Next Article in Journal
Applications of Machine Learning Methods in Sustainable Forest Management
Previous Article in Journal
Indigenous Contestations of Carbon Markets, Carbon Colonialism, and Power Dynamics in International Climate Negotiations
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Climate
Volume 13
Issue 8
10.3390/cli13080157
climate-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Differences in Perceived Future Impacts of Climate Change on the Workforce Among Residents of British Columbia

by
Andreea Bratu
1,
Aayush Sharma
2,
Carmen H. Logie
3,
Gina Martin
4,
Kalysha Closson
1,2,
Maya K. Gislason
2,
Robert S. Hogg
2,5,
Tim Takaro
2 and
Kiffer G. Card
2,*
1
School of Population and Public Health, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
2
Faculty of Health Sciences, Simon Fraser University, Burnaby, BC V5A 1S6, Canada
3
Factor-Inwentash Faculty of Social Work, University of Toronto, Toronto, ON M5S 1V4, Canada
4
Faculty of Health Disciplines, Athabasca University, Athabasca, AB T9S 3A3, Canada
5
British Columbia Centre for Excellence in HIV/AIDS, Vancouver, BC V6Z 1Y6, Canada
*
Author to whom correspondence should be addressed.
Climate 2025, 13(8), 157; https://doi.org/10.3390/cli13080157
Submission received: 6 June 2025 / Revised: 18 July 2025 / Accepted: 19 July 2025 / Published: 24 July 2025
Browse Figure
Review Reports Versions Notes

Abstract

Certain industries will bear a disproportionate share of the burden of climate change. Climate change risk perceptions can impact workers’ mental health and well-being; increased climate change risk perceptions are also associated with more favourable adaptive attitudes. It is, therefore, important to understand whether climate risk perceptions differ across workers between industries. We conducted an online survey of British Columbians (16+) in 2021 using social media advertisements. Participants rated how likely they believed their industry (Natural Resources, Science, Art and Recreation, Education/Law/Government, Health, Management/Business, Manufacturing, Sales, Trades) would be affected by climate change (on a scale from “Very Unlikely” to “Very Likely”). Ordinal logistic regression examined the association between occupational category and perceived industry vulnerability, adjusting for socio-demographic factors. Among 877 participants, 66.1% of Natural Resources workers perceived it was very/somewhat likely that climate change would impact their industry; only those in Science (78.3%) and Art and Recreation (71.4%) occupations had higher percentages. In the adjusted model, compared to Natural Resources workers, respondents in other occupations, including those in Art and Recreation, Education/Law/Government, Management/Business, Manufacturing, Sales, and Trades, perceived significantly lower risk of climate change-related industry impacts. Industry-specific interventions are needed to increase awareness of and readiness for climate adaptation. Policymakers and industry leaders should prioritize sectoral differences when designing interventions to support climate resilience in the workforce.

1. Introduction

Anthropogenically driven climate change poses a considerable threat to individuals, families, communities, nations, and the global community [1]. However, the effects of climate change are not uniform [1,2,3,4,5]. People from socially, economically, and culturally marginalized communities; those living in locations with increased environmental risks; and people in certain sectors of the economy will bear a disproportionate burden of climate change [1,2,3,4,5]. As society moves toward a more sustainable future, justice should be a key target for transition [6]. A just transition is one where equity is a key consideration, particularly for marginalized communities and those whose livelihoods are disproportionately impacted by climate change [7].
Climate change impacts the lives of workers, both directly and indirectly, as well as psychologically [8]. Some examples of direct impacts include increased levels of exposure to extreme weather, pathogens, and air pollution [9]. Additionally, occupations that spend a significant amount of time outdoors, such as natural resources workers, will be more affected, particularly in terms of job quality and productivity [10]. For farmers, growing seasons can be altered and precipitation levels can vary; for fishermen and women, water temperatures may differ, which could impact fish farming and catching rates; and forestry workers may have fewer trees to work with due to droughts or fires [9]. Emergency services may also be directly impacted as a result of increased rates of natural disasters (floods and droughts) [10]. Indirect impacts may include job insecurity, increased hours, or mental stress resulting from the need to change careers [9].
For some individuals and communities, the risks of climate change are twofold. First, the direct and indirect effects of climate change tend to cause cascading social and economic disruptions [11]. Second, government programs, policies, and responses produce unequal impacts [12]. For example, some occupations and industries are exposed to environmental events that are intensified by the geographical, socio-political, economic, or cultural contexts within which these events occur [4,13,14,15,16,17,18,19,20,21]. This reality is underscored by a growing body of research that demonstrates rising rates of depression, anxiety, suicide, and emotional distress among agricultural, forestry, fishery, recreation, and tourism industry workers [3,22,23,24].
As governments take steps to mitigate the effects of climate change, such as transitioning away from carbon-intensive extractive industries (e.g., oil, gas, mining, lumber), occupational workers and industry leaders must adapt to new regulations and economic impacts to generate social and economic stability and to do so in a way that creates just transition pathways for labourers, their families, and communities whose lives are connected to these industries [25,26]. Climate change adaptations are particularly relevant in Canada, where the national economy traditionally relies on the primary resource sector and intensive natural resource extraction [27]. Canada is developing climate mitigation strategies within a complex reality as Canada is simultaneously one of the top 10 contributors to carbon emissions; faces diverse and severe climate events that have led to deaths, damage to key infrastructure, and supply chain issues; and is working to advance zero-carbon policy reforms at the provincial and federal levels [28,29,30]. Decision-makers in every community across Canada are grappling with these troubling and conflicting realities as they navigate the harsh realities of climate change and other planetary shifts. Furthermore, Canadian workers, unions, employers, and industry groups are playing an increasingly significant role in planning for the necessary economic transitions, and building and renovating for better resilience is also required due to climate change [31,32,33]. How workers understand the risks of climate change, both broadly and within their specific sectors, is critical to developing policy spaces that will help mitigate climate change and its harmful effects on communities in a just and socially accountable manner. This research presents findings from British Columbia (BC), a socially and ecologically diverse province that is already engaged in the complexities of industry transitions.
Climate change vulnerability perceptions are a key aspect to consider when creating climate policies. According to the IPCC, vulnerability in the context of climate change is defined as one of two things: it is either the potential damage that could occur due to a climate event or the current state of the systems that could be impacted by a climate event [3,34,35]. Climate vulnerability and risk perceptions can be influenced by various factors, including personal experiences, social networks, cognitive biases, and political, cultural, and economic conditions [36,37]. For example, climate change risk perceptions can be influenced as certain occupations view observable markers of climate change (such as rising temperatures or floods), whereas other populations may not view these impacts and have different perceptions because of it [38]. Given that risk perceptions are subjective and influenced by these various factors, the perceived risks of climate change are likely to vary across individuals, communities, and industries [39,40]. As climate change risk perceptions are the result of so many factors and are subjective, they can be overestimated or underestimated compared to actual risk [37]. These perceptions of risk shape how industries and occupational groups can effectively cope with the impacts of climate change and the mitigation strategies developed [41]. To date, little research has focused on the perceived industry vulnerability due to climate change among employees in resource-intensive industries. This makes it hard to understand which industries may be ready to respond to direct and indirect climate effects in socially, culturally, economically, and environmentally fair and just ways.
The present study aims to (1) describe British Columbian workers’ perceived industry vulnerability to climate change, (2) understand which socio-demographic factors are associated with workers’ perceived industry vulnerability, and (3) assess whether workers’ perceived industry vulnerability is significantly different among nine different occupational categories.

2. Materials and Methods

2.1. Study Context

British Columbia is a thriving region in Canada, boasting a highly educated population. Specifically, 35% of individuals aged 25–64 hold a Bachelor’s degree or higher, and 66% possess a postsecondary certificate, diploma, or degree [42]. It is also very diverse, with 34.4% of the population being racialized, with large South Asian and Chinese populations [42]. The government of BC also strives for equity in its policies, services, and legislation, which is why it has implemented gender-based analysis plus (GBA+) to ensure all residents of BC have equitable access and treatment [43]. However, there are still inequities ingrained in BC, with women having a disproportionate share of minimum-wage jobs and being underrepresented in government positions, Indigenous and visible minority women earning less on average than White women, and 2SLGBTQIA+ individuals more than twice as likely to encounter violence than heterosexual residents [43].
Considering the devastating and long-lasting impact of fossil fuel-driven climate change, those affected by climate-related concerns share a common experience shaped by a system bound by time, space, and socioeconomic determinants of health. BC is increasingly experiencing significant environmental damage, leading to growing interest in climate change mitigation and adaptation, with commitments such as reducing greenhouse gas emissions by 40% before 2030 [29]. There have been notable human health costs due to climate change in recent years. Examples include damage caused by the 2021 Pacific Northwest American Heat Dome, the associated wildfires that destroyed entire communities such as the BC village of Lytton, and the BC flooding in November 2021, which destroyed infrastructure and agriculture in Southern BC with insurable damages of CAD 675 million and non-insured losses of CAD 4.7 billion [44]. These impacts amplify pre-existing inequities. For example, according to the 2016 Canadian census data, more households in BC earn less than CAD 30,000 per year compared to the Canadian average (52.1% vs. 45%) [45]. The economic profile for the province is also relevant as, according to the Canadian census data (2016), BC’s main industries include retail trade (11.5%), health care (11%), construction (8.1%), manufacturing (6.4%), and agriculture, forestry, fishing and hunting (2.6% combined) [45]. Politically, BC can situate climate-related research within broader federal contexts due to its commitment to reducing greenhouse gas emissions by 40% before 2030 [46]. The outlook data for BC’s labour market suggests that agriculture and fishing-related industries and occupations will have an annual employment growth rate of 0.3%, mining and oil and gas extraction will have a growth rate of 0.4%, and forestry and logging will have an annual decrease by 0.6% between 2024 and 2034 [47]. However, natural resource sectors follow ongoing boom and bust cycles due to social, political, economic, and ecological factors, making these industries even more exposed to the impacts of climate change.

2.2. Study Design

The current study leverages survey data from three rounds of data collection conducted between May 2021 and December 2021. The original scope of the CDMS was to explore differences in British Columbians’ mental health before and after extreme weather events [48]. The scope and context of the CDMS and details describing the CDMS research design (including recruitment strategies and power calculations) were previously described [48]. The current study pooled data from three different CDMS independent samples that were collected during three survey periods as follows: pre and post the 2021 North American Heat Dome, which took place between 12 May 2021 and 21 June 2021) (waves 1 & 2) [48,49]; and post 2021 British Columbia (BC) floods, which took place between 6 November, 2021 and 1 December, 2021 (wave 3) [50].
Participants were recruited using non-probabilistic sampling via paid advertisements on social media (Facebook and Instagram). Eligibility criteria consisted of individuals older than 16 years who were employed and living in BC, Canada, at the start of the study.

2.3. Ethics

Ethics approval for the BC-CDMS, including two CDMS amendments, was received from the Research Ethics Board (REB) at Simon Fraser University (SFU) (REB#: 30000309). Participants provided informed consent prior to study participation.

2.4. Variables

The outcome variable of interest was individuals’ self-reported, perceived industry vulnerability to climate change. The outcome-related data were based on participants being asked to respond on a 5-point Likert scale (from “Very Unlikely” to “Very Likely”) to the CDMS question “How likely or unlikely do you think the industry you’re working in will be affected by climate change?”. The outcome variable is treated in two different ways in the analysis portion of this study, further detailed in Section 2.5.
The main explanatory variable of interest was the self-reported occupational category, which contained nine category levels according to the 2016 Canadian census occupational classification system, the National Occupational Classification (NOC) 2016 [51]. The categories were “Natural Resources” (reference), “Art and Recreation,” “Education/Law/Government,” “Health,” “Management/Business,” “Manufacturing,” “Sales,” “Sciences,” and “Trades.” As defined by the NOC 2016, natural resource workers were workers in the mining, oil and gas, forestry/logging, agriculture, horticulture, and farming professions [51].
Potential confounders were selected based on a priori knowledge, combined with an initial scoping review that explored potential factors conceptually associated with the outcome and exposure of interest but not caused by the exposure itself. The multivariable model included the following confounding variables: age, gender, ethnicity, income, education, political orientation, wave number, and average time spent actively using social media per day [40,52,53,54,55,56,57]. The categories of these confounding variables included the following: age (in years: “16–24,” “25–44,” “45–64,” “>65”), gender (“man,” “non-binary,” “woman”), ethnicity (“White,” “Chinese,” “Indigenous,” “South Asian,” “Other”), individual-level income (“less than CAD 30,000,” “CAD 30,000 to CAD 59,999,” “CAD 60,000 to CAD 89,999”), education (“high school or less,” “some postsecondary training,” “bachelor’s degree or higher”), political orientation (“extremely conservative,” “extremely liberal,” “moderately conservative,” “moderately liberal,” “neither liberal nor conservative,” “slightly conservative,” “slightly liberal”), geographical location (“rural,” “suburban,” “urban”), the wave of data collection (“wave 1,” “wave 2,” “wave 3”), and average daily time (in hours) spent actively using social networking like Facebook, Twitter, or Reddit (“less than 2 h,” “2 h or more”).

2.5. Data Analysis

Descriptive statistical analyses assessed the distribution of observations and differences among different participant groups using the Kruskal–Wallis one-way analysis of rank test for continuous variables and the Chi-square tests for categorical variables. The results for these analyses are separated into two categories for the primary outcome variable, with the first being “unsure” or “very/somewhat unlikely,” and the second being “very” or “somewhat likely.” Unsure responses could indicate that participants are truly unaware of the potential risks to their occupational fields due to a lack of education or information avoidance [58]. Removing this option could increase measurement error and reduce statistical power [58]. With our operationalization, our study tests for the presence of at least some perceived industry vulnerability, as opposed to being unaware of or denying any potential industry vulnerability, thereby helping to understand our intended knowledge gaps better. We also conducted a sensitivity analysis where we operationalized the outcome variable with the categories “very likely”/”somewhat likely”/”unsure” vs. “very unlikely”/”somewhat unlikely” to test whether our operationalization had a meaningful impact on the results.
Unadjusted and adjusted ordinal logistic regression models were constructed to identify which occupations were associated with a perception that climate change is very or somewhat likely to affect the industry in the future, with the Natural Resources occupation being the reference category. The confounding effects of age, gender, ethnicity, income, education, political orientation, and geographic population density were controlled for, as were design effects that accounted for frequent social media use and the timing of the survey wave, using adjusted ordinal logistic regression modelling. The outcome variable, individuals’ self-reported perceived industry vulnerability to climate change, is operationalized as an ordinal variable with five levels, ranging from “very unlikely” to “very likely.”
Due to the observational nature of this study, our findings may suggest significant associations between exposure and outcome variables; however, they cannot prove causality [59].

3. Results

Initially, 1704 participants completed a survey across one of the three waves of data collection. Then, 538 participants were removed because they were not employed full-time or part-time or did not provide their occupational industry, leaving 1166 participants. From the 1166 remaining participants, 1100 answered the survey question about the perceived impact of climate change on their industry. A further 223 responses were omitted due to missing demographic information. Therefore, we included 877 participants in the study.
Descriptive statistics for the pooled data in this study’s sample, stratified by their perceived effect of climate change on their industry, are presented in Table 1. Table A1 shows non-stratified descriptive statistics. Overall, 49.5% of participants self-identified as men, 46.1% as women, and 4.4% as non-binary. Most individuals were White (79.6%), between the ages of 25 and 44 (45.2%) and 45 and 64 (30.4%), and adopting a political orientation as either neither liberal nor conservative (20.2%), moderately liberal (25.0%), or extremely liberal (26.0%). Figure A1 displays participants’ perceived likelihood of climate change impacting their industry, using boxplots that are stratified by participants’ occupations. Bivariate statistics results suggest that there were significant differences in the perceived likelihood that climate change will impact the participants’ industry among the various occupation groups (p < 0.001), age (p < 0.001), gender (p < 0.001), income groups (p < 0.001), education groups (p <0.001), political orientation groups (p < 0.001), ethnicity (p = 0.012), time spent on phone (p = 0.016), and survey wave (p = 0.045). Our sensitivity analysis revealed that the operationalization of the outcome variable had an impact on the bivariate analysis, as indicated by the p-value of the survey wave variable becoming non-significant (p = 0.078).
As shown in Table 2, among the 877 participants, 58.3% and 41.7% perceived that climate change impacts on their industry were very or somewhat likely (n = 511) and very or somewhat unlikely (n = 366), respectively. Among 877 participants, 66.1% of Natural Resources occupation workers perceived it was very or somewhat likely that climate change would impact their industry, compared to 78.3% in Science, 71.4% in Art and Recreation, 63.3% in Health, 63.2% in Education/LawGovernment, 51.6% in Manufacturing, 50.0% in Sales, 40.2% in Trades, and 38.8% in Management/Business. Among those with occupations in the Natural Resources category, 52% believed that climate impacts are very likely to affect their industry (n = 61), while 32% believed these impacts to be very or somewhat unlikely (n = 38). Most individuals with occupations in the Management/Business (56.3%) and Trades (53.3%) categories believed that climate impacts on their industry are very/somewhat unlikely. Our sensitivity analysis revealed that unsure responses were limited to 3.2% in Manufacturing (among those who identified their industry as manufacturing), 1.7% in Natural Resources, 4.8% in Education/Law/Government, 5.0% in Management/Business, 6.5% in Sales, 4.5% in Science, and 6.6% in Trades. This would result in the proportions in Table 2 shifting by these amounts toward the “very likely” or “somewhat likely” group. With this, only the sales occupation would shift to having the majority of participants being very, somewhat, or unsure about the perceived climate impacts on their industry. In contrast, the remaining variables would remain unchanged in terms of the majority’s opinions.
The adjusted model results are presented in Table 3; the variables adjusted for include age, gender, ethnicity, income, education, political orientation, survey wave, and frequency of social media use. Results suggest that, after adjusting for the aforementioned variables, most occupations had a lower perceived likelihood of climate change impacting their industry than those in natural resource occupations. This includes significantly lower effects among those in Art and Recreation (aOR = 0.55, [95% CI = 0.31–0.99]), Education/Law/Government (adjusted odds ratio (aOR) = 0.46, [95% CI = 0.29–0.72]), Management/Business (aOR = 0.27, [95% CI = 0.15–0.47]), Manufacturing (aOR = 0.42, [95% CI = 0.20–0.90]), Sales (aOR = 0.39, [95% CI = 0.23–0.67]), and Trades (aOR = 0.33, [95% CI = 0.20–0.55]). Only people belonging to the Health (aOR = 0.69, [95% CI = 0.39–1.23]) and Science (aOR = 0.89, [95% CI = 0.48–1.67]) occupations were not significantly different from those belonging to the Natural Resources occupation group.

4. Discussion

This study aimed to describe British Columbian workers’ perceived industry vulnerability to climate change and assess whether workers’ perceived industry vulnerabilities are influenced by their occupations. Results suggest that people working in the natural resources sector have a significantly higher perceived likelihood that climate change will impact their industry than those working in a range of other provincial sectors; however, those in management positions within these sectors expressed the least concerns. We also observed considerable awareness of climate change impacts on the labour market across sectors, with the highest awareness among people reporting to work in Science- and Health-related categories and the lowest awareness among people who work in Management/Business. Additionally, the analysis suggests that the perceived concern that climate change will have a higher impact on their industry is more significant among women and younger adults between the ages of 25 and 44 and those whose income is less than CAD 30,000 per year.
These results are consistent with the existing literature on fossil fuel-driven climate change, which discusses it as a consequence of global and national power structures. The distribution of resources and influence disproportionately impacts vulnerable and marginalized populations [2,60]. From this perspective, occupation can be used as a marker reflecting various socioeconomic factors in describing individuals’ power, including lifestyle, risk perceptions, or adaptive capacity. The fact that our results suggest that those in management positions, which conceptually have higher positional power, were the least concerned about climate change impacts is consistent with this theory. However, it is essential to recognize that the management industry is also hierarchical, with management workers encompassing everyone from support staff to senior executive officers, each with drastically different levels of power and influence [61]. Our analysis does not distinguish between different levels of management. Therefore, it is essential to consider the different levels of power within occupations, including management, in future studies.
Another way of considering power is through other measures of socioeconomic status, such as income. Compared to management positions, which have high levels of power and income, those in natural resource occupations, such as a nursery labourer (median salary around CAD 17.85/h in BC) and general livestock farm workers (median salary around CAD 21.00/h in BC), with lower levels of power and income thought that climate change would affect their industries [62,63]. Similarly, as tested in this study, the perceived concern that climate change will have a greater impact on their industry is more significant among those who earn less than CAD 30,000 compared to higher-income earners. Therefore, although our findings may suggest power structures, particularly given that 30% of those working in management earn over CAD 90,000 per year, power and its role in climate risk perceptions are a complex and dynamic issue that requires further analysis and discussion.
Additionally, the results reflect findings from other studies, which suggest that workers in science fields, such as climate change or public health, tend to be more attuned to general climate-related concerns [3,13,15]. In the Health-related category, public safety personnel, such as first responders and health support workers, have been highlighted as two primary populations at risk of climate change-related psychosocial risks in Canada, and thus, our findings suggest accurate risk assessment among this population [30,64,65,66].
The higher likelihood that individuals in the Natural Resources category perceived climate-related industry vulnerability as very/highly likely, compared with most other occupations, is consistent with the fact that industries related to this occupational category are at high risk of experiencing direct climate change impacts due to land and ecosystem damage by worsening effects of climate-driven disasters [8]. As these communities rely on the land and waters for their livelihood, this occupational group is expected to experience a wide spectrum of industry-wide concerns, including food or job insecurity, dislocation and unstable housing, loss of personal or occupational identity, or loss of lifestyle values as the environment upon which their occupation depends is further degraded. While there is no known research to quantify such assumptions, perceived future industry vulnerability to climate change can provoke severe emotional distress among workers in the extractive industries, including increased levels of climate change anxiety and depression, especially among farmers [23,67,68]. On the other hand, some natural resources workers may experience frustration due to sustainability-related changes in their industry, which can lead to a loss of status or independence [69]. However, it is also crucial to note that nuance exists in these discussions, as natural resource industries (e.g., oil and gas) have disproportionately contributed to climate change and will need to be phased out as countries strive to meet their climate goals, which may impact many employees [70,71,72]. Our findings also signal accurate risk perception among this population about climate impacts.
Compared to those in natural resources occupations, all other occupations had a lower perceived likelihood of climate change impacting their industry. This finding aligns well with prior research, as workers’ perceptions of their climate change vulnerability and its potential impact on their fields are likely strongly tied to personal experiences, social networks, cognitive biases, and political, cultural, and economic conditions [36,37]. Natural resources workers, who are potentially undergoing an economic transition toward net-zero technologies, are also experiencing increased rates of mental health issues within their workplaces [22,23,24,29]. They are likely being both directly impacted by climate change events and indirectly affected through these secondary mechanisms [29]. Other occupations, in comparison, may not encounter these secondary factors as frequently and thus may have a lower perception of risk regarding how climate change may impact their industry.
When examining the perceived likelihood of climate change impacting their industries, our regression findings for workers in Science and Health occupations were not statistically significant, and they had wide confidence intervals. Wide confidence intervals could be the result of small sample sizes or a diverse range of responses in the sample [73]. The health and science occupations were on the smaller side in terms of sample size, with 79 and 69 participants, respectively, but were not the smallest, with Manufacturing having 31 participants. It is possible that variation in responses led to a higher standard error, with 22% of respondents selecting “very unlikely” compared to 49% selecting “very likely” for Health workers; in contrast, Science had 13% select “very unlikely” and 61% say “very likely.”
Previous studies have shown that climate change risk perception is complex and is compounded by multiple factors, not only direct experiences of climate events [74]. For example, economic disruption is a key factor that can impact one’s perception of climate change risk, as well as cause stress and anxiety [75]. Our study supports this, as although all participants resided in BC, levels of risk perception differed based on factors such as age, gender, and income when controlling for other factors.
Perspectives of risk and how the public conceptualizes climate change, such as those highlighted in this article, can affect how individuals experience community and cope with feelings of hopelessness or insecurity in the face of climate impacts [76]. Additionally, knowledge of perceived climate risks among workers may help researchers assess labourers’ perceived trust in adaptation goals, as well as their readiness to change and adopt adaptation strategies, which could enhance the effectiveness of climate change and public health interventions [77,78]. Adaptation is shaped by community dynamics in Canada, with a potential hypothesis being that increased population and economic diversity among communities and natural ecosystems can promote resilience and sustainability [79].

4.1. Implications

In addition to the public’s readiness to engage in climate action and adequately cope with climate change-related challenges, a main driver of human vulnerability to climate change is the capacity of local, municipal, and national governments to offer essential infrastructure, services, and socioeconomic support before, during, and in the aftermath of climate-driven events [19]. This study provides insights into social and human vulnerability dynamics, which are essential components in designing effective climate adaptation strategies that can enhance collective resilience and mitigate socioeconomic costs associated with maladaptation.
As previously outlined, the transition toward sustainable economics must integrate the public’s perspectives and perceptions of climate change risks, including those views held by people employed within and dependent upon these industries [76,77,78]. Our findings promote knowledge sharing among researchers, decision-makers, and the public by providing opportunities for collaboration and engagement with the public. This can take the form of integrating the public’s perspectives and prioritizing the views of communities most impacted by these industries into discussions and policy proposals related to climate vulnerability and resilience building. This approach increases our understanding of how to design effective climate change responses aligned with community realities so that they can benefit the public from health, socioeconomic, and ecological perspectives. As discussed by Monaco [6], aiming for a just transition ensures that no group or sector is left behind. However, ensuring justice requires intersectoral interventions, with policymakers, communities, and workers working together to find solutions, whether through community-led initiatives that discuss the depletion of resources and an unstable economy or policymakers introducing carbon pricing or new regulations and standards [6]. As BC increasingly engages in these complex efforts, examples of this work exist, including the various efforts to support workers in the East Kootenays to manage reliance on coal mining and in the Interior and Coastal regions to manage deferrals for old-growth logging. This includes the Rural Dividend Program, the Forest Employment Program, and the Bridging to Retirement Program [80,81,82].
Additionally, the Canadian government has recently described several initiatives that aim to support the upskilling and reskilling of workers in transforming sectors like natural resources (oil and gas/agriculture) and energy in the form of lowering energy costs for homes, more renewable electricity, and easier electric vehicle charging [29]. Strategies like this have been offered in the province during previous industry downturns and can serve as a tool within a suite of just transition strategies. While these policy plans and investments will provide some support, they are likely insufficient.
BC is a province that has historically relied on natural resources as a significant portion of its GDP, is transitioning toward more sustainable energy sources, and has experienced several extreme climate events in recent years [29,48,83]. With this in mind, some of this study’s findings may be considered context-specific. For example, natural resources workers may be more aware of potential impacts on their occupations due to public commitments made at both the federal and provincial levels by the Canadian government. Additionally, initiatives such as resource-allocation initiatives or sectors that require reskilling may differ between contexts.
However, other study findings are more generalizable. Our study findings can provide preliminary insights that can urgently forward research and education to support worker preparedness for reskilling, upskilling, and retirement bridging. Additionally, climate adaptation efforts must be rooted in empirical knowledge of climate vulnerability factors, which includes considering the public’s evolving perspectives and experiences as they navigate the political and economic ecologies of fossil fuel-driven climate change impacts. Crucially, communities must also be able to participate in the work to inform and manage these transitions, particularly rural, undiversified, resource-based communities that will be most directly impacted by climate change. Evidence highlighting the public’s concerns, such as the findings presented in this study, can help enhance a sense of community cohesion, direct resources, increase community identity, and ultimately catalyze climate resilience.

4.2. Strengths and Limitations

While some climate vulnerability research explores climate risk perception and adaptive capacity among specific occupations, our study is unique due to its focus on comparing and contrasting such research considerations among all different categories of work, particularly in the Canadian context after several severe climate events [2,13,14,15,16,17,18,19,20,21]. Additionally, this study is timely as it was conducted within a Canadian context to explore climate change-related concerns among workers in the context of industry-related trends and as influenced by occupational characteristics in British Columbia. As such, this study delivers insights describing potential challenges and perceived risks that many Canadian workers are currently navigating to adapt to the rapidly evolving socio-political and labour market ecosystem in the Canadian climate change context. Considering the limited climate vulnerability research in Canada, the study’s geographic limitations to the province of BC should be evaluated in the context of its primary scope, which is to provide foundational descriptive insights to encourage further research in this field in Canada and internationally.
While the analyses used are strengthened by a large sample size contributing to significant statistical power, our study relies on a cross-sectional design [84]. Therefore, the results have limited causal interpretability [84]. Our operationalization of the outcome variable to include the “unsure” option, with the “somewhat unlikely” and “very unlikely” categories, does impact the results of the Kruskal–Wallis and Chi-Square tests and proportions. However, using a sensitivity analysis, we found that the differences were not drastic, with one variable losing significance in the Kruskal–Wallis and Chi-Square tests, and one occupation becoming a majority “somewhat or very likely” with the additional unsure participants. Additionally, the study employed convenience sampling methods and recruited participants through social media advertisements, limiting the sample’s representativeness in BC, as reflected in the descriptive statistics results. Our sample was predominantly White, highly educated, and politically liberal. This is a particularly significant limitation, given the growing importance of equity-informed designs in advancing climate change-related research. The disproportionate sample demographics may also limit the generalizability of the findings. It is possible that our sampling strategy, which relied on the internet, may have excluded marginalized participants, resulting in the type of sample we observed. As these demographics are more plentiful in the sample and may answer the survey in a certain way that differs from the true study population, this could lead to under- or overestimating findings or incorrect results [85]. Therefore, findings should be interpreted with caution. While non-probabilistic sampling through social media advertisements can introduce selection biases, research indicates that this method can effectively reach diverse and hard-to-reach populations, enhancing sample representativeness in certain contexts [86,87]. Therefore, while selection bias is a consideration, strategic social media recruitment can mitigate representativeness concerns. However, as seen through our overrepresentation of White, highly educated, and politically liberal individuals in our sample, future studies should alter this social media recruitment methodology. The data used were self-reported, which increases the risk of information and social desirability bias [88]. Additionally, while the study provided preliminary insights into how power in different professions can influence risk perceptions and readiness to engage in climate action, it has a limited ability to offer nuanced insights into how power affects adaptive capacities across different population groups. More research integrating intersectional factors that can account for such inequities and test various affordable interventions in different populations is urgently needed.

5. Conclusions

Among the first to evaluate workers’ perceived industry vulnerability to climate change among British Columbian workers, our study suggests that BC workers perceive future climate change impacts on their industry as very or highly likely across most occupational categories. Additionally, natural resources workers are more likely to perceive these impacts as concerning than most other occupational categories. While the Canadian government has recently pledged to support Canadian workers throughout the country’s just transition to sustainable economies and reduced greenhouse gas emissions [29], these climate adaptation efforts must be rooted in empirical knowledge of worker’s perception of risk and insecurity and how these risks could impact the populations’ mental health and the consequent impacts on work.

Author Contributions

Conceptualization, A.B. and K.G.C.; methodology, K.G.C.; formal analysis, A.B. and K.G.C.; investigation, K.G.C.; data curation, K.G.C.; writing—original draft preparation, A.B.; writing—review and editing, A.B., A.S., C.H.L., G.M., K.C., M.K.G., R.S.H., T.T. and K.G.C.; supervision, K.G.C.; project administration, A.B.; funding acquisition, R.S.H. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Canadian Institutes of Health Research (CIHR) through PI Dr. Robert Hogg’s CIHR Foundation Grant (#143342).

Data Availability Statement

Data is available upon request.

Conflicts of Interest

The authors declare no conflicts of interest.

