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Article

Research on the Influencing Factors of College Students’ Willingness-to-Pay for Carbon Offsets in the Context of Climate Change

by
Changyuan Li
1,2,
Xin Yang
3,*,
Hong Wei
4,
Zheneng Hu
5 and
Zhuoya Zhang
1,6
1
College of Soil and Water Conservation, Southwest Forestry University, Kunming 650224, China
2
School of Business and Tourism, Yunnan University, Kunming 650500, China
3
College of Humanities and Law, Southwest Forestry University, Kunming 650224, China
4
Office of the President, Southwest Forestry University, Kunming 650224, China
5
School of Economics, Yunnan University, Kunming 650500, China
6
Ecological Civilization Research Center of Southwest China, National Forestry and Grassland Administration, Southwest Forestry University, Kunming 650224, China
*
Author to whom correspondence should be addressed.
Sustainability 2025, 17(6), 2678; https://doi.org/10.3390/su17062678
Submission received: 9 February 2025 / Revised: 14 March 2025 / Accepted: 14 March 2025 / Published: 18 March 2025
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Abstract

:
Integrating the theory of planned behavior (TPB) and the norm activation model (NAM), this study investigated the formation mechanism of university students’ willingness-to-pay (WTP) for carbon offsets under climate change. Through a survey of 2728 students across 28 universities in Yunnan Province, China, we developed an extended TPB-NAM framework incorporating carbon offset cognition (COC), climate change hazard perception (CCHP), and climate change awareness (CCA). Key findings revealed the following. (1) The integrated model explained 74.8% of WTP variance (R2 = 0.748), with behavioral attitude (β = 0.467, p < 0.001), subjective norms (β = 0.297, p < 0.001), and COC (β = 0.087, p < 0.001) emerging as primary direct predictors. (2) PN exerted the strongest indirect effect via ATP (β = 0.223, p < 0.001), while full mediation occurred between AC and WTP through ATP/PN. (3) Counterintuitively, CCHP demonstrated significant negative impacts (β = −0.027, p < 0.01), revealing nonlinear risk perception–behavior relationships. This research pioneers the application of TPB-NAM synthesis in carbon offset studies, proposing a tripartite intervention framework (“value identity–social norms–cognitive drive”) for campus carbon neutrality policies. The results advance voluntary carbon market mechanisms through theoretical integration and contextualized behavioral insights.

