k_{\mathit{ij}}^x=\left({\underline{n}}_{\mathit{ij}}^x,+,{\overline{n}}_{\mathit{ij}}^x\right)/2$$

Step 4: Calculation of the overall performance of the alternatives (Si). The overall performance values for all alternatives are computed using the non-linear logarithmic function delineated in Eq. 4.

4

$$S_i=\text{ln}\left(1,+,\left(\frac{1}{m},\sum _j,\left|\text{ln},\left(k_{\mathit{ij}}^x\right)\right|\right)\right)$$

Step 5: Calculation of the performance of the alternatives by removing each criterion. The computation of the overall performance of alternatives is conducted by individually eliminating each criterion. This process is represented in Eq. 5, utilizing the matrix notation $S_{\mathit{ij}}^{\prime }$.

5

$${S\prime }_{\mathit{ij}}=\text{ln}\left(1,+,\left(\frac{1}{m},\sum _{k,k\ne j},\left|\text{ln},\left(n_{\mathit{ik}}^x\right)\right|\right)\right)$$

Step 6: Computation of the summation of absolute deviations. The impact of the overall performance, as determined in Step 4, and the performance resulting from the elimination of each criterion, as outlined in Step 5, are computed using Eq. 6.

6

$$E_j=\sum _i\left|{S^{\prime }}_{\mathit{ij}},-,S_i\right|$$

Step 7: Determination of the final weights of the criteria. The final weight is derived by normalizing the $E_j$ values, as detailed in Eq. 7.

7

$$W_j=\frac{E_j}{{\sum }_k{E}_k}$$

Grey MABAC (MABAC-G)

Upon determining the weight of key performance indicators via the Grey MEREC method, the prioritization of the practices identified from the scoping review is executed through the application of the Grey MABAC (Multi-Attribute Border Approximation Area Comparison) method. Introduced in 2015, the MABAC methodology was designed for the prioritization of alternatives [28]. It has been demonstrated that the MABAC method offers advantages in generating consistent solutions. The framework for utilizing the MABAC method involves defining the distance of the criterion function from each alternative, based on the Boundary Approximation Area (BAA). The MABAC-G represents an extended version of the traditional MABAC. The MABAC method has the advantage of producing consistent solutions under similar conditions compared to other techniques such as the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and VIekriterijumsko KOmpromisno Rangiranje (VIKOR). It is a predominantly practical and consistent tool for rational decision-making, as evidenced by research conducted by Xue et al. in 2016 [29]. Moreover, the structure of the MABAC method can be defined as analogous to prospect theory, affirming that individuals can make conclusions based on the potential value of gains and losses rather than on final outcomes.

Furthermore, the integration of grey system theory (GST) and MABAC offers significant advantages in the selection of optimal alternatives. In the traditional MABAC method, evaluations are performed using crisp ratings. However, in real-life problems, experts may find it inappropriate and inflexible to assess performance solely with precise numbers. This approach provides decision-makers with greater flexibility in articulating their opinions and evaluation ratings. Additionally, grey systems theory effectively captures the inherent fuzziness of real-world situations, representing a key advantage over fuzzy set theory, as it does not necessitate the use of robust fuzzy membership functions [30]. The procedure for implementing the MABAC-G encompasses the following steps [31]:

Step 1: The construction of the aggregate grey decision matrix is the initial step. Given ‘m’ alternatives, ‘n’ criteria, and ‘k’ experts, the aggregate grey decision matrix is derived through the application of Eq. 8.

