Comprehensive Tool for Assessing Farmers’ Knowledge and Perception of Climate Change and Sustainable Adaptation: Evidence from Himalayan Mountain Region

Abstract

Knowledge and perceptions are prerequisites for contributing to CC mitigation and adaptation. This paper developed a framework and a tool (scale) to capture farmers’ knowledge and perceptions of all aspects of CC. We involved 15 extremely qualified (those with PhD degrees in agriculture and allied disciplines and experience in scale construction and CC research) experts and 83 highly qualified (a minimum of a PhD degree in agriculture and allied fields was the prerequisite criterion for acting as a judge) judges in the construction of this scale. Further, we adopted factor analysis to draw valid conclusions. We proposed 138 items/statements related to 14 dimensions/issues (General, GHGs, Temperature, Rainfall, Agricultural emissions, shifting cultivation, rice cultivation, Mitigation, C-sequestration, Impact on Agriculture, Livestock, Wind, Natural disaster, Impact, and Adaptation) associated with agriculture and CC scenarios. Finally, 102 items/statements were retained with six indicators/dimensions. The results indicate that the scale explains 83% of variance. The scale is highly consistent (Cronbach alpha = 0.985) and widely applicable to future research and policy decisions. Further, the scale was adopted (with 100 respondents) to assess consistency and validity. Finally, the tool (scale) for assessing farmers’ knowledge and perceptions of CC was prepared for further use and replication. The policy and research system may adopt the framework and scale to assess stakeholders’ inclusive knowledge and perceptions of CC. The findings of this study may be helpful for policymakers, researchers, development workers, and extension functionaries.

1. Introduction

Climate change (CC) is a global challenge; developing countries are the most vulnerable to it [1,2], and CC mitigation policy should emphasize the economic development of developing countries [3]. Various drivers, namely, construction and infrastructure development, the power and electric sector, industry, fossil fuel burning, and agriculture, contribute to CC and global warming through the emission of greenhouse gases (GHGs) [1,4]. However, to some extent, crop cultivation is emission-neutral and C-sequestering, but some crop cultivation emits GHGs. Further, agriculture is highly vulnerable to CC and global warming [1,5,6,7,8,9]. The influence of CC and global warming on agriculture varies in degree and nature [1,10,11,12]. Therefore, assessing the effect of CC on agriculture is essential for management and policymaking regarding the mitigation of and adaptation to CC [13]. For instance, global warming primarily and negatively impacts the temperate crops of mountain and plain ecosystems. Paradoxically, it has a positive influence on non-cold-loving/non-thermo-sensitive crops. The nature and degree of influence also differ across mountainous, coastal [14], and plain agroecological situations.

This study was conducted in the Himalayan Mountain region of India. Among mountain ecosystems, the Himalayan Mountain region is the most vulnerable to CC and global warming [5]. The Himalayan Mountain region is experiencing significant changes in climate, consistent with broader warming and climatic shifts across Northeast India [15]. Recent warming trends in both the minimum and maximum temperatures are evident across the region, contributing to greater heat stress and altered seasonal cycles [16,17]. Similarly, precipitation patterns have also become more variable, including a declining frequency of monsoon rainfalls and increased frequency of extreme rainfall events. This makes the region vulnerable to both flood and drought conditions and increases the risk of agricultural disruptions [18]. In Nagaland, historical climate data reveal increasing temperatures and high inter-annual rainfall variability, which together affect groundwater recharge, crop productivity, and the stability of rain-fed farming systems that dominate the state’s economy [19].

The Himalayan Mountain region spans eight countries: Afghanistan, Bangladesh, Bhutan, China, India, Myanmar, Nepal, and Pakistan. It covers an area around 7,550,000 km² [20]. Around 70% of people in the Himalayan Mountains depend on crop-based and livestock-based livelihood activities [21,22]. In the Himalayan Mountain region, various forms of agriculture and crop cultivation remain, like permanent or settled and shifting cultivation.

Mountain and non-mountain agriculture are important drivers of CC through the emission of GHGs and are also highly vulnerable to CC. It is universally accepted that the mitigation and adaptation of CC are easily achievable through involvement and contribution from individual/general people and the community [23,24,25,26,27]. Similarly, farm-level adaptation may be an alternative to minimize the emission of GHGs and the risk of CC in agriculture [28], and in situ adaptation is more desirable for managing the CC risk [29,30]. For instance, local communities are playing a leading role in addressing drought problems through the combined use of traditional and modern methods [31]. Therefore, individuals’ thorough understanding of CC and its mitigation and adaptation is essential for effective CC mitigation and adaptation [6,23,32,33,34].

However, it is widely accepted that awareness leads to the acquisition of knowledge, which leads to comprehension, followed by understanding, perception, and application [35]. On the other hand, CC, global warming, and its mitigation and adaptation are relatively new concepts. Knowledge about these among ordinary people is inadequate [6]. Concerning rural and farming communities, people’s overall knowledge and perceptions are very low, as literacy rates and educational attainment are relatively low in rural areas [36,37,38]. Therefore, it is a prerequisite to assess the knowledge and perceptions of stakeholders/individuals about CC and its impacts on the environment, society, and livelihoods. Research is an alternative to address knowledge gaps [39]. This will support understanding the CC scenario from the perspective of stakeholders [40] and appropriate policymaking to minimize the knowledge and perception gaps of stakeholders and facilitate the start of CC mitigation and adaptation initiatives.

Understanding farmers’ knowledge and perceptions of climate change is essential for shaping effective policies, interventions, and sustainable practices in agriculture [41]. Farmers’ perceptions influence how they adapt to climate risks, such as changes in rainfall, temperature, and extreme weather, which directly affect their livelihoods. Tailoring solutions to align with their beliefs and knowledge ensures greater acceptance and practicality, while bridging knowledge gaps can promote resilience and proactive measures like water conservation and crop diversification. Incorporating traditional knowledge with scientific research fosters innovative, locally adapted solutions, and effective communication based on farmers’ experiences builds trust and engagement. Ultimately, recognizing farmers’ perspectives empowers them to address climate challenges while ensuring that policies and initiatives are equitable and impactful. However, farmers’ knowledge has widely been under-recorded and overlooked in scientific research [42]. The knowledge and perception of people regarding CC have been of great importance for CC mitigation and adaptation [43,44], and exposure to the extreme consequences of CC may raise awareness [45] and initiate mitigation endeavors [46]. Several researchers [47,48,49,50] found that public perception may facilitate the implementation of CC mitigation and adaptation policy. However (based on website searches and discussions with some research methodology experts), the availability of tools and techniques for examining people’s awareness, knowledge, and perception of agricultural CC is relatively limited.

Tools for assessing interaction amongst farmers–agriculture–CC–global warming–mitigation and adaptation to CC are either absent or extremely inadequate. This paper consists of an attempt with the following objectives: (i) to develop a framework for scale construction and (ii) to construct a scale to measure the knowledge and perception of farming communities on CC. This paper emphasizes the impact of CC on all agroecological situations (mountain, foothill, and plain situations) with different cultivation models (settled and shifting cultivation). Accordingly, we involved 15 extremely qualified experts, 83 highly qualified judges, and 100 farmers in the construction of this scale.

This research made a novel contribution to the fields of CC and agricultural research through its multidimensional approach to assessing farmers’ knowledge and perceptions of CC, which aligns with Wilson et al.’s [51] findings. Many existing studies often focus on specific aspects of CC. However, this research develops a robust, scientifically validated scale grounded in an extensive conceptual framework using various dimensions of CC impacts and agricultural practices. The involvement of highly qualified experts and judges in scale development lends credibility and depth to the findings. Importantly, this research highlights the scalability of the framework, making it adaptable to various socio-economic, ecological, and geographical settings worldwide. Further, there is relatively sparse evidence on such a comprehensive scale on CC and agricultural interaction for the Himalayan Mountain region; therefore, this paper adds value to the existing literature.

This paper is organized as follows. The next section presents a conceptual framework, the basis for this study, and the methodology adopted to complete this study. The results and discussion are presented in Section 3. Concluding remarks form the last section.

2. Methodology

This section presents this study’s conceptual framework and the research methods used to develop the scale for assessing farmers’ knowledge and perceptions of CC. The present study was conducted in Nagaland (25°6′ N to 27°4′ N and 93°20′ E to 95°15′ E), India (Figure 1). Also, the study area is in the Himalayan Mountain region.

A Likert-type scale was constructed to assess farmers’ knowledge and perceptions of CC. The 1st step of scale construction is to form the theory and define the construct to be assessed based on the literature [52]. Van Valkengoed et al. [43], based on the literature [53,54,55,56], postulated three types of CC knowledge and perception, namely, belief about the reality of CC, causes of CC, and consequences of CC. van der Linden [57] proposed three interrelated areas: public knowledge about the causes, impacts, and responses to CC. Further, the CC risk perception model (CCRPM) of van der Linden [57], willingness to act on CC [58], CC skepticism questionnaire [59], and health risks associated with CC [60] were taken into consideration. Further, to explain the interaction between agriculture and CC, it is necessary to include other issues from agriculture and CC, as well as the outcomes of their interaction.

Concerning agricultural CC, it is necessary to include additional aspects and sub-sectors of agricultural activities, along with their interaction with CC and global warming. Therefore, we included general awareness about the environment; climate; CC; global warming; interaction with CC and agriculture and allied activities; the contribution of agriculture and allied activities to CC; the influence of CC on agriculture; information about GHGs (how these gases are responsible for CC and which agricultural activities are responsible for production and sequestration of GHGs); consequences of CC on the pattern of temperature, rainfall, wind flow, and natural disasters; and the impact of CC and its mitigation and adaptation. In general, mitigation refers to reducing GHG emissions, whereas adaptation is a strategic intervention conducted to reduce vulnerability to CC. However, it is often difficult to clearly distinguish the two, and the term ‘climate resilience’ is an alternative. Accordingly, all the issues mentioned earlier are included in the conceptual framework (Figure 2).

The conceptual framework (Figure 2) depicts a detailed description and outline of this research for scale construction. It covers the literature survey with an emphasis on various dimensions of CC and includes all important dimensions in this research. Subsequently, it includes the appropriate methodological guidelines, i.e., item assumption and postulation to item finalization through the contribution from experts and judges, followed by final scale construction on the knowledge and perceptions of farmers regarding CC (Figure 2).

2.1. Item/Statement Selection

Knowing that an idea, practice, or object exists is awareness; retaining and recalling the idea, practice, or object is knowledge. Similarly, perception is one’s capability of understanding, perceiving, and interpreting something. A rigorous literature review of all the earlier-mentioned aspects was conducted to develop the items. Initially, 162 items were selected. After thorough consideration, 143 items were retained, and the remaining 19, which were redundant or irrelevant, were removed. All the aspects of climatic factors; climatic variables; the impact of CC and warming on agriculture and allied issues; and the role/contribution of agriculture to CC, C-sequestration, mitigation, and adaptation of CC were taken into account at the time of item selection. Accordingly, a list of 143 items was prepared and sent to experts (N = 15) with a binary continuum (relevant or irrelevant) to examine the consistency of all items. It is essential to mention that experts had PhD degrees in agriculture and allied disciplines and experience in scale construction and CC research and were recognized as ‘extremely qualified’. However, 5 items (out of 143) received 100% non-relevant responses, which were discarded accordingly. Finally, 138 items were retained and included in the scale construction for rating by the judges.

2.2. Scoring

It is accepted that CC is a relatively new subject. Educated and progressive people are required to perceive new concepts like CC. Indian farming communities are relatively less educated, less progressive, and unable to accurately perceive the CC scenario. The present scale construction was intended to be conducted to assess Indian farmers’ knowledge and perceptions of CC. A good blending of negative and positive scoring in scale construction was recommended. However, the expert (N = 15) group recommended avoiding negative scoring because respondents would consist of a less educated and less progressive farming community. Accordingly, all the items (138) were considered with a four-point positive continuum (following the Likert technique): extremely relevant, highly relevant, relevant, and not relevant. Further, weightings of 3 (extremely relevant), 2 (highly relevant), 1 (relevant), and 0 (not relevant) were assigned to responses for each item and used for analyses.

2.3. Construction of Questionnaire

All items were included in the judges’ rating process, with a four-point scoring continuum. The questionnaire starts with a cover letter for information and to request the judge’s presence, followed by an option for collecting some basic information about the judge, like their name, discipline/specialization, qualification, designation, department/organization, email ID, and Phone Number. The last part of the questionnaire contained 138 items. Each item separately had four alternative responses. The response to the option is editable or changeable by the respondent until submission. The questionnaire was published online as a www.google.com form (with the following link: https://forms.gle/vCSox37YL2sTF3AHA).

2.4. Criteria for Selection of Judge

It is accepted that CC is a relatively new concept, and qualified people can perceive and interpret it. Therefore, advanced knowledge of agriculture is required to serve as a judge to establish the relationship between CC and agriculture. Considering all the issues, a minimum of a PhD degree in agriculture and allied fields was decided as a prerequisite criterion for acting as a judge, and people with this designation were recognized as ‘highly qualified.’

2.5. Collection of Judges’ Responses

Based on the criteria, a list of intended judges was prepared by consulting the websites of agricultural universities and research institutes; the online profiles of faculty and scientists and professional groups; and participants’ lists of seminars, symposiums, and workshops. All the intended judges (approximately 500) were communicated with through email using the following www.google.com/form link: https://docs.google.com/forms/d/e/1FAIpQLScvAZEFDZvr2G5TXyS8Jw80nWthxt5cCINPON8zjE7ummBR9Q/formResponse, accessed on 28 April 2025. They were subsequently sent reminders/requests for response. Further, only the complete online submission of forms by judges (respondents) was enabled to avoid incomplete submissions. Around 83 judges’ complete responses were included in this study.

2.6. Data Analysis

Initially, we examined the distribution of responses under different continuums. Based on the scholarly literature [61], it was decided that items with ≥20.00% non-relevant responses were rejected, and the remaining items were retained as components/items of the scale. Following Patra and Babu [62] and Patra et al. [63], we adopted Cronbach alpha analysis, and following Patra and Babu [62] and Abunyewah [64], we adopted factor analysis. Cronbach alpha analysis was used to assess the reliability and consistency of the questionnaire with retained items. Following this, factor analysis (principal component analysis) with varimax rotation was adopted to complete scale construction. Factor analysis is unique in converting large numbers of variables with similar backgrounds into a small set of factors. In factor analysis, three aspects, namely, eigenvalue, factor loading, and communality, were considered for retaining the variable and factor. Based on the literature [62,65,66], with factor analysis, a factor with an eigenvalue ≥ 1 was retained in this study. Similarly, variables with factor loadings ≥ 0.35 and communality ≥ 0.70 were retained for further interpretation.

2.7. Consistency Assessment

Another important parameter in scale construction is the calculation of internal consistency and reliability or coefficient alpha. The most popular method of measurement is the calculation of Cronbach alpha. In general, a higher reliability coefficient (>0.80) indicates the greater internal consistency and reliability of the scale. In this scale construction, we separately calculated the entire scale and each factor.

2.8. Application of Scale for Assessment of Farmers’ Knowledge and Perceptions of Climate Change

Lastly, the constructed scale was adopted to measure the knowledge and perceptions of the intended respondents and to evaluate its consistency. All the retained items/statements from 14 dimensions/issues (General, GHGs, Temperature, Rainfall, Agricultural emission, shifting cultivation, rice cultivation, Mitigation, C-sequestration, Impact on Agriculture, Livestock, Wind, Natural disaster, Impact, and Adaptation) associated with agriculture and CC scenarios were included as components in the knowledge and perception index (KPI) to assess farmers’ knowledge and perceptions of CC. Further, all the items were presented (following the Likert technique) with the 3-point continuum: fully aware/knowledgeable, partially aware/knowledgeable, and not aware/knowledgeable, with weights of 2, 1, and 0, respectively. Regarding scoring, the researcher determined the score during the interview based on the respondents’ degree of awareness/knowledge/perception in the continuum consisting of without, partial, and full/complete knowledge. Accordingly, 100 farmers were included as respondents to assess their knowledge and perceptions. Based on the respondents’ responses, a ‘knowledge and perception index’ (KPI) was developed to categorize them based on their KPI score. The following formula was used to calculate the KPI.

$$Knowledge and perception Index (KPI)={\displaystyle \frac{Achieved score}{Achievable score}} \times 100$$

For data collection from farmers, researchers conducted face-to-face and oral interviews with individual farmers (Figure 3) using an interview schedule. Further, the English language was used for the drafting of the interview schedule and for interactions with respondents. Simultaneously, Nagamese (the most commonly used language in the state of Nagaland) was used as required to overcome language barriers and facilitate effective interviews and interactions.

2.9. Criteria for Selection of Farmers

The present study was conducted with an emphasis on representing the Himalayan Mountain region. Accordingly, farmers from the state of Nagaland (25°6′ N to 27°4′ N and 93°20′ E to 95°15′ E), India (Figure 1), were selected as respondents. Also, the study area is in the Himalayan Mountain region. Two districts, namely Chumoukedima and Dimapur, with an altitude range from 145 to 2000 m above MSL [67,68], were selected to include 100 farmers (50 farmers from each district) as respondents in the present study. Further, farmers with a minimum of 5 years of continuous experience in farming and their willingness and ability to interact with the researcher/interviewer were the criteria for the selection of farmers as respondents.

2.10. Ethical Statement

Regarding ethics approval, this study was conducted in accordance with the approval given by the Human Research Ethics Committee of Nagaland University (protocol code ref. NU/HREC/2025/01/05 and date of approval 4 July 2025). Further, the consent of experts, judges, and respondents was waived. This study on human participants was conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

3. Results

Altogether, 83 respondents submitted their responses, constituting the judges’ community for this scale construction. It was decided that items that received ≥20.00% of ‘irrelevant’ responses were rejected or discarded. Subsequently, items that received ≥80.00% relevant/highly relevant/extremely relevant reactions, either individually or in combined responses, were accepted as items for the scale on CC knowledge and perception. Accordingly, 109 items were retained, and 29 were rejected. All the 109 retained items were denoted as x₁–x₁₀₉ (

Table 1. Items/statements rejected based on judges’ rating for scale construction.

Items/Statements
* X14 Solar and renewable energy are emission-neutral/climate-friendly
* X33 The environment is getting warmer day by day
* X34 Frequency of unexpected climatic events has increased
* X37 Erratic precipitation during monsoon
* X41 Duration of the rainy season has shortened
* X50 Jhum (shifting cultivation) burning is emitting/adding GHGs
* X78 Climatic uncertainty has increased

* Rejected items/statements.

and

Table 2. Rotated component/factor matrix and retained/accepted items/statements of scale on knowledge and perceptions of farmers on CC.

Indicator (Name of Indicator)	Factor	Items/Statements	Factor Loading	Eigenvalue	Communality	Cronbach Alpha
						(Overall = 0.985) Each Factor
I (Respondents’ Knowledge and perception of agricultural CC, emission of GHGs and mitigation strategy)	1	* x46 Proper composting of crop residue is a/an mitigation/adaptation strategy to climate change (CC)	0.408	43.33	0.745	0.938
		x52 Rice cultivation is also adding N2O to the environment	0.556		0.813
		x54 Alternative dry and wet spells of rice fields is a mitigation measure to reduce the emission of GHGs from rice field	0.545		0.879
		x55 SRI has the potential to reduce the emission of GHGs from rice field	0.526		0.790
		** x57 Root zone placement of Nitrogenous fertiliser is a mitigation measure to reduce the emission of N2O from crop field	0.465		0.871
		** x59 Proper handling of animal urine is a measure to reduce the emission of GHG	0.461		0.818
		x60 Earth’s surface is an enormous reservoir of Carbon	0.637		0.857
		x61 Exposing/disturbing the soil surface is used to expose soil carbon, which is also a reason for CC	0.778		0.877
		x62 Ploughing is a means to expose soil carbon	0.759		0.850
		x63 Grazing is a reason to expose the earth’s surface	0.713		0.881
		x64 Zero or minimum tillage is desirable to adapt the emission due to disturbance of soil surface	0.763		0.822
		x65 Are you aware of C-sequestration	0.564		0.826
		** x66 Crop cultivation is a practice of C-sequestration	0.474		0.833
		x67Agriculture (except some crops) is used to consider as GHG emission neutral	0.566		0.854
		x68 Eastern Himalayan region is more vulnerable to CC	0.552		0.772
	13	x43 Agriculture is highly vulnerable to CC	0.743	1.63	0.888	-
	14	** x40 Annual rate of rainfall is inconsistent (with respect to previous years)	0.451	1.59	0.774	0.759
		* x53 Waterlogging rice cultivation is greatly responsible for CC	0.444		0.828
		x56 Split application of nitrogenous fertiliser is a mitigation measure to reduce the emission of N2O from rice field	0.502		0.859
	15	* x42 Agriculture is also emitting GHGs	0.430	1.45	0.827	0.765
		* x45 Proper composting can reduce the emission from animal dropping	0.421		0.804
		x58 Animal urine is a source of nitrogen gas and emitting/adding N to the environment	0.538		0.840
	20	x17 Deforestation is responsible for CC	0.606	1.16	0.764	-
	22	* x44 Livestock (ruminant) are greatly emitting GHG	0.3848	1.01	0.783
II (Sustainable Adaptation to Agricultural CC)	2	x90 CC may be the reason for the increased cost of cultivation	0.505	5.97	0.806	0.939
		** x91 CC is the reason for the early maturity/harvesting of crops	0.481		0.775
		** x92 Change in planting time is a mitigation measure to CC	0.442		0.760
		x94 CC has influenced the increased occurrence of disease attacks in the crop	0.527		0.865
		x102 Change from long-duration to short-duration crop varieties is an adaptation strategy to CC	0.725		0.883
		x103 Change to more cash crops is an adaptation strategy to CC	0.711		0.876
		x104 Change of planting/sowing time as per weather conditions is an adaptation strategy to CC	0.790		0.855
		x105 Integrated water management for scarcity (during the winter) is an adaptation strategy to CC	0.650		0.880
		x106 Intercropping is an adaptation strategy to CC	0.708		0.847
		x107 Construction of farm ponds is an adaptation strategy to CC	0.531		0.737
		x108 Mulching is an adaptation strategy to CC	0.606		0.840
		* x109 Resort to terrace/settled cultivation in place of shifting cultivation is an adaptation strategy to CC	0.401		0.767
III (Effect of CC on Agriculture)	4	x7 CC is mostly anthropogenic	0.529	4.61	0.764	0.937
		** x77 Occurrence of strong wind has increased	0.484		0.814
		* x93 CC has influenced the increased occurrence of insect attacks in the crop	0.416		0.846
		x96 (Due to CC) Increased occurrence of incidence of disease in animals	0.698		0.822
		x97 (Due to CC) Increased mortality rate of the animals	0.731		0.850
		x98 CC is the reason for the reduction in the species of forest trees	0.665		0.910
		x99 High temperature is the reason for the reduction in milk production of animal	0.773		0.878
		x100 High temperature is the reason for the restricted growth of livestock	0.675		0.851
		x101 Collection of water for livestock during summer/drought is a difficult task	0.665		0.860
	6	x86 Agricultural losses have increased due to CC	0.704	2.69	0.899	0.930
		x87 CC has influenced the reduction of yield	0.654		0.896
		x88 Unfavorable weather is responsible for crop failure	0.613		0.856
		x89 CC is a tremendous threat to food security	0.665		0.854
		x95 Owing to CC (drought/flood), the availability of fodder for livestock has decreased	0.502		0.808
IV (Impact of CC Or Occurrences of CC consequences)	3	x73 CC has a substantial negative impact on cold-loving (temperate) crops (like apples)	0.533	4.84	0.880	0.939
		x79 Occurrence of natural disasters has increased	0.569		0.890
		x80 Occurrence of the landslide has increased	0.785		0.896
		x81 Occurrence of thunderstorms has increased	0.709		0.857
		x82 Phenomenon of drought occurrence has increased	0.621		0.888
		x83 Phenomenon of flood occurrence has increased	0.765		0.883
		x84 Phenomenon of cyclone occurrence has increased	0.649		0.880
		** x85 Frequency of dry spells has increased	0.459		0.828
		* x51 Rice cultivation is emitting CH4 gas	0.378		0.790
	8	* x1 CC is a natural process	0.404	2.21	0.721	0.893
		x3 CC is also a reason for global warming	0.598		0.829
		x4 CC is the reason for the rise of sea level	0.848		0.850
		x5 CC is the reason for mountain glacier melting	0.819		0.930
		x6 CC is the reason for pole glacier melting	0.775		0.879
	11	* x36 Unpredictable occurrence of rainfall has increased	0.417	1.83	0.773	0.916
		x38 Unpredictable onset of monsoon rain	0.790		0.930
		x39 Unpredictable cessation of monsoon rain	0.741		0.870
	19	x35 The Frequency of heavy rain has increased	0.648	1.23	0.785	-
V (Basic/general awareness/knowledge of CC)	5	** x12 Burning of fuel by vehicle is adding GHGs (and harmful gases) to the environment	0.408	3.04	0.833	0.892
		** x13 Modernization of society, i.e., better civilization, is also a reason for CC	0.460		0.834
		x18 Have you heard about GHGs?	0.533		0.752
		x19 Emission of GHGs is the reason for CC	0.651		0.839
		x20 CO2 is a GHG	0.813		0.836
		x21 CH4 is a GHG	0.868		0.895
		x22 N2O is a GHG	0.803		0.840
		x23 CFC is also a GHG	0.504		0.803
	7	x26 Rainfall is a climatic factor/variable	0.800	2.53	0.861	0.889
		x27 Temperature is a climatic factor/variable	0.759		0.862
		x28 Humidity is a climatic factor/variable	0.730		0.850
		** x32 Temperature is rising during the daytime	0.482		0.854
	9	x8 Overpopulation is also a reason for CC	0.785	2.05	0.850	0.844
		x9 Urbanisation is a driver for CC	0.788		0.900
		** x10 Generation/production of electricity is a reason for CC	0.474		0.793
		x11 Fossil fuel burning is a major driver for CC	0.560		0.821
		* x49 Jhum (shifting cultivation) is a reason for CC	0.403		0.750
	16	x29 Have you ever heard the term CC (CC)	0.577	1.38	0.874	0.897
		x30 Have you ever heard the term global warming	0.592		0.876
		x31 Have you heard about ozone layer depletion	0.511		0.870
		* x47 Crop residue burning is adding GHGs	0.3571		0.830
	17	** x15 Industry is a responsible driver for CC	0.479	1.33	0.802	0.693
		x16 Construction (road and building) work is a driver for CC	0.626		0.729
VI (Impact of CC on life and livelihood)	10	x69 CC has a huge negative impact on human well-being	0.731	1.90	0.827	0.908
		x70 CC has a huge negative impact on human health	0.557		0.838
		x71 CC has a substantial negative impact on livelihood	0.639		0.877
		** x72 CC has a substantial negative impact on livestock	0.460		0.847
	12	* x25 H2O-vapour is also a reason for local-level warming	0.434	1.72	0.740	0.805
		* x48 Household (including kitchen waste) waste is also releasing/emitting GHGs	0.447		0.788
		x74 Owing to CC (warming), the cold place is more comfortable to live	0.534		0.764
		x75 Owing to CC (warming), warm places (tropical zone) are more uncomfortable to live	0.705		0.823
		x76 Owing to CC (warming), coastal zones are more vulnerable to sea level rise	0.550		0.814
	18	x24 CO2 is harmful to the human and mammal kingdom	0.679	1.25	0.853	-
	21	x2 Owing to increasing population, some extent of CC is unavoidable	0.761	1.14	0.832	-

* Item/statement with factor loading ≤ 0.450. ** Item/statement with factor loading ≤ 0.500.

). Retained items (x₁–x₁₀₉) represented all the aspects of climatic factors; climatic variables; the impact of CC and warming on agriculture and allied issues; and the role/contribution of agriculture to CC, C-sequestration, mitigation, and adaptation of CC. Apart from these, items x₄₅, x₄₆, x₅₄–x₅₇, and x₅₉ addressed the issue of mitigation, and items x₆₄, x₆₆, x₉₂, and x₁₀₂–x₁₀₉ addressed the aspect of adaptation. In the next stage, factor analysis was applied to complete the scale construction to measure farmers’ knowledge and perceptions of CC.

Table 2 shows how 102 statements about climate change were grouped into six categories based on how experts, judges, and farmers responded. These groups, i.e., indicators, reflect different aspects of respondents/farmers’ understanding of climate change. Group I captures what respondents/farmers know about how agriculture contributes to climate change, such as greenhouse gas emissions, soil disturbance, and mitigation practices like composting or minimum tillage. Group II includes statements related to how respondents/farmers adapt to climate change, such as changing planting dates, using drought-resistant crops, improving water management, and adopting practices like mulching or intercropping. Group III focuses on the effects of climate change on agriculture and livestock, including increased crop losses, reduced yields, more pests and diseases, and stress on animals. Group IV covers the broader impacts and consequences of climate change, such as more floods, droughts, landslides, heat, and glacier melt. Group V reflects the general awareness of climate change causes, greenhouse gases, and basic climate concepts. Finally, group VI captures how climate change affects human life and livelihoods, including impacts on health, well-being, comfort, and daily living. Together, these results show that the scale is comprehensive and captures all major areas of knowledge and perception that matter for understanding climate change in farming communities.

The factor analysis (Table 2) of 109 items/variables produces various factors, but factors with eigenvalues of ≥1.00 were retained and the remaining discarded. Altogether, 22 factors were retained with eigenvalues greater than 1.00 (Figure 4 and Table 2). On the other hand, 102 items/statements (x₁–x₁₃, x₁₅–x₂₅–x₃₂, x₃₅, x₃₆, x₃₈–x₄₀, x₄₂–x₄₉, x₅₁–x₇₇, and x₇₉–x₁₀₉) were retained based on a factor loading ≥0.35 with a communality ≥0.70 (Table 2), and the remaining 7 items/statements (x₁₄, x₃₃, x₃₄, x₃₇, x₄₁, x₅₀, and x₇₈) were discarded due to low factor loading (Table 1). The overall Cronbach alpha value of 109 items is 0.985, and the cumulative percentage of variance is 83.32. Patra and Babu [62] reported a Cronbach alpha value of 0.857 with a cumulative percentage of variance of 86.15%, and the CCRPM of Linden [57] explained around 70% of the variance.

The determination of stakeholders’ ‘Knowledge need’ is crucial for filling the knowledge gap in adaptation planning [50,69], an appropriate adaptation strategy [70], and policy decisions [71]. Subsequently, Factor 1 accounts for 40.12% of data variability. Altogether, 16 items/statements (x₄₄, x₄₆, x₅₂, x₅₄, x₅₅, x₅₇, x₅₉, x₆₀, x₆₁, x₆₂, x₆₃, x₆₄, x₆₅, x₆₆, x₆₇, and x₆₈) are retained under Factor 1. The Cronbach alpha value of the factor is 0.938. Factor 13 accounts for 1.51% of the data variability with one (x₄₃) variable. Factor 14 accounts for 1.48% of the data variability and retains three items/statements (x₄₀, x₅₃, and x₅₆). Factor 15 accounts for 1.35% of the data variability and retains three items/statements (x₄₂, x₄₅, and x₅₈). Further, Factor 20 and Factor 22 account for 1.08% and 1.01% of the data variability; each retains a single item/statement (x₁₇ and x₄₄, respectively). Altogether, 24 similar items/statements are retained under Indicator-I (Factor 1, Factor 13, Factor 14, Factor 15, Factor 20, and Factor 22). ‘Respondents’ Knowledge and perception of agricultural CC, emission of GHGs and mitigation strategy’ (Table 2) is the nomenclature of Indicator-I, and items are associated with exploring the same. Further, of the 24 items/statements, 7 are associated with mitigation strategies (x₄₅, x₄₆, x₅₄–x₅₇, and x₅₉) for agricultural GHG emissions. Similar types of items/statements were emphasized in the development of the CC perception scale by van Valkengoed et al. [43]).

Farmers’ adoption of environmentally friendly practices is less than achievable [72]. The IPCC has recognized the strength of traditional and local knowledge for mitigation and adaptation to CC [1,73]. CC knowledge among farmers significantly influences their CC adaptation [64]. Guiding farmers on CC adaptation has sustainably impacted CC adaptation [74]. Concerning adaptation, Factor 2 has an eigenvalue of 5.97 with a data variability of 5.54%. The Cronbach alpha value of Factor 2 is 0.939, and 12 items/statements (x₉₀–x₉₂, x₉₄, and x₁₀₂–x₁₀₉) are retained under this factor. Further, nine items/statements (x₉₂ and x₁₀₂-x₁₀₉) are associated with adaptation strategies to agricultural CC. Factor 2, with 12 items/statements, constitutes Indicator-II. Based on the retained items/statements, the nomenclature is given as ‘Sustainable adaptation to agricultural CC’. Concerning this, Asrat and Simane [75], Madaki et al. [76], and Raghuvanshi and Ansari [77] immensely prioritized adaptation-related issues. On the other hand, Sara and Terre [78] studied genetically engineered crops for CC adaptation.

CC is placing massive pressure on the availability of critical resources, namely, water, energy, and food [79]. Further, the FAO [80] found that CC is putting tremendous pressure on water resources. Therefore, CC is impacting various dimensions. Here, we concentrate on the diversity of the effects of CC on agriculture and allied sectors. At this juncture, Factor 4 retains nine items/statements, namely x₇, x₇₇, x₉₃, x₉₆, x₉₇, x₉₈, x₉₉, x₁₀₀, and x₁₀₁. The data variability is 4.28%, with a Cronbach alpha value of 0.937. Factor 6 also retains five items/statements (x₈₆, x₈₇, x₈₈, x₈₉, and x₉₅) with 2.50% data variability and a Cronbach alpha of 0.930. All the items/statements under Factor 4 and Factor 6 are highly concerned about the occurrences of climatic hazards, impacts, and exposure to climatic hazards. Accordingly, Indicator-III is constituted by Factor 4 and Factor 6, and the nomenclature is given as ‘Effect of CC on Agriculture’. Regarding this, Thaker et al. [60] immensely emphasized climatic hazards and exposure to assess the perception of climatic hazards.

The impact of CC on different contexts and dimensions should be considered [71]. Similarly, Indicator-IV is constituted by four factors (Factors 3, 8, 11, and 19), and the nomenclature is given as ‘Impact of CC or Occurrence of CC consequences.’ Factor 3 retains nine items/statements (x₅₁, x₇₃, x₇₉, x₈₀, x₈₁, x₈₂, x₈₃, x₈₄, and x₈₅) with a data variability of 4.49% and Cronbach alpha value of 0.939. Factor 8 retains five items/statements (x₁, x₃, x₄, x₅, and x₆) with 2.05% data variability and a Cronbach alpha value of 0.893. On the other hand, Factor 11 retains three items/statements (x₃₆, x₃₈, and x₃₉) with a data variability of 1.69% and Cronbach alpha value of 0.916. Factor 19 retains only one item/statement (x₃₅) with a data variability of 1.14%. Eventually, the impact of CC was also taken into account by Patra and Babu [62], Asrat and Simane [75], Madaki et al. [76], and Raghuvanshi and Ansari [77] in a similar type of scale construction.

Stakeholders’ collaborative role in adaptation is widely accepted [81]. Assessing stakeholders’ willingness to act on CC is essential for appropriate policy formulation [82]. The knowledge and perception levels of community/farmers regarding CC accelerate their willingness to act on CC adaptation [83]. Concerning the knowledge and perception of CC, Factor 5, Factor 7, Factor 9, Factor 16, and Factor 17 accounted for 2.82%, 2.34%, 1.90%, 1.28%, and 1.23% of the total data variability, with Cronbach alpha values of 0.892, 0.889, 0.844, 0.897, and 0.693, respectively, and these were taken into consideration for the assessment of the knowledge and perception levels of farmers. Factor 5 retained eight items/statements (x₁₂, x₁₃, x₁₈, x₁₉, x₂₀, x₂₁, x₂₂, and x₂₃), and Factor 7 retained four items/statements (x₂₆, x₂₇, x₂₈, and x₃₂). Similarly, Factor 9 retained five items/statements (x₈, x₉, x₁₀, x₁₁, and x₄₉), Factor 16 retained four items/statements (x₂₉, x₃₀, x₃₁, and x₄₇), and Factor 17 retained two items/statements (x₁₅ and x₁₆). Based on the retained items/statements, these factors are closely related to the general awareness and knowledge of CC. Accordingly, Indicator-V is constituted by these factors; the nomenclature is given as ‘Basic/general awareness/knowledge and perception on CC’. Linden [57] and Xie et al. [58] emphasized the cause and impact/influence of knowledge in their scale construction.

CC negatively impacts life, livelihood, agriculture, and livestock [1]. Rijal et al. [84] reported that the diversification of livelihoods and agricultural practices are the two most common adaptation strategies. Factor 10 retained four items/statements (x₆₉, x₇₀, x₇₁, and x₇₂) with a data variability of 1.76% and Cronbach alpha value of 0.908. Factor 12 retained five items/statements (x₂₅, x₄₈, x₇₄, and x₇₅) with a data variability of 1.60% and Cronbach alpha value of 0.805. Similarly, Factor 18 and Factor 21 retained one item/statement each (x₂₄ and x₂) with a variability of 1.16% and 1.06%. Further, concerning the retained items/statements in the four factors, all are well connected to the consequences and impacts of CC. Accordingly, Indicator-VI was formed with four factors and has a nomenclature given as ‘Impact of CC on life and livelihood.’ In this respect, Madaki et al. [76] and Raghuvanshi and Ansari [77] also emphasized these issues during scale construction.

Subsequently, we assess the internal consistency and reliability of the scale, including the 109 items/statements and the individual factors, using the Cronbach alpha value. The Cronbach alpha for 109 items is 0.985, indicating that the scale items have high internal consistency. Therefore, it can be concluded that the constructed scale and its items are highly consistent, appropriate, and reliable for measuring farmers’ knowledge and perceptions. This finding aligns with the findings of Patra and Babu [62]. Further, the Cronbach alpha values for Factors 1, 2, 3, 4, 6, 10, and 11 are ≥0.900. The Cronbach alpha values for Factors 5, 7, 8, 9, 12, and 16 are ≥0.800. Similarly, the Cronbach alpha values for Factors 14, 15, and 17 are ≥ 0.700. On the other hand, Cronbach alpha analysis is not possible for Factors 13, 18, 19, 20, 21, and 22 because each of these factors contains only one item/statement. Further, the scale explains around 83.32% of data variability. Therefore, it can be concluded that the constructed scale is highly consistent and reliable, as the value of the reliability coefficient for the entire scale is 0.985; for the seven Factors, it is also >0.900; for six factors, it is >0.800 (the recommended value is ≥0.800).

Application of Scale for Assessment of Farmers’ Awareness, Knowledge, and Perceptions of CC

Farmers are playing a front-line and leading role in CC adaptation in the farming sector [85]. Further, farmers’ CC knowledge is positively associated with their CC perception [76]. In this section, we evaluate the developed scale’s consistency for measuring farmers’ knowledge and perceptions of CC. All items/statements (Table 2) of the constructed scale (102 items/statements) are included as components in the KPI to assess farmers’ level of knowledge and perceptions of CC. Accordingly, all items/statements are included in the interview schedule, with responses rated on a 3-point continuum (2, 1, and 0). The possible range of scores is 0.00 to 204.00 (102 × 2). The scores achieved by all respondents ranged from 5.39 to 78.92, with a mean score of 41.37 and a standard deviation value of 15.03 (Figure 5). According to their KPI score, further respondents are categorized into three groups: low, medium, and high. It is clear that around 57.00% of the farmers are classified under the medium category, and around 69.00% (Figure 5) of the farmers achieved KPI scores that are under 50.00% of the maximum achievable score, and the respondents’ overall knowledge and perception level is relatively low (mean score: 41.37). Accordingly, it can be concluded that in the study area, farmers have a huge inadequacy of CC knowledge and perception.

4. Discussion

This study developed a multidimensional and statistically robust scale to assess farmers’ knowledge and perceptions of climate change (CC) in the Himalayan Mountain region. The six indicators that emerged from factor analysis represent distinct yet interconnected dimensions of CC knowledge, perception, and experiential understanding. The following discussion is organized around these six indicators. The indicators derived from factor analysis reveal a strong conceptual structure aligned with existing CC perception frameworks, such as those by van Valkengoed et al. [43] and Linden [57].

4.1. Knowledge and Perception

Indicator-I reflects farmers’ understanding of how agricultural activities contribute to greenhouse gas (GHG) emissions, carbon dynamics, and available mitigation options. Items retained under this indicator emphasize knowledge of soil carbon exposure, fertilizer management practices, rice field emissions, livestock-related emissions, and mitigation strategies such as composting, SRI, zero tillage, and proper manure handling. The strength of this indicator confirms that mitigation-related concepts are conceptually distinct within farmers’ cognitive frameworks. However, field application revealed that while farmers have a general awareness of agriculture–climate interactions, their technical understanding of mitigation practices remains limited. This aligns with van Valkengoed et al. [43], who found that mitigation perceptions require higher-order conceptual knowledge, often lacking in rural populations. The indicator highlights a critical gap and an urgent need to address climate literacy. Farmers experience climate stress but are less familiar with emission-reducing agronomic interventions. Strengthening this knowledge is essential for promoting low-carbon agriculture and meeting broader regional climate action goals.

4.2. Sustainable Adaptation

Indicator-II captures farmers’ awareness of adaptation strategies, including changing crop duration, shifting planting windows, integrated water management, mulching, farm ponds, intercropping, and transitioning from shifting to settled cultivation. These strategies reflect well-established adaptation pathways recommended in mountainous and rainfed ecosystems [75,76]. The strong performance of adaptation-related items in factor analysis suggests that farmers more readily grasp strategies that are practical, experience-based, and directly relevant to their production systems. Yet, the KPI results indicate that despite familiarity with some adaptation practices, overall adoption and deeper understanding remain limited. This is consistent with global findings [80] that emphasize barriers such as resource constraints, limited extension access, incentives to adopt sustainable practices, and insufficient technical guidance. Indicator-II, therefore, underscores the need for reinforcing adaptation extension programs that translate scientific recommendations into actionable, locally tailored practices.

4.3. Effects of Climate Change on Agriculture

Indicator-III consolidates farmers’ perceptions of CC impacts on crops and livestock, including increased pest and disease pressure, reduced milk production, livestock mortality, fodder scarcity, and additional burdens such as water collection challenges during hot or drought periods. The items in this indicator reflect direct experiential evidence consistent with documented impacts in the Himalayan region [21] and other developing countries experiencing agroecological stress. The farmers’ strong agreement with these impact-related items demonstrates that their operations are likely being affected by the effects of climate change. However, their limited ability to link these observable changes to broader CC processes reflects a disconnect between experiential knowledge and scientific understanding. This distinction is important because it suggests that while farmers can identify symptoms of CC, they may not possess the conceptual tools needed to anticipate future risks or select appropriate interventions without targeted climate education.

4.4. Occurrence of Climate Hazards

Indicator-IV covers farmers’ perceptions of extreme climatic events—landslides, floods, droughts, heavy rainfall, strong winds, cyclones, unpredictable monsoon behavior, and glacial influences. The clustering of natural disaster-related items into this indicator highlights the centrality of climate hazards in shaping farmers’ lived experiences and risk perceptions. These findings are consistent with regional reports showing increased climate variability and hazard frequency in the Himalayan belt [1,15]. Farmers’ strong recognition of hazard patterns indicates that climate shocks form a major component of their CC awareness. However, while their experiential understanding is strong, conceptual gaps remain in interpreting climate drivers or linking these hazards to long-term climate trends. This emphasizes the value of community-level climate services, early warning dissemination, and participatory hazard mapping to enhance preparedness and adaptive decision-making.

4.5. Awareness/Knowledge of Climate Change

Indicator-V incorporates farmers’ general climate literacy, such as their awareness of greenhouse gases, climate drivers, environmental degradation, fossil fuel emissions, ozone depletion, and modernization-related impacts. This indicator corresponds to foundational CC knowledge highlighted in global perception models [57,58]. Despite the comprehensive nature of these items, the KPI results show that overall general awareness remains low. This mirrors findings from northeastern India and other developing regions where CC is increasingly observed but not thoroughly understood [37,86]. Limited educational levels, insufficient climate communication infrastructure, and the relatively recent introduction of CC concepts contribute to these gaps. Strengthening Indicator-V through targeted capacity-building efforts is essential, as general awareness provides the cognitive base for interpreting climate signals, evaluating risks, and adopting mitigation and adaptation practices.

4.6. Impact of Climate Change on Livelihood

Indicator-VI addresses farmers’ perceptions of CC impacts beyond agriculture, including effects on the health, well-being, comfort levels, habitability, and vulnerability of coastal or warm regions. It reflects how farmers conceptualize climate change as a broader socio-environmental threat rather than solely an agricultural issue. The inclusion of these livelihood dimensions aligns with the literature recognizing CC as a multidimensional stressor affecting food security, health systems, income stability, and living conditions [1]. Farmers’ strong agreement with these items suggests an intuitive understanding that CC affects all aspects of life, even if they lack detailed scientific explanations. This indicator reinforces the need for resilience-building programs that integrate agriculture, public health, disaster preparedness, and social protection.

4.7. Enhancing the Capacity of Farmers

There may be various reasons for farmers’ low KPIs for CC. Experts also recognized that the concept of CC is relatively new to the farming community, and farmers may face problems in response to each item/statement. Accordingly, experts have suggested avoiding negative items/statements in the scale. Various studies have been conducted on improved cultivation practices, focusing on farmers’ knowledge and perception in the present study area. The knowledge and perception of the farmers in the present study area are relatively inadequate. For instance, Patra and Kense [87] reported a low level of knowledge among Mandarin growers in Nagaland. Patra et al. [88] also reported a low level of knowledge of rubber growers in Nagaland, India. Lathmawii et al. [37] and Patra et al. [38] from NER, India, also reported a similar observation. Patra et al. [84] also reported a significant lack of knowledge on improved maize cultivation in Nagaland, India. Finally, by collating and comparing the findings of the present study on farmers’ KPIs of CC using the constructed scale and previous findings in a similar genre, it can also be concluded that the constructed scale has sufficient consistency to assess farmers’ knowledge and perceptions of CC.

Enhancing farmers’ climate literacy is essential to translating the analytical insights of this study into on-farm actions. The findings highlight that improved access to localized climate information, clearer interpretation of indicators, and timely communication of emerging risks can strengthen growers’ ability to make informed decisions under increasing uncertainty. Building climate literacy requires a combination of practical, locally relevant, and tailored tools such as seasonal outlooks, heat risk, and yield risk summaries, delivered by trained extension professionals through workshops, demonstration sites, and peer-learning networks. By integrating the six indicators evaluated in this manuscript into grower-centered decision support products, farmers can better understand how climate signals relate to management choices, anticipate adverse conditions earlier, and adopt adaptive strategies with greater confidence. Ultimately, strengthening climate literacy bridges the gap between scientific assessments and farm-level action, enabling more proactive and climate-smart agricultural systems.

5. Concluding Remarks

Initially, out of 138 items/statements, 109 items/statements were retained, and 29 were discarded. Further, we applied factor analysis with the principal component method and varimax rotation with 109 items/statements, retaining 102 items (from 14 dimensions) under 22 factors. Subsequently, six indicators were developed, namely, Indicator-I—‘Respondents’ Knowledge and perception of agricultural CC, emission of GHGs and mitigation strategy;’ Indicator-II—‘Sustainable adaptation to agricultural CC;’ Indicator-III—‘Effect of CC on agriculture;’ Indicator-IV—‘Impact of CC or Occurrence of CC consequences;’ Indicator-V—‘Basic/general awareness/knowledge of CC;’ and Indicator-VI—‘Impact of CC on life and livelihood.’

Regarding the scale’s internal consistency, the Cronbach alpha for 109 items was 0.985, indicating high internal consistency. Cronbach’s alpha values for Factors 1, 2, 3, 4, 6, 10, and 11 were ≥0.900. Cronbach’s alpha values for Factors 5, 7, 8, 9, 12, and 16 were ≥0.800. Simultaneously, the scale explained around 83.32% of data variability. Accordingly, it can be concluded that the constructed scale is highly consistent and reliable. Further, the reliability coefficient for the entire scale was 0.985; for seven factors, it was >0.900; and for six factors, it was >0.800 (the recommended value is ≥0.80). Therefore, it can be concluded that the constructed scale and its items were highly consistent, appropriate, and reliable in measuring farmers’ knowledge and perceptions of CC. Lastly, we evaluated the developed scale’s consistency for measuring farmers’ knowledge and perceptions of CC. All 102 items/statements were included in the interview schedule, with a 3-point response continuum. A total of 100 farmers were included as respondents. Based on the scale’s application, we concluded that it is highly consistent and accurate in mapping farmers’ level of knowledge and perceptions regarding CC.

Finally, it can be concluded that the developed conceptual framework (Figure 2) and scale are appropriate to adopt for any scale construction (perception scale, awareness scale, knowledge scale, and attitude scale) in society for any discipline under social and behavioral sciences and any part of the world to map respondents’ knowledge and perceptions about CC and CC adaptation. The policy and research systems may adopt the framework and scale to assess stakeholders’ inclusive knowledge and perceptions of CC worldwide. The findings of this study may be helpful for policymakers, development workers, and extension functionaries in the Himalayan and other mountain regions, as well as in other areas of the planet, for mitigation and adaptation to CC and sustainable, resilient building to combat CC. Despite its many positive aspects, this study’s major limitation is that it relies on judges’ and respondents’ perceptions and recall abilities. Eventually, the possibility of bias and error is present. When adopting the scale in future research, researchers may develop a need-based strategy to minimize the influence of error.