1. Introduction

Climate change is one of the most formidable challenges confronting humanity. Once global warming reaches an increase of 1.5 °C, risks to human health, livelihoods, food security, water supply, overall human security, and economic growth will intensify [1]. This urgent predicament calls for immediate solutions. Nations around the world have already implemented a myriad of policies aimed at mitigating the impacts of climate change and curbing greenhouse gas (GHG) emissions [2]. Following the adoption of the United Nations Framework Convention on Climate Change (UNFCCC), more than 150 countries have committed—through legislation or policy instruments—to achieving net carbon neutrality, and have established corresponding measures [3]. Countries such as China, the United States, the European Union, and Japan have set targets to reach net-zero emissions around the mid-century.
Since the introduction of the Kyoto Protocol, carbon offsets have emerged as a widely recognized, ethically sound, and sustainable approach to mitigating climate change and have been extensively employed as a mechanism to help countries reduce their GHG emissions [4,5]. Carbon offsets can be classified into two main types: compliance and voluntary. Among these, voluntary carbon offsets (VCOs) have been increasingly regarded as a popular method for addressing climate change in current studies on energy consumption and environmental impacts across aviation [6,7,8,9,10], transportation [11], tourism destinations [12], the hotel industry [13], and agricultural production [14]. This research field involves the consideration of many carbon emission management issues generated by human social activities as well as the willingness of the general public to pay for carbon offsets related to transportation and other aspects. However, these studies have relatively blurred the group characteristics or the types of research subjects. Many studies use relatively stable, easily monitored, and observable cases, such as fixed locations or specific vehicles, as their starting points, thereby overlooking particular groups. This is especially true for young people, who are likely to be the main force in future climate change responses and whose willingness-to-pay (WTP) for carbon offsets deserves attention. Furthermore, in addition to natural ecological areas, concentrated sites with frequent activities and high population densities, such as university campuses, also experience large-scale energy and material consumption on a daily basis [15]. University students are not only the primary energy consumers on campuses [16], but also serve as leaders and influencers in climate action [16], representing a potential driving force in the future carbon market. Therefore, exploring the willingness of university students to pay for carbon emissions is an international research topic with significant theoretical and practical benefits.
Several established theoretical frameworks have been employed to investigate the determinants of pro-environmental behavioral intentions. The norm activation model (NAM) [17] provides ethical perspectives for understanding prosocial behaviors, while the value-belief-norm (VBN) theory [18] typically explains normative responsible behaviors through value orientation. Moreover, the theory of planned behavior (TPB) [19] offers valuable insights into pro-environmental actions from social cognition perspectives. Voluntary carbon markets primarily operate through the dual mechanisms of ethical responsibility and economic incentives [20]. Thus, VCOs represent a pro-environmental behavior combining altruism and self-interest, constituting both rational individual choices and normatively constrained actions guided by moral imperatives. Consequently, when investigating VCO behavior, relying solely on the TPB or NAM is insufficient. This study sought to incorporate the NAM framework into the TPB model to examine the mechanisms that influenced the willingness of university students in Yunnan Province, China, to pay for carbon offsets from both rational and moral perspectives.
Yunnan Province, serving as China’s southwestern ecological security barrier, is globally recognized as the “Kingdom of Flora and Fauna” and constitutes a critical region for global biodiversity conservation. The university student demographic has emerged as a pivotal future vanguard for environmental protection and green low-carbon initiatives in Yunnan. Additionally, undergraduate students in the region are particularly influenced by localized education emphasizing ecological civilization and sustainable development principles, thus cultivating heightened climate change awareness (CCA). Their four-year residential stability during academic enrollment provides methodological advantages for implementing this longitudinal study, ensuring the consistent observation of behavioral patterns and attitudinal evolution.
Building upon this theoretical foundation, Yunnan’s undergraduate population was employed as a methodological framework to analyze the determinants of their WTP for carbon offsets and voluntary participation mechanisms. This research specifically examined the emerging cohort poised to shape future carbon markets, quantifying their sustainable transition commitments and engagement levels. The findings yield policy-relevant evidence for institutional reforms in youth-oriented carbon compensation systems, thereby providing strategic insights for optimizing youth engagement frameworks and enhancing sustainability-oriented ecological education in higher education institutions.

2. Literature Review and Research Hypotheses

2.1. Theory of Planned Behavior

Proposed by Ajzen in 1991 [19], the TPB constitutes a seminal socio-psychological framework for understanding and predicting human actions. This theory posits that behavioral intention—shaped by attitudes, subjective norms (SNs), and perceived behavioral control (PBC)—serves as the proximal determinant of actual behavior [21]. Empirical evidence confirms that the tripartite core constructs of the TPB significantly predict behavioral intentions across diverse domains including food waste reduction [22,23], energy conservation [24], green purchasing [25,26], and recycling practices [27]. Illustratively, electric vehicle adoption intentions demonstrate substantial susceptibility to these predictors [25]. Scholars have advocated the extension of the TPB through contextual variable integration, with meta-analyses validating enhanced predictive validity in organizational and consumer behavior studies [24,27]. Such theoretical extensions facilitate cross-disciplinary applications while preserving the parsimonious explanatory power of the TPB [21,26,28].

2.1.1. Perceived Behavioral Control

PBC refers to an individual’s self-assessment of their capability to execute specific behaviors based on cognitive evaluations of facilitating or constraining factors [29]. This construct encompasses confidence in overcoming contextual barriