Navigating climate change: Climate change awareness and strategies in micro, small, and medium-sized enterprises in a developing economy

PLOS One | https://doi.org/10.1371/journal.pone.0327165 July 2, 2025 7 / 26 corporate practice level, we considered awareness of practices such as using sustainable transportation (e.g., electric or hybrid vehicles), analyzing and measuring carbon footprint indicators, engaging in voluntary tree planting and refor- estation, purchasing or issuing green bonds, and producing green energy. At the operational process level, we examined awareness of energy reduction and efficiency programs, environmental policy implementation, networking with clusters and associations focused on CC, adopting hybrid work models to reduce carbon emissions, green planting initiatives, and waste reclassification practices. In addition to assessing strategic awareness, we identified the anticipated timeline for ventures to achieve carbon neutrality. Respondents were asked to choose from several options: within 5 years, between 6 to 10 years, between 11 to 20 years, more than 20 years, not in the company’s interest, deemed infeasible, or unknown. We also evaluated knowl- edge of specific carbon-neutral strategies at both the corporate and operational levels. At the corporate practice level, we considered strategies such as compensating for carbon emissions through reforestation or biodiversity restoration, imple- menting shared transport systems for employees, purchasing green bonds, and establishing “life fences” to enhance bio- diversity. At the operational process level, we focused on practices like conducting environmental diagnostics of corporate

impacts, optimizing the use of raw materials, providing training in environmental and energy management, adopting clean energy sources, and implementing comprehensive waste management systems. To further analyze the factors influencing ventures’ awareness toward carbon neutrality, we incorporated several elements that describe external pressures and normative discourses shaping sustainability-oriented decisions. First, we included a dummy variable to assess whether organizational decisions were made by a gender-balanced executive team (i.e., 50 percent male, 50 percent female at the C-level), assigning a value of 1 if gender-balanced leadership was reported. Additionally, two dummy variables captured perceived pressures from key stakeholders: one indicating whether customer pressures were perceived as relevant or very relevant, and another for investor pressures. Each variable was assigned a value of 1 if the venture perceived these pressures as relevant or very relevant. Furthermore, we complemented our analysis with two dummy variables measuring perceived support from the govern- ment and key stakeholders (employees). A value of 1 was assigned if the venture perceived these sources of support as relevant or very relevant. Additionally, we considered internal constraints by incorporating dummy variables reflecting capi- tal (i.e., financial, human, or time) and technological barriers. Each variable was assigned a value of 1 if the venture identi- fied these barriers as relevant. Lastly, we created three dummy variables to capture ventures’ awareness and engagement

with carbon neutrality initiatives: (1) knowledge of carbon markets, assigned a value of 1 if the venture reported aware- ness of such markets; (2) involvement in carbon-neutral practices, assigned a value of 1 if the venture actively engaged in these practices; and (3) participation in organizational programs related to carbon neutrality, assigned a value of 1 if the venture was involved in such programs. Data analysis Our data analysis consisted of three interrelated steps. First, we employed descriptive analysis techniques to identify com- mon patterns regarding ventures’ awareness of CC and their strategic decisions toward carbon neutrality. Specifically, we categorized ventures into different subsamples based on organizational type and economic sector, focusing on compar- ing means across these subgroups. This descriptive analysis aimed to highlight variations and commonalities to uncover potential awareness gaps and shortcomings that could guide future research and the development of targeted public pol- icies and interventions [27,36,38,53]. Subsequently, for each category of analysis, we conducted a non-parametric Pear- son chi-square (X2) test of homogeneity [54] to assess the distributional similarities within subgroups. This test enabled us to determine whether two or more populations shared similar distributions across a single categorical variable. We per- formed each test using the ‘chisq.test’ function from the ‘stats’ package in R, applied to count-based contingency tables.

For each test we computed the p-value based on the asymptotic chi-squared distribution of the test statistic. The code and data supporting these analyses are publicly accessible via the DOI provided in the Data Availability Statement.