Crisis-Induced Environmental Action: Examining Youth Environmental Behaviors during Economic Crisis in Pakistan

Description

This dataset arises from a cross-sectional survey conducted to explore how Generation Z's cognition of the post-COVID-19 economic recession influences their Pro-Environmental Behaviors (PEBs) across distinct spheres. The data was collected from respondents aged 18–26, residing in six major cities across Pakistan. The survey instrument measured variables related to emergency relevance, emergency coping, positive and negative environmental affective reactions, and self-reported PEBs in each sphere. The dataset serves as the empirical foundation for testing an integrative model informed by Affective Events Theory, which examines the interplay of emergency cognition and affective reactions in driving environmental behavior during economic crises. The study employed Partial Least Squares Structural Equation Modeling (PLS-SEM) for analysis, offering insights into differential behavioral patterns across the identified spheres and advancing understanding of the challenges and opportunities for environmental action during economic downturns.This resource is valuable for researchers and policymakers interested in behavioral responses to intersecting economic and environmental challenges.

Steps to reproduce

To arrive at our data, we implemented a cross-sectional survey research design targeting Generation Z individuals (aged 18–26) from six major urban centers in Pakistan. A structured questionnaire was designed, grounded in Affective Events Theory and prior literature on Pro-Environmental Behaviors (PEBs). The survey included validated scales to measure emergency relevance, emergency coping, affective reactions (positive and negative), and PEBs across four spheres: personal, public, workplace, and financial. Participants were recruited using purposive sampling, ensuring diversity within the Gen Z demographic. Data collection was conducted both online and in person, adhering to ethical protocols, including informed consent and anonymity. The survey responses were digitized and checked for completeness and accuracy. Data analysis employed Partial Least Squares Structural Equation Modeling (PLS-SEM) using SmartPLS software. The workflow included initial data screening, exploratory factor analysis to confirm measurement validity, and hypothesis testing to evaluate relationships between constructs. The comprehensive methodology ensures replicability and provides a roadmap for similar studies in other contexts.

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