8

$$\begin{array}{cc}\hfill \widehat{X}={\left[\otimes x_{\mathit{ij}}\right]}_{m\times n}& ={\left[\begin{array}{c}\left[{\underline{x}}_{11},{\overline{x}}_{11}\right]\left[{\underline{x}}_{12},{\overline{x}}_{12}\right]\dots \left[{\underline{x}}_{1n},{\overline{x}}_{1n}\right]\\ \left[{\underline{x}}_{21},{\overline{x}}_{21}\right]\left[{\underline{x}}_{22},{\overline{x}}_{22}\right]\dots \left[{\underline{x}}_{2n},{\overline{x}}_{2n}\right]\\ \dots \dots \dots \dots \dots \dots \\ \left[{\underline{x}}_{m1},{\overline{x}}_{m1}\right]\left[{\underline{x}}_{m2},{\overline{x}}_{m2}\right]\dots \left[{\underline{x}}_{\mathit{mn}},{\overline{x}}_{\mathit{mn}}\right]\end{array}\right]}_{m\times n}\hfill \\ \hfill {\underline{x}}_{\mathit{ij}}& =\sum _{k=1}^K{\sigma }_k.{\underline{x}}_{\mathit{ij}}^k;and{\overline{x}}_{\mathit{ij}}=\sum _{k=1}^K{\sigma }_k.{\overline{x}}_{\mathit{ij}}^k\hfill \\ \hfill \end{array}$$

Step 2: Normalization of the grey decision matrix (⊗N). Normalization for both benefit and cost indicators is performed using Eq. 9. In the context of this research, it is important to note that the first and fourth indicators represent higher emission and pollution (negative), while the remaining indicators are associated with less emission and pollution (positive). The normalized grey decision matrix is represented as per Eq. 10.

9

$$\begin{array}{cc}\hfill \otimes y_{\mathit{ij}}& =\left[{\underline{y}}_{\mathit{ij}},{\overline{y}}_{\mathit{ij}}\right]=\left[\frac{{\underline{x}}_{\mathit{ij}}}{x_j^{\mathit{max}}},\frac{{\overline{x}}_{\mathit{ij}}}{x_j^{\mathit{max}}}\right]ifj\in \text{B}\hfill \\ \hfill \otimes y_{\mathit{ij}}& =\left[{\underline{y}}_{\mathit{ij}},{\overline{y}}_{\mathit{ij}}\right]=\left[\frac{x_j^{\mathit{min}}}{{\overline{x}}_{\mathit{ij}}},\frac{x_j^{\mathit{min}}}{{\underline{x}}_{\mathit{ij}}}\right]ifj\in \text{C}\hfill \\ \hfill \end{array}$$

10

$$\widehat{Y}={\left[\otimes y_{\mathit{ij}}\right]}_{m\times n}={\left[\begin{array}{c}\left[{\underline{y}}_{11},{\overline{y}}_{11}\right]\left[{\underline{y}}_{12},{\overline{y}}_{12}\right]\dots \left[{\underline{y}}_{1n},{\overline{y}}_{1n}\right]\\ \\ \left[{\underline{y}}_{21},{\overline{y}}_{21}\right]\left[{\underline{y}}_{22},{\overline{y}}_{22}\right]\dots \left[{\underline{y}}_{2n},{\overline{y}}_{2n}\right]\\ \dots \dots \dots \dots \dots \dots \\ \left[{\underline{y}}_{m1},{\overline{y}}_{m1}\right]\left[{\underline{y}}_{m2},{\overline{y}}_{m2}\right]\dots \left[{\underline{y}}_{\mathit{mn}},{\overline{y}}_{\mathit{mn}}\right]\end{array}\right]}_{m\times n}$$

Step 3: Calculation of the weighted normalized decision matrix. The elements of the weighted matrix, denoted as $\otimes f_{\mathit{ij}}$, are derived using Eq. 11. Here, $W_j$ represents the weight of the ‘j-th’ criterion, which is calculated based on the Grey MEREC method.

11

$$\begin{array}{cc}\hfill \otimes f_{\mathit{ij}}& =\left[{\underline{f}}_{\mathit{ij}},{\overline{f}}_{\mathit{ij}}\right]=W_j\times \otimes y_{\mathit{ij}}=\left[W_j.{\underline{y}}_{11},W_j.{\overline{y}}_{11}\right]\hfill \\ \hfill \widehat{F}& ={\left[\otimes f_{\mathit{ij}}\right]}_{m\times n}={\left[\begin{array}{c}\left[{\underline{f}}_{11},{\overline{f}}_{11}\right]\left[{\underline{f}}_{12},{\overline{f}}_{12}\right]\dots \left[{\underline{f}}_{1n},{\overline{f}}_{1n}\right]\\ \\ \left[{\underline{f}}_{21},{\overline{f}}_{21}\right]\left[{\underline{f}}_{22},{\overline{f}}_{22}\right]\dots \left[{\underline{f}}_{2n},{\overline{f}}_{2n}\right]\\ \dots \dots \dots \dots \dots \dots \\ \left[{\underline{f}}_{m1},{\overline{f}}_{m1}\right]\left[{\underline{f}}_{m2},{\overline{f}}_{m2}\right]\dots \left[{\underline{f}}_{\mathit{mn}},{\overline{f}}_{\mathit{mn}}\right]\end{array}\right]}_{m\times n}\hfill \\ \hfill \end{array}$$

Step 4: Determination of the grey border approximation area (BBA) matrix $\widehat{G}$. The determination of this matrix is predicated on the geometric mean for each criterion, as outlined in Eq. 12.

12

$$\begin{array}{cc}\hfill \otimes g_j& =\left[{\underline{g}}_j,{\overline{g}}_j\right]=\left[{\left(\prod _{i=1}^m{\underline{f}}_{\mathit{ij}}\right)}^{1\left./\phantom{1m}\right)m},{\left(\prod _{i=1}^m{\overline{f}}_{\mathit{ij}}\right)}^{1\left./\phantom{1m}\right)m}\right]\hfill \\ \hfill \widehat{G}& ={\left[\begin{array}{c}\left[{\underline{g}}_1,{\overline{g}}_1\right]\left[{\underline{g}}_2,{\overline{g}}_2\right]\dots \left[{\underline{g}}_n,{\overline{g}}_n\right]\\ \\ \left[{\underline{g}}_1,{\overline{g}}_1\right]\left[{\underline{g}}_2,{\overline{g}}_2\right]\dots \left[{\underline{g}}_n,{\overline{g}}_{2n}\right]\\ \dots \dots \dots \dots \dots \dots \\ \left[{\underline{g}}_1,{\overline{g}}_1\right]\left[{\underline{g}}_2,{\overline{g}}_2\right]\dots \left[{\underline{g}}_n,{\overline{g}}_n\right]\end{array}\right]}_{m\times n}\hfill \\ \hfill \end{array}$$

Step 5: Calculation of the preference index matrix (Q). The computation of this matrix involves the use of the Euclidean distance between the grey numbers $\otimes f_{\mathit{ij}}$ and $\otimes g_j$, as detailed in Eq. 13. Furthermore, Appendix 3 (Basic Operations of interval grey number) presents more details regarding the calculation of the grey numbers).

13

$$Q=\widehat{F}-\widehat{G}={\left[q_{\mathit{ij}}^k\right]}_{m\times n}{\left[\begin{array}{c}d\left(\otimes f_{11},\otimes g_1\right)d\left(\otimes f_{12},\otimes g_2\right)\dots d\left(\otimes f_{1n},\otimes g_n\right)\\ d\left(\otimes f_{21},\otimes g_1\right)d\left(\otimes f_{22},\otimes g_2\right)\dots d\left(\otimes f_{2n},\otimes g_n\right)\\ \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \dots \\ d\left(\otimes f_{m1},\otimes g_n\right)d\left(\otimes f_{m2},\otimes g_n\right)\dots d\left(\otimes f_{\mathit{mn}},\otimes g_n\right)\end{array}\right]}_{m\times n}$$

The calculation of preference indices for both benefit and cost criteria are conducted using the respective equations provided below:

14

$$q_{\mathit{ij}}=\left\{\begin{array}{c}d\left(\otimes f_{\mathit{ij}},\otimes g_j\right)if\otimes f_{\mathit{ij}}>\otimes g_j\\ -d\left(\otimes f_{\mathit{ij}},\otimes g_j\right)if\otimes f_{\mathit{ij}}<\otimes g_j\end{array}\right)$$

15

$$q_{\mathit{ij}}=\left\{\begin{array}{c}-d\left(\otimes f_{\mathit{ij}},\otimes g_j\right)if\otimes f_{\mathit{ij}}>\otimes g_j\\ d\left(\otimes f_{\mathit{ij}},\otimes g_j\right)if\otimes f_{\mathit{ij}}<\otimes g_j\end{array}\right)$$

Step 6: Prioritization of the alternatives. The Closeness Coefficient (CC) for each alternative, relative to the Boundary Approximation Area (BAA), is computed by summing the elements of each row in the matrix (Q), as defined in Eq. 16. It should be noted that an alternative’s priority increases with the value of its CC.

16

$$CC\left(A_i\right)=\sum _{j=1}^n{q}_{\mathit{ij}}=\sum _{j=1}^{n}d\left(\otimes f_{\mathit{ij}},\otimes g_j\right);i=1,2,\dots ,n.$$

Results

The results of the study are manifested within the following two sections:

Phase one

Scoping review

After conducting a search in the databases of PubMed, Scopus, ProQuest, and the Cochrane Library, a total of 8,749 articles were retrieved. Of these, 410 articles were identified as duplicates. Following the independent review of titles and abstracts by two authors, 8,291 articles were excluded. Furthermore, after assessing the eligibility of the remaining articles, an additional 10 were excluded, resulting in a final selection of 38 articles for further study. Figure 2 presents the PRISMA flowchart for the eligible included studies.

Fig. 2 [Images not available. See PDF.]

PRISMA Flow diagram of the scoping review

The descriptive analysis of the included studies revealed that out of the 38 articles examined, 29 (76.31%) employed a quantitative design, while 9 (23.68%) utilized a qualitative approach. The majority of the studies, constituting 63%, were conducted in the USA (18.42%), India (10.52%), Saudi Arabia (10.52%), and Malaysia (7.89%). Canada, the Philippines, and Italy each accounted for 5.25% of the studies, while the remainder were carried out in other countries.

The included studies aimed to assess various aspects related to renewable energy sources, energy-saving strategies, green building designs, and telehealth services in healthcare settings. Specifically, they focused on evaluating energy consumption, greenhouse gas emissions, environmental impact, and economic feasibility. Among the renewable energy sources discussed, solar photovoltaic systems were the most prevalent, followed by wind turbines and batteries. Green building design emerged as the most commonly discussed energy-saving strategy, followed by telehealth services and waste minimization and management. The studies primarily emphasized greenhouse gas emissions as the most prevalent environmental impact indicator, followed by energy consumption and cost savings. Appendix 1 (Bibliography of Final Studies) provides a summary of the characteristics of the included studies.

Thematic analysis

Following a thematic analysis of the findings, eleven main categories emerged: Green Climate, Green Energy, Green Lighting, Green Construction, Green Medicine, Green Equipment, Waste and Water Management, Green Transportation, Green Environment, Green Information Technology (IT), Green Radiology, and Green Organizational Management (Fig. 3). Table 2 provides a comprehensive list of the identified categories, subcategories, and initial codes. The following delineates the description of each of the eleven categories that emerged from the thematic analysis:

Fig. 3 [Images not available. See PDF.]

Practices to combat climate change in healthcare centers

3.1.2.1. Green Climate pertains to methodologies or technologies designed to enhance the efficiency and performance of heating and cooling systems within buildings or facilities.

3.1.2.2. Green Energy encompasses sources or types of renewable or alternative energy aimed at reducing reliance on fossil fuels and decreasing greenhouse gas emissions.

3.1.2.3. Green Lighting involves devices or systems that regulate illumination and conserve energy consumption in lighting within buildings or facilities.

3.1.2.4. Green Construction refers to materials or techniques that augment thermal comfort and energy efficiency in buildings or structures.

3.1.2.5. Green Medicine involves the utilization of information and communication technologies to deliver remote healthcare services and minimize the environmental impact of healthcare delivery.

3.1.2.6. Green Equipment, Waste, and Water Management denotes processes or technologies that treat or dispose of waste generated by equipment or activities in an eco-friendly manner, and conserve or reuse water resources within buildings or facilities.

3.1.2.7. Green Transportation involves modes or vehicles that reduce fuel consumption and emissions in urban or rural transportation.

3.1.2.8. Green Environment encompasses practices or activities that foster biodiversity and sustainability in the natural environment and food production.

3.1.2.9. Green Information Technology (IT) refers to applications or innovations that optimize performance and reduce energy consumption of IT systems or devices across various domains.

3.1.2.10. Green Radiology involves techniques or tools that enhance the quality and efficiency of radiological imaging and diagnosis while minimizing radiation exposure or waste generation.

3.1.2.11. Green Organizational Management pertains to strategies or initiatives that elevate awareness and knowledge of practices addressing climate change among an organization’s employees or stakeholders, and measure or improve their performance in implementing these practices.

Phase 2: prioritizing the most important practices addressing climate change in healthcare centers

As previously detailed in the methods section, this phase of the study aimed to prioritize the most critical practices in Iranian healthcare centers utilizing the Grey MEREC and Grey MABAC (MABAC-G) methodologies. To this end, the authors selected seven climate indicators as performance variables. This selection was made with the objective of facilitating the ranking of practices, which were identified through a comprehensive scoping review. Table 3 delineates the climate indicators and practices employed in the current study.

Table 3. Key climate indicators and practices addressing climate change within healthcare centers

Performance(functional) variables (Climate indicators)
P1	Total greenhouse gas emissions (kt of CO2 equivalent)	P5	Access to clean fuels and technologies for cooking (% of population)
P2	Renewable energy consumption (% of total final energy consumption)	P6	Forest area (% of land area)
P3	Agricultural land (% of land area)	P7	Renewable internal freshwater resources per capita (cubic meters)
P4	PM2.5 air pollution, mean annual exposure (micrograms per cubic meter)
Practices addressing climate change
C1	Green climate	C7	Green transportation
C2	Green energy	C8	Green Environment
C3	Green lightning	C9	Green Information Technology (IT)
C4	Green construction	C10	Green Radiology
C5	Green medicine	C11	Green organizational Management
C6	Green equipment, waste and water management

The aggregated grey matrix, which compiles expert opinions, has been calculated and is presented in Table 4. It is important to note that within this context, the first and fourth indicators represent negative attributes, where lower values correspond to higher benefits. Conversely, the remaining indicators represent positive attributes, with higher values indicating greater benefits.

Table 4. Aggregated grey matrix

Aggregate	P1		P2		P3		P4		P5		P6		P7
C1	3.428	4.428	3.142	4.142	2.142	3.142	2.571	3.571	2.833	3.833	3.166	4.166	2.5	3.5
C2	3.428	4.428	3.285	4.285	2.428	3.428	2.714	3.714	2.666	3.666	2.833	3.833	2.666	3.666
C3	2.571	3.571	2.571	3.571	1.571	2.571	1.857	2.857	2.333	3.333	2.166	3.166	1.5	2.5
C4	2.428	3.428	2.571	3.571	2.142	3.142	1.857	2.857	1.666	2.666	2.5	3.5	2.333	3.333
C5	2.571	3.571	2.571	3.571	1.714	2.714	2.428	3.428	2.5	3.5	2.5	3.5	2.5	3.5
C6	2.857	3.857	2.571	3.571	2.428	3.428	2.857	3.857	2.666	3.666	3.166	4.166	3.333	4.333
C7	3	4	2.428	3.428	2	3	3	4	2.5	3.5	2.833	3.833	2.333	3.333
C8	3.142	4.142	2.857	3.857	3.428	4.428	3	4	2.666	3.666	3.333	4.333	2.833	3.833
C9	2	3	2.142	3.142	1.857	2.857	1.857	2.857	2.333	3.333	1.833	2.833	1.833	2.833
C10	1.857	2.857	1.571	2.571	1.571	2.571	1.714	2.714	1.666	2.666	1.666	2.666	1.666	2.666
C11	2.857	3.857	2.857	3.857	2.571	3.571	2.857	3.857	2.5	3.5	3	4	3	4

By employing the Kernel, the overall performance values of the practices (S) and the weights of the seven indicators were extracted, as detailed in Table 5.

Table 5. Overall performance values of practices addressing climate change

$n_{\mathit{ij}}$	P1	P2	P3	P4	P5	P6	P7	S
C1	0.8870968	0.439655	0.616667	0.767857	0.511509	0.463158	0.514286	0.29634
C2	0.8870968	0.422464	0.552696	0.803571	0.539773	0.511509	0.485795	0.296871
C3	0.6935484	0.525556	0.805556	0.589286	0.607143	0.647773	0.8	0.234759
C4	0.6612903	0.525556	0.616667	0.589286	0.8125	0.571429	0.546429	0.272158
C5	0.6935484	0.525556	0.747807	0.732143	0.571429	0.571429	0.514286	0.269043
C6	0.7580645	0.525556	0.552696	0.839286	0.539773	0.463158	0.398077	0.309878
C7	0.7903226	0.552696	0.654762	0.875	0.571429	0.511509	0.546429	0.256728
C8	0.8225806	0.478704	0.406586	0.875	0.539773	0.442308	0.460358	0.321661
C9	0.5645161	0.616667	0.698077	0.589286	0.607143	0.748663	0.673797	0.250176
C10	0.5322581	0.805556	0.805556	0.553571	0.8125	0.8125	0.73125	0.196069
C11	0.7580645	0.478704	0.525556	0.839286	0.571429	0.486111	0.4375	0.306132

As illustrated in Table 6, the results indicate that Renewable Energy Consumption (P2) has been identified as the highest priority climate indicator by the expert participants involved in the study. Conversely, the total greenhouse gas emissions indicator was deemed to have the lowest level of importance among the climate indicators.

Table 6. Ranking of climate indicators

	Climate indicator	W	Rank
P1	Total greenhouse gas emissions (kt of CO2 equivalent)	0.0948	7
P2	Renewable energy consumption (% of total final energy consumption)	0.1840	1
P3	Agricultural land (% of land area)	0.1349	5
P4	PM2.5 air pollution, mean annual exposure (micrograms per cubic meter)	0.0954	6
P5	Access to clean fuels and technologies for cooking (% of population)	0.1465	4
P6	Forest area (% of land area)	0.1687	3
P7	Renewable internal freshwater resources per capita (cubic meters)	0.1753	2

The coefficient of weights, derived through the Grey MEREC methodology, facilitated the creation of the weighted grey decision matrix. The final results, obtained via the MABAC methodology, are presented in Table 7. "Green environment," "green equipment, waste and water management," and "green energy" were identified as the highest priority practices among those reviewed in the literature. Conversely, "green radiology" was deemed to have the lowest priority.

Table 7. Ranking of practices addressing climate change

	Practice	CC	Rank
C1	Green climate	0.0618	4
C2	Green energy	0.0653	3
C3	Green lightning	−0.0371	9
C4	Green construction	−0.0358	8
C5	Green medicine	0.0136	7
C6	Green equipment, waste and water management	0.0677	2
C7	Green transportation	0.0202	6
C8	Green Environment	0.0929	1
C9	Green Information technology (IT)	−0.0668	10
C10	Green Radiology	−0.1130	11
C11	Green organizational Management	0.0608	5

Discussion

The findings of the study revealed a variety of practices identified through the scoping review. Furthermore, the outcomes of the second phase demonstrated that these practices exhibited varying levels of priority relative to one another. In this segment of the study, we analyzed the practices with the highest priority identified during the second phase. Our objective was to analyze and compare these findings with the existing body of literature on the subject.

As delineated by the study results, practices under the category of "Green environment" refer to activities that promote biodiversity and sustainability of the natural environment and food production, such as organic food farms and tree planting [43, 46]. The prioritization of this practice may be attributed to its evident positive impacts on addressing the climate change crisis. In this context, the World Bank has reported that nature-based solutions could contribute to 37% of climate change mitigation efforts [68]. Alternatively, the arid conditions prevalent in Iran and the Middle East as a whole may have influenced the authors to place a higher emphasis on this practice compared to other strategies [69].

Our findings regarding tree planting are consistent with prior literature, which has identified it as a top-priority strategy for Iran to address and adapt to the impacts of climate change. Reforestation and afforestation initiatives have the potential to rehabilitate degraded ecosystems, enhance carbon sequestration, regulate water cycles, and mitigate climate-related disasters such as floods and droughts [70]. Furthermore, previous literature has demonstrated the influence of natural elements, particularly trees, on environmental comfort within Iranian gardens, emphasizing the significance of specific tree species in providing shade and enriching the urban landscape [71].

Regarding the initiative of organic food farms, Iran has addressed the impacts of climate change on food production by enhancing the resilience of rural farmers. This has involved researching the degree of farmers’ resilience against climate change, strategizing to boost their adaptability, and promoting sustainable agriculture. Emphasis has been placed on regional planning to tackle varying climate challenges, enabling the identification of priorities and the development of effective plans [72, 73]. Furthermore, Iran has undertaken initiatives to enhance soil preservation techniques, curtail deforestation, and adopt sustainable agricultural practices in an effort to conserve natural resources and mitigate environmental degradation [74].

Iran's agricultural land has been extensively evaluated for suitability based on soil properties, topography, and climate. Despite our study's prioritization of planting organic food farms, literature findings indicate that significant portions of land are unsuitable due to factors such as low precipitation, limited soil organic carbon, steep slopes, and high soil sodium content. The limited regions with good suitability pose challenges for achieving food self-sufficiency due to environmental constraints and unsustainable practices. Consequently, large-scale organic food farms may face obstacles due to prevailing limitations in soil quality and other factors [75]. Given these substantial challenges to prioritizing tree planting in Iran as a solution to address climate change, policymakers and researchers must devise comprehensive solutions to tackle this issue.

As delineated by the study results, practices under the category of "Green equipment waste and water management" refer to processes or technologies that treat or dispose of waste generated by equipment or activities in an eco-friendly manner and conserve or reuse water resources in buildings or facilities. Examples include high-temperature thermophilic anaerobic digesters, decontamination and reusability of equipment, recycling of waste, biogas co-firing, electronic waste systems, autoclaving of waste, and harvesting and recycling of water [37, 43, 46]. Our research findings regarding waste and water management as a policy to mitigate climate change are corroborated by prior literature, which emphasizes the significance of sustainable wastewater treatment practices in addressing environmental challenges and enhancing resource efficiency in Iran [76].

The practice of waste management gains importance considering the estimated global growth rate of healthcare waste management costs, projected to increase to USD 17.89 billion by 2026, with a compound annual growth rate of 5.3% [77]. Additionally, the generation and management of medical waste, which became unsustainable during the COVID-19 pandemic in 2020, poses an imminent risk of causing environmental pollution and a public health crisis unless it is safely and properly contained [78].

As presented in the results section, practices under the category of "Green energy" refer to the use of renewable or alternative energy sources that can reduce dependence on fossil fuels and lower greenhouse gas emissions. Examples include PV technology, CNG, and mixed technologies such as wind turbines, hybrid diesel-PV, hybrid diesel-wind, and hybrid PV-wind [37–52]. This finding aligns with significant global policies initiated by international organizations, which propose strategies on a global scale. In this context, the provision of affordable, reliable, sustainable, and modern energy services to all people is one of the Sustainable Development Goals (SDGs) that the United Nations (UN) aims to achieve by 2030 [79]. Solar photovoltaic (PV) technology, also known as solar panels, is a widely adopted method to improve the efficiency and sustainability of healthcare centers [80].

The practices corresponding to "Green energy" appear to possess the potential to diminish the disparity between rural and urban regions over an extended period. In this regard, solar panels have demonstrated the potential to enhance energy access, particularly for remote and rural locations. Empirical studies on energy availability for healthcare facilities in the Global South indicate that photovoltaics constitute the main energy source for facilities in rural contexts [80, 81]. However, it should be noted that, based on scientific literature, wind energy surpasses photovoltaic energy in terms of output for the same cost when the wind speed is above 4.5 m/s, although wind turbines require more maintenance than PV panels [82].

The evidence suggests that healthcare facilities in areas with poor or unreliable grid energy must rely on decentralized power sources. Since both generators and renewable energy sources have advantages and disadvantages, the optimal solution is to integrate both systems (hybrid systems) to optimize the benefits and mitigate the drawbacks. Furthermore, the evidence indicates that hybrid energy systems are a viable option for large healthcare facilities, such as rural healthcare centers [83].

Limitations and implications

This study acknowledges certain limitations and implications that need to be addressed.

Managerial/practical

The findings of this study have the potential to benefit a diverse array of stakeholders within healthcare systems, including policymakers and administrators seeking to implement strategies to combat the climate change crisis, as well as researchers in the field. As the results of the study indicated, focusing on the practice of a "green environment," which includes measures such as planting trees and establishing organic farms, can be a significant proposal for Iranian policymakers and administrators to adopt in order to confront climate change within their respective healthcare systems. This implication can be beneficial for stakeholders residing in regions with similar socioeconomic and environmental conditions, such as the Middle East as a whole.

Theoretical

The experts consulted within the study were exclusively from Iran, and their perspectives may have been influenced by the unique socioeconomic and ecological characteristics of the region. This suggests an opportunity for future research to engage experts from various continents on an international stage, thereby fostering a more global perspective on the subject matter. Moreover, the study's scope, confined to healthcare centers, precluded the inclusion of experts whose primary profession pertains to climate change; As, these professionals exhibited a lack of familiarity with the structural and organizational characteristics of healthcare centers. This limitation should be acknowledged as a constraint of the study.

We recommend that future research endeavors thoroughly investigate the effects of the practices identified in the current study, particularly those with higher priority. This would provide a more comprehensive and precise understanding of the most efficient and effective practices for confronting the climate change crisis within healthcare centers worldwide. This approach could significantly contribute to the development of robust, climate-resilient healthcare infrastructures globally.

Societal

The findings of the study may be advantageous for the general population by enhancing environmental health and offering improved protection against environmental hazards.

Conclusions

This study initially undertook a comprehensive assessment of literature pertaining to practices implemented in healthcare centers to address climate change. These practices spanned various areas, including energy, construction, healthcare services, and organizational management. Each area encompassed a unique set of practices aimed at mitigating climate change and enhancing sustainability within healthcare centers. In the next step, the study prioritized the practices obtained from the literature review, recognizing that prioritizing these practices provides valuable insights into their significance for stakeholders such as healthcare administrators and policymakers. The study proposed that practices or activities promoting biodiversity and environmental sustainability, such as organic food farming and tree planting, should be considered paramount approaches in Iranian healthcare centers to combat climate change, based on the views of a group of experts. Furthermore, the study suggested that "Renewable Energy Consumption" should be regarded as a key climate indicator for evaluating practices aimed at addressing climate change in Iranian healthcare centers. Finally, the findings of the study appeared to correlate with the distinctive socioeconomic and ecological features of Iran.

Acknowledgements

There are no acknowledgements regarding the research.

Author contributions

MK conducted the review and wrote the text of the manuscript, ZZ cooperated in the review and writing of the manuscript; MAM cooperated in theorizing the project and writing of the manuscript; RI cooperated in writing of the manuscript; PS conducted the analaysis and cooperated in the revision of the manuscript.

Funding

There is no funding regarding the research.

Data availability

The study data is available through making contact with the corresponding author.

Declarations

Ethics approval and consent to participate

We adhered to all of the corresponding guidelines throughout the process of conducting the research and the manuscript was approved by the ethical committee of Shiraz University of Medical Sciences (SUMS) with the ID: IR.SUMS.NUMIMG.REC.1402.126.

Informed consent

Informed consents were obtained from all of the study participants.

Competing interests

The authors declare no competing interests.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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