Appendix A

Table A1. Socio-demographic characteristics of individuals included in the sample.
Table A1. Socio-demographic characteristics of individuals included in the sample.
n = 877
Occupation
Natural Resources118 (13.5)
Art and Recreation77 (8.8)
Education/Law/Government209 (23.8)
Health79 (9.0)
Management/Business80 (9.1)
Manufacturing31 (3.5)
Sales92 (10.5)
Science69 (7.9)
Trades122 (13.9)
Age (years)
16–24122 (13.9)
25–44396 (45.2)
45–64267 (30.4)
65 years and over92 (10.5)
Gender
Man434 (49.5)
Non-binary39 (4.4)
Woman404 (46.1)
Ethnicity
White698 (79.6)
Chinese28 (3.2)
Indigenous42 (4.8)
Other94 (10.7)
South Asian15 (1.7)
Income
Less than CAD 30,000299 (34.1)
CAD 30,000 to CAD 59,999229 (26.1)
CAD 60,000 to CAD 89,999172 (19.6)
CAD 90,000 or more177 (20.2)
Education
High School or Less112 (12.8)
Some Postsecondary Training297 (33.9)
Bachelor’s Degree or higher468 (53.4)
Political Orientation
Extremely conservative43 (4.9)
Moderately conservative115 (13.1)
Slightly conservative54 (6.2)
Neither liberal nor conservative177 (20.2)
Slightly liberal41 (4.7)
Moderately liberal219 (25.0)
Extremely liberal228 (26.0)
Geographical location
Rural421 (48.0)
Suburban150 (17.1)
Urban306 (34.9)
Data collection wave
Wave 1365 (41.6)
Wave 2299 (34.1)
Wave 3213 (24.3)
Frequency of social media use
Less than 2 h460 (52.5)
2 h or more417 (47.5)
Climate 13 00157 g0a1
Figure A1. Participants’ Perceived Likelihood of Climate Change Impacting Their Industry, Stratified by Self-Reported Occupational Classifiers. Each box represents the interquartile range (IQR), with the lower and upper hinges marking the 25th and 75th percentiles, respectively. The horizontal line within each box denotes the median, while whiskers extend to the smallest and largest values within 1.5 times the IQR. Points beyond the whiskers are plotted as outliers. Superimposed horizontal bars indicate the mean and standard error of the mean for each occupational group. Individual participant responses are jittered for visibility. This visualization illustrates both the central tendency and distribution of perceived impact within and across occupational sectors.
Figure A1. Participants’ Perceived Likelihood of Climate Change Impacting Their Industry, Stratified by Self-Reported Occupational Classifiers. Each box represents the interquartile range (IQR), with the lower and upper hinges marking the 25th and 75th percentiles, respectively. The horizontal line within each box denotes the median, while whiskers extend to the smallest and largest values within 1.5 times the IQR. Points beyond the whiskers are plotted as outliers. Superimposed horizontal bars indicate the mean and standard error of the mean for each occupational group. Individual participant responses are jittered for visibility. This visualization illustrates both the central tendency and distribution of perceived impact within and across occupational sectors.
Climate 13 00157 g0a1

References

  1. Intergovernmental Panel on Climate Change (IPCC). Climate Change 2021—The Physical Science Basis: Working Group I Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 1st ed.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2023. [Google Scholar] [CrossRef]
  2. Intergovernmental Panel on Climate Change (IPCC) (Ed.) Human Health: Impacts, Adaptation, and Co-Benefits. In Climate Change 2014—Impacts, Adaptation and Vulnerability: Part A: Global and Sectoral Aspects: Working Group II Contribution to the IPCC Fifth Assessment Report: Volume 1: Global and Sectoral Aspects; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2014; Volume 1, pp. 709–754. [Google Scholar] [CrossRef]
  3. IPCC (Ed.) Impacts, Adaptation, and Vulnerability; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2001. [Google Scholar]
  4. IPCC. Human Health: Impacts, Adaptation, and Co-Benefits—IPCC 2014. Available online: https://www.ipcc.ch/report/ar5/wg2/human-health-impacts-adaptation-and-co-benefits/ (accessed on 31 March 2025).
  5. Lavell, A.; Oppenheimer, M.; Diop, C.; Hess, J.; Lempert, R.; Li, J.; Muir-Wood, R.; Myeong, S.; Moser, S.; Takeuchi, K.; et al. Climate Change: New Dimensions in Disaster Risk, Exposure, Vulnerability, and Resilience. In Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation, 1st ed.; Field, C.B., Barros, V., Stocker, T.F., Dahe, Q., Eds.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2012; pp. 25–64. [Google Scholar] [CrossRef]
  6. Monaco, S. Climate (of) change: The promise and perils of technology in achieving a just transition. Int. J. Sociol. Soc. Policy 2023, 43, 129–145. [Google Scholar] [CrossRef]
  7. Allwood, G. Mainstreaming Gender and Climate Change to Achieve a Just Transition to a Climate-Neutral Europe. JCMS J. Common Mark. Stud. 2020, 58 (Suppl. S1), 173–186. [Google Scholar] [CrossRef]
  8. Adam-Poupart, A.; Labrèche, F.; Smargiassi, A.; Duguay, P.; Busque, M.-A.; Gagné, C.; Zayed, J. Impact of Climate Change on Occupational Health and Safety. 2013. Available online: https://pharesst.irsst.qc.ca/rapports-scientifique/310 (accessed on 31 March 2025).
  9. Canadian Centre for Occupational Health and Safety. Climate Change: Workplace Impacts; Canadian Centre for Occupational Health and Safety: Hamilton, ON, Canada, 2023. [Google Scholar]
  10. European Foundation for the Improvement of Living and Working Conditions. Job Quality Side of Climate Change. Publications Office, 2024. Available online: https://data.europa.eu/doi/10.2806/693002 (accessed on 5 July 2025).
  11. Cunsolo Willox, A.; Harper, S.L.; Ford, J.D.; Edge, V.L.; Landman, K.; Houle, K.; Blake, S.; Wolfrey, C. Climate change and mental health: An exploratory case study from Rigolet, Nunatsiavut, Canada. Clim. Change 2013, 121, 255–270. [Google Scholar] [CrossRef]
  12. Wlasichuk, J. School Strike 4 Climate (Justice): Exploring the experiences of climate strike organisers in Canada. Aust. J. Environ. Educ. 2022, 38, 121–123. [Google Scholar] [CrossRef]
  13. Berry, H.L.; Bowen, K.; Kjellstrom, T. Climate change and mental health: A causal pathways framework. Int. J. Public Health 2010, 55, 123–132. [Google Scholar] [CrossRef] [PubMed]
  14. Bratu, A.; McLinden, T.; Kooij, K.; Ye, M.; Li, J.; Trigg, J.; Sereda, P.; Nanditha, N.G.A.; Lima, V.; Guillemi, S.; et al. Incidence of diabetes mellitus among people living with and without HIV in British Columbia, Canada between 2001 and 2013: A longitudinal population-based cohort study. BMJ Open 2021, 11, e048744. [Google Scholar] [CrossRef] [PubMed]
  15. Clayton, S. Climate anxiety: Psychological responses to climate change. J. Anxiety Disord. 2020, 74, 102263. [Google Scholar] [CrossRef] [PubMed]
  16. Hayes, K.; Blashki, G.; Wiseman, J.; Burke, S.; Reifels, L. Climate change and mental health: Risks, impacts and priority actions. Int. J. Ment. Health Syst. 2018, 12, 28. [Google Scholar] [CrossRef] [PubMed]
  17. Hayes, K.; Poland, B. Addressing Mental Health in a Changing Climate: Incorporating Mental Health Indicators into Climate Change and Health Vulnerability and Adaptation Assessments. Int. J. Environ. Res. Public Health 2018, 15, 1806. [Google Scholar] [CrossRef] [PubMed]
  18. Hrabok, M.; Delorme, A.; Agyapong, V.I.O. Threats to Mental Health and Well-Being Associated with Climate Change. J. Anxiety Disord. 2020, 76, 102295. [Google Scholar] [CrossRef] [PubMed]
  19. Intergovernmental Panel on Climate Change (IPCC). Climate Change 2022—Impacts, Adaptation and Vulnerability: Working Group II Contribution to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change, 1st ed.; Cambridge University Press: Cambridge, UK; New York, NY, USA, 2023. [Google Scholar] [CrossRef]
  20. Masuda, J.R.; Poland, B.; Baxter, J. Reaching for environmental health justice: Canadian experiences for a comprehensive research, policy and advocacy agenda in health promotion. Health Promot. Int. 2010, 25, 453–463. [Google Scholar] [CrossRef] [PubMed]
  21. Wilson-Genderson, M.; Heid, A.R.; Pruchno, R. Long-term effects of disaster on depressive symptoms: Type of exposure matters. Soc. Sci. Med. 2018, 217, 84–91. [Google Scholar] [CrossRef] [PubMed]
  22. Bonsal, B.R.; Wheaton Elaine, E.; Chipanshi Aston, C.; Lin Charles Sauchyn David, J.; Wen, L. Drought Research in Canada: A Review. Atmos. Ocean. 2011, 49, 303–319. [Google Scholar] [CrossRef]
  23. Ellis, N.R.; Albrecht, G.A. Climate change threats to family farmers’ sense of place and mental wellbeing: A case study from the Western Australian Wheatbelt. Soc. Sci. Med. 2017, 175, 161–168. [Google Scholar] [CrossRef] [PubMed]
  24. Sutherland, L.-A.; Glendinning, T. Farm Family Coping with Stress: The Impact of the 1998 Ice Storm. J. Comp. Fam. Stud. 2008, 39, 527–543. [Google Scholar] [CrossRef]
  25. BloombergNEF. Energy Transition Investment Trends. 2025. Available online: https://about.bnef.com/energy-transition-investment/ (accessed on 28 March 2025).
  26. United Nations Environment Programme International Resource Panel. Global Resources Outlook 2019: Natural Resources for the Future We Want. 2019. Available online: https://wedocs.unep.org/20.500.11822/27517 (accessed on 31 March 2025).
  27. Galway, L.P.; Beery, T. Exploring Climate Emotions in Canada’s Provincial North. Front. Psychol. 2022, 13, 920313. [Google Scholar] [CrossRef] [PubMed]
  28. Government of Canada Environment and Climate Change. Global Greenhouse Gas Emissions [Research]. 2012. Available online: https://www.canada.ca/en/environment-climate-change/services/environmental-indicators/global-greenhouse-gas-emissions.html (accessed on 28 March 2025).
  29. Government of Canada. 2030 Emissions Reduction Plan: Clean Air, Strong Economy [Forward Regulatory Plan]. 2022. Available online: https://www.canada.ca/en/services/environment/weather/climatechange/climate-plan/climate-plan-overview/emissions-reduction-2030.html (accessed on 28 March 2025).
  30. Tsakonas, K.; Badyal, S.; Takaro, T.; Buse, C. Occupational health impacts of climate-related emergency events on health system workers in British Columbia. Int. J. Disaster Risk Reduct. 2025, 118, 105227. [Google Scholar] [CrossRef]
  31. Allan, K.; Robinson, J. Working towards a green job?: Autoworkers, climate change and the role of collective identity in union renewal. J. Ind. Relat. 2022, 64, 585–607. [Google Scholar] [CrossRef]
  32. Calvert, J. Why the Construction Trades Have a Valuable Role in Meeting the Climate Challenge. Labour/Le Travail 2024, 94, 223–278. [Google Scholar] [CrossRef]
  33. Hudson, M. Climate change and energy transition: Perceptions of emergency, responsibility, and the future imaginaries of Manitoban unionized workers. Futures 2024, 157, 103339. [Google Scholar] [CrossRef]
  34. Allen, K. Vulnerability reduction and the community-based approach: A Philippines study. In Natural Disaster and Development in a Globalizing World; Routledge: London, UK, 2003. [Google Scholar]
  35. Jones, R.; Boer, R. Assessing Current Climate Risks. 2003. Available online: https://www4.unfccc.int/sites/NAPC/Country%20Documents/General/apf%20technical%20paper04.pdf (accessed on 5 July 2025).
  36. Azócar, G.; Billi, M.; Calvo, R.; Huneeus, N.; Lagos, M.; Sapiains, R.; Urquiza, A. Climate change perception, vulnerability, and readiness: Inter-country variability and emerging patterns in Latin America. J. Environ. Stud. Sci. 2021, 11, 23–36. [Google Scholar] [CrossRef]
  37. Nelson, L.K.; Cullen, A.C.; Koehn, L.E.; Harper, S.; Runebaum, J.; Bogeberg, M.; Strawn, A.; Levin, P.S. Understanding perceptions of climate vulnerability to inform more effective adaptation in coastal communities. PLoS Clim. 2023, 2, e0000103. [Google Scholar] [CrossRef]
  38. Alcantara, L.B.; Creencia, L.A.; Madarcos, J.R.V.; Madarcos, K.G.; Jontila, J.B.S.; Culhane, F. Climate change awareness and risk perceptions in the coastal marine ecosystem of Palawan, Philippines. UCL Open Environ. 2023, 5, e054. [Google Scholar] [CrossRef] [PubMed]
  39. Kahan, D.M. Fixing the Communications Failure. Nature 2010, 463, 296–297. [Google Scholar] [CrossRef]
  40. van der Linden, S. The social-psychological determinants of climate change risk perceptions: Towards a comprehensive model. J. Environ. Psychol. 2015, 41, 112–124. [Google Scholar] [CrossRef]
  41. Whitmarsh, L.; Capstick, S. 2—Perceptions of climate change. In Psychology and Climate Change; Clayton, S., Manning, C., Eds.; Academic Press: Wooster, OH, USA; Saint Paul, MN, USA, 2018; pp. 13–33. [Google Scholar] [CrossRef]
  42. Statistics Canada. Profile Table, Census Profile, 2021 Census of Population—British Columbia [Province]. 2023. Available online: https://www12.statcan.gc.ca/census-recensement/2021/dp-pd/prof/index.cfm?Lang=E (accessed on 4 July 2025).
  43. Government of British Columbia. Gender-Based Analysis Plus. Available online: https://www2.gov.bc.ca/assets/gov/british-columbians-our-governments/services-policies-for-government/gender-equity/factsheet-gba.pdf (accessed on 4 July 2025).
  44. Lee, M.; Parfitt, B. The Staggering Price of Climate Inaction. The Tyee. 2022. Available online: https://narrations.ad-auris.com/widget/tyee/the-staggering-price-of-climate-inaction (accessed on 7 March 2025).
  45. Statistics Canada. Census Profile, 2016 Census—British Columbia [Province] and Canada [Country]. 2017. Available online: https://www12.statcan.gc.ca/census-recensement/2016/dp-pd/prof/details/page.cfm?Lang=E&Geo1=PR&Code1=59&Geo2=PR&Code2=01&SearchText=Canada&SearchType=Begins&SearchPR=01&B1=All&TABID=1&type=1 (accessed on 31 March 2025).
  46. Ministry of Environment and Climate Change. Climate Change. 31 March 2025. Available online: https://www2.gov.bc.ca/gov/content/environment/climate-change (accessed on 31 March 2025).
  47. WorkBC. BC’s Labour Market Outlook. 31 March 2025. Available online: https://www.workbc.ca/research-labour-market/bc-labour-market-outlook-2024 (accessed on 31 March 2025).
  48. Bratu, A.; Card, K.G.; Closson, K.; Aran, N.; Marshall, C.; Clayton, S.; Gislason, M.K.; Samji, H.; Martin, G.; Lem, M.; et al. The 2021 Western North American heat dome increased climate change anxiety among British Columbians: Results from a natural experiment. J. Clim. Change Health 2022, 6, 100116. [Google Scholar] [CrossRef]
  49. Human Rights Watch. Canada: Disastrous Impact of Extreme Heat. 2021. Available online: https://www.hrw.org/news/2021/10/05/canada-disastrous-impact-extreme-heat (accessed on 31 March 2025).
  50. Richards-Thomas, T.S.; Déry, S.J.; Stewart, R.E.; Thériault, J.M. Climatological context of the mid-November 2021 floods in the province of British Columbia, Canada. Weather. Clim. Extrem. 2024, 45, 100705. [Google Scholar] [CrossRef]
  51. Statistics Canada. National Occupational Classification (NOC) 2016 Version 1.3. 2019. Available online: https://www.statcan.gc.ca/en/subjects/standard/noc/2016/indexV1.3 (accessed on 7 March 2025).
  52. Jost, J.T. The end of the end of ideology. Am. Psychol. 2006, 61, 651–670. [Google Scholar] [CrossRef] [PubMed]
  53. Latkin, C.A.; Dayton, L.; Lee, D.-I.; Yi, G.; Uzzi, M. Correlates of Levels of Willingness to Engage in Climate Change Actions in the United States. Int. J. Environ. Res. Public Health 2021, 18, 9204. [Google Scholar] [CrossRef] [PubMed]
  54. Mavrodieva, A.V.; Rachman, O.K.; Harahap, V.B.; Shaw, R. Role of Social Media as a Soft Power Tool in Raising Public Awareness and Engagement in Addressing Climate Change. Climate 2019, 7, 122. [Google Scholar] [CrossRef]
  55. Ren, H.; Wu, X.; Zhao, W.; Cheng, J. Potential Risks of PM2.5 in Urban and Suburban Environments: A Dual Perspective on Chemical Constituents and Pollution Sources. Aerosol Air Qual. Res. 2024, 24, 230315. [Google Scholar] [CrossRef]
  56. Schuldt, J.P.; Pearson, A.R. The role of race and ethnicity in climate change polarization: Evidence from a U.S. national survey experiment. Clim. Change 2016, 136, 495–505. [Google Scholar] [CrossRef]
  57. Tobler, C.; Visschers, V.H.M.; Siegrist, M. Addressing climate change: Determinants of consumers’ willingness to act and to support policy measures. J. Environ. Psychol. 2012, 32, 197–207. [Google Scholar] [CrossRef]
  58. Waters, E.A.; Kiviniemi, M.T.; Hay, J.L.; Orom, H. Dismissing “don’t know” responses to perceived risk survey items threatens the validity of theoretical and empirical behavior change research. Perspect. Psychol. Sci. J. Assoc. Psychol. Sci. 2022, 17, 841–851. [Google Scholar] [CrossRef] [PubMed]
  59. Andrade, C. Cause versus association in observational studies in psychopharmacology. J. Clin. Psychiatry 2014, 75, e781–e784. [Google Scholar] [CrossRef] [PubMed]
  60. Owen, G. What makes climate change adaptation effective? A systematic review of the literature. Glob. Environ. Change 2020, 62, 102071. [Google Scholar] [CrossRef]
  61. Marmot, M.; Brunner, E. Cohort Profile: The Whitehall II study. Int. J. Epidemiol. 2005, 34, 251–256. [Google Scholar] [CrossRef] [PubMed]
  62. Government of Canada Employment and Social Development. Nursery Labourer in British Columbia|Labour Market Facts and Figures—Job Bank. 2025. Available online: http://www.jobbank.gc.ca/explore_career/job_market_report/summary_occupation_report.xhtml (accessed on 6 July 2025).
  63. Government of Canada Employment and Social Development. General Farm Worker—Livestock in British Columbia|Wages—Job Bank. Available online: http://www.jobbank.gc.ca/explore_career/job_market_report/wage_occupation_report.xhtml (accessed on 5 July 2025).
  64. Carleton, R.N.; Afifi, T.O.; Turner, S.; Taillieu, T.; Duranceau, S.; LeBouthillier, D.M.; Sareen, J.; Ricciardelli, R.; MacPhee, R.S.; Groll, D.; et al. Mental Disorder Symptoms among Public Safety Personnel in Canada. Can. J. Psychiatry 2018, 63, 54–64. [Google Scholar] [CrossRef] [PubMed]
  65. Dodgen, D.; Donato, D.; Kelly, N.; Greca, A.L.; Morganstein, J.; Reser, J.; Ruzek, J.; Schweitzer, S.; Shimamoto, M.M.; Tart, K.T.; et al. Ch. 8: Mental Health and Well-Being. In The Impacts of Climate Change on Human Health in the United States: A Scientific Assessment; U.S. Global Change Research Program: Washington, DC, USA, 2016; pp. 217–246. Available online: https://health2016.globalchange.gov/mental-health-and-well-being (accessed on 31 March 2025).
  66. Tsakonas, K.; Badyal, S.; Takaro, T.; Buse, C.G. Rapid review of the impacts of climate change on the health system workforce and implications for action. J. Clim. Change Health 2024, 19, 100337. [Google Scholar] [CrossRef]
  67. Mouallem, O. The Boom, the Bust, the Darkness: Suicide Rate Soars in Wake of Canada’s Oil Crisis. The Guardian—US. 2015. Available online: https://www.inkl.com/news/the-boom-the-bust-the-darkness-suicide-rate-soars-in-wake-of-canada-s-oil-crisis (accessed on 31 March 2025).
  68. Soutar, C.; Wand, A.P.F. Understanding the Spectrum of Anxiety Responses to Climate Change: A Systematic Review of the Qualitative Literature. Int. J. Environ. Res. Public Health 2022, 19, 990. [Google Scholar] [CrossRef] [PubMed]
  69. Fleming, A.; Dowd, A.-M.; Gaillard, E.; Park, S.; Howden, M. “Climate change is the least of my worries”: Stress limitations on adaptive capacity. Rural. Soc. 2015, 24, 24–41. [Google Scholar] [CrossRef]
  70. Government of Canada Natural Resources. 10 Key Facts on Canada’s Natural Resources—2023. 2024. Available online: https://natural-resources.canada.ca/science-data/data-analysis/10-key-facts-canada-s-natural-resources-2023 (accessed on 28 March 2025).
  71. Huang, Z.; Ren, X. Impact of natural resources, resilient economic growth, and energy consumption on CO2 emissions. Resour. Policy 2024, 90, 104714. [Google Scholar] [CrossRef]
  72. Wang, Q.; Zhang, S.; Li, R. Impact of risk factors on the link between natural resources rents and carbon emissions: Evidence from economic, financial, and political risks. Humanit. Soc. Sci. Commun. 2024, 11, 1. [Google Scholar] [CrossRef]
  73. Hespanhol, L.; Vallio, C.S.; Costa, L.M.; Saragiotto, B.T. Understanding and interpreting confidence and credible intervals around effect estimates. Braz. J. Phys. Ther. 2019, 23, 290–301. [Google Scholar] [CrossRef] [PubMed]
  74. Ai, X.; Han, Z.; Zhang, Q. Extreme weather experience and climate change risk perceptions: The roles of partisanship and climate change cause attribution. Int. J. Disaster Risk Reduct. 2024, 108, 104511. [Google Scholar] [CrossRef]
  75. Health Canada. Health in a Changing Climate 2022. 2022. Available online: https://changingclimate.ca/health-in-a-changing-climate/chapter/4-0/#:~:text=Secondary%20impacts%20of%20climate%20hazards%20(such%20as,lead%20to%20ongoing%20stress%2C%20anxiety%2C%20and%20depression (accessed on 4 July 2025).
  76. Malka, A.; Krosnick, J.A.; Langer, G. The Association of Knowledge with Concern About Global Warming: Trusted Information Sources Shape Public Thinking. Risk Anal. 2009, 29, 633–647. [Google Scholar] [CrossRef] [PubMed]
  77. Hajjar, R.; Kozak, R.A. Exploring public perceptions of forest adaptation strategies in Western Canada: Implications for policy-makers. For. Policy Econ. 2015, 61, 59–69. [Google Scholar] [CrossRef]
  78. Poortinga, W.; Pidgeon, N.F. Exploring the Dimensionality of Trust in Risk Regulation. Risk Anal. 2003, 23, 961–972. [Google Scholar] [CrossRef] [PubMed]
  79. MacKendrick, N.A.; Parkins, J.R. Frameworks for Assessing Community Sustainability: A Synthesis of Current Research in British Columbia; Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre: Edmonton, AB, Canada, 2004.
  80. British Columbia Ministry of Forests, Lands, and Natural Resource Occupations. BC Rural Dividend Program Guide: Second Round Application Intake 2016/17. Available online: https://www2.gov.bc.ca/assets/gov/employment-business-and-economic-development/economic-development/resources/webinars/2016-05-17/rural_dividend_program_may_17_2016.pdf (accessed on 31 March 2025).
  81. Government of British Columbia Ministry of Forests. Bridging to Retirement Program. 2025. Available online: https://www2.gov.bc.ca/gov/content/industry/forestry/supports-for-forestry-workers/retirement-bridging-program (accessed on 31 March 2025).
  82. Government of British Columbia Ministry of Forests. Forest Employment Program. 2025. Available online: https://www2.gov.bc.ca/gov/content/industry/forestry/supports-for-forestry-workers/short-term-forest-employment-program (accessed on 31 March 2025).
  83. Cross, P. The Role of Natural Resources in British Columbia’s Economy. Available online: https://www.fraserinstitute.org/sites/default/files/role-of-natural-resources-in-british-columbias-economy.pdf (accessed on 4 July 2025).
  84. Taris, T.W.; Kessler, S.R.; Kelloway, E.K. Strategies addressing the limitations of cross-sectional designs in occupational health psychology: What they are good for (and what not). Work. Stress 2021, 35, 1–5. [Google Scholar] [CrossRef]
  85. Loftus, S.C. (Ed.) Chapter 3—Data collection: Methods and concerns. In Basic Statistics with R; Academic Press: Sweet Briar, VA, USA, 2022; pp. 17–25. [Google Scholar] [CrossRef]
  86. Ali, S.H.; Foreman, J.; Capasso, A.; Jones, A.M.; Tozan, Y.; DiClemente, R.J. Social media as a recruitment platform for a nationwide online survey of COVID-19 knowledge, beliefs, and practices in the United States: Methodology and feasibility analysis. BMC Med. Res. Methodol. 2020, 20, 116. [Google Scholar] [CrossRef] [PubMed]
  87. Goldman, N.; Willem, T.; Buyx, A.; Zimmermann, B.M. Practical Benefits, Challenges, and Recommendations on Social Media Recruitment: Multi-Stakeholder Interview Study. J. Med. Internet Res. 2023, 25, e44587. [Google Scholar] [CrossRef] [PubMed]
  88. Vesely, S.; Klöckner, C.A. Social Desirability in Environmental Psychology Research: Three Meta-Analyses. Front. Psychol. 2020, 11, 1395. [Google Scholar] [CrossRef] [PubMed]
Table 1. Socio-demographic characteristics of individuals included in the sample based on their perceived effect of climate change on their industry.
Table 1. Socio-demographic characteristics of individuals included in the sample based on their perceived effect of climate change on their industry.
How Likely or Unlikely Do You Think the Industry You’re Working in Will Be Affected by Climate Change?
Total (n = 877)Unsure or Very/Somewhat Unlikely (n = 366)Very/Somewhat Likely (n = 511)p-Value
Perceived likelihood of future impact of climate change on work industries <0.001
Very unlikely209 (23.8) 209 (57.1)0 (0.0)
Somewhat unlikely123 (14.0) 123 (33.6)0 (0.0)
Unsure34 (3.9) 34 (9.3)0 (0.0)
Somewhat likely164 (18.7) 0 (0.0) 164 (32.1)
Very likely347 (39.6) 0 (0.0) 347 (67.9)
Occupation <0.001
Natural Resources118 (13.5)40 (10.9)78 (15.3)
Art and Recreation77 (8.8)22 (6.0)55 (10.8)
Education/Law/Government209 (23.8)77 (21.0)132 (25.8)
Health79 (9.0)29 (7.9)50 (9.8)
Management/Business80 (9.1)49 (13.4)31 (6.1)
Manufacturing31 (3.5)15 (4.1)16 (3.1)
Sales92 (10.5)46 (12.6)46 (9.0)
Science69 (7.9)15 (4.1)54 (10.6)
Trades122 (13.9)73 (19.9)49 (9.6)
Age (years) <0.001
16–24122 (13.9)40 (10.9)82 (16.0)
25–44396 (45.2)146 (39.9)250 (48.9)
45–64267 (30.4)133 (36.3)134 (26.2)
65 years and over92 (10.5)47 (12.8)45 (8.8)
Gender <0.001
Man434 (49.5)218 (59.6)216 (42.3)
Non-binary39 (4.4)14 (3.8)25 (4.9)
Woman404 (46.1)134 (36.6)270 (52.8)
Ethnicity 0.012
White698 (79.6)280 (76.5)418 (81.8)
Chinese28 (3.2)9 (2.5)19 (3.7)
Indigenous42 (4.8)16 (4.4)26 (5.1)
Other94 (10.7)55 (15.0)39 (7.6)
South Asian15 (1.7)6 (1.6)9 (1.8)
Income <0.001
Less than CAD 30,000299 (34.1)101 (27.6)198 (38.7)
CAD 30,000 to CAD 59,999229 (26.1)90 (24.6)139 (27.2)
CAD 60,000 to CAD 89,999172 (19.6)76 (20.8)96 (18.8)
CAD 90,000 or more177 (20.2)99 (27.0)78 (15.3)
Education <0.001
High School or Less112 (12.8)49 (13.4)63 (12.3)
Some Postsecondary Training297 (33.9)152 (41.5)145 (28.4)
Bachelor’s Degree or higher468 (53.4)165 (45.1)303 (59.3)
Political Orientation <0.001
Extremely conservative43 (4.9)39 (10.7)4 (0.8)
Moderately conservative115 (13.1)86 (23.5)29 (5.7)
Slightly conservative54 (6.2)31 (8.5)23 (4.5)
Neither liberal nor conservative177 (20.2)85 (23.2)92 (18.0)
Slightly liberal41 (4.7)13 (3.6)28 (5.5)
Moderately liberal219 (25.0)60 (16.4)159 (31.1)
Extremely liberal228 (26.0)52 (14.2)176 (34.4)
Geographical location 0.275
Rural421 (48.0)187 (51.1)234 (45.8)
Suburban150 (17.1)61 (16.7)89 (17.4)
Urban306 (34.9)118 (32.2)188 (36.8)
Data collection wave 0.045
Wave 1365 (41.6)169 (46.2)196 (38.4)
Wave 2299 (34.1)110 (30.1)189 (37.0)
Wave 3213 (24.3)87 (23.8)126 (24.7)
Frequency of social media use 0.016
Less than 2 h460 (52.5)210 (57.4)250 (48.9)
2 h or more417 (47.5)156 (42.6)261 (51.1)
p-values indicate the results of the X2 and Kruskal–Wallis tests, which assess the distribution of observations and differences among participant groups. X2 tests were used to test differences in categorical variables, and Kruskal–Wallis tests were used for continuous variables.
Table 2. The proportion of the sample that perceives climate impacts as very or somewhat likely on their industry by occupation group.
Table 2. The proportion of the sample that perceives climate impacts as very or somewhat likely on their industry by occupation group.
Unsure or Very/Somewhat UnlikelyVery/Somewhat Likely
Overall0.420.58
Management/Business0.610.39
Trades0.600.40
Sales0.500.50
Manufacturing0.480.52
Education/Law/Government0.370.63
Health0.370.63
Natural Resources0.340.66
Art and Recreation0.290.71
Science0.220.78
Table 3. Multivariable ordinal logistic regression model measuring the association between occupational category and the perceived likelihood of climate change impacting their industry.
Table 3. Multivariable ordinal logistic regression model measuring the association between occupational category and the perceived likelihood of climate change impacting their industry.
aOR *95% CI *
Natural Resources1.00Reference
Science0.890.481.67
Health0.690.391.23
Art and Recreation0.550.310.99
Education/Law/Government0.460.290.72
Manufacturing0.420.200.90
Sales0.390.230.67
Trades0.330.200.55
Management/Business0.270.150.47
* Model is adjusted for age, gender, ethnicity, income, education, political orientation, survey wave, and frequency of social media use.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Bratu, A.; Sharma, A.; Logie, C.H.; Martin, G.; Closson, K.; Gislason, M.K.; Hogg, R.S.; Takaro, T.; Card, K.G. Differences in Perceived Future Impacts of Climate Change on the Workforce Among Residents of British Columbia. Climate 2025, 13, 157. https://doi.org/10.3390/cli13080157

AMA Style

Bratu A, Sharma A, Logie CH, Martin G, Closson K, Gislason MK, Hogg RS, Takaro T, Card KG. Differences in Perceived Future Impacts of Climate Change on the Workforce Among Residents of British Columbia. Climate. 2025; 13(8):157. https://doi.org/10.3390/cli13080157

Chicago/Turabian Style

Bratu, Andreea, Aayush Sharma, Carmen H. Logie, Gina Martin, Kalysha Closson, Maya K. Gislason, Robert S. Hogg, Tim Takaro, and Kiffer G. Card. 2025. "Differences in Perceived Future Impacts of Climate Change on the Workforce Among Residents of British Columbia" Climate 13, no. 8: 157. https://doi.org/10.3390/cli13080157

APA Style

Bratu, A., Sharma, A., Logie, C. H., Martin, G., Closson, K., Gislason, M. K., Hogg, R. S., Takaro, T., & Card, K. G. (2025). Differences in Perceived Future Impacts of Climate Change on the Workforce Among Residents of British Columbia. Climate, 13(8), 157. https://doi.org/10.3390/cli13080157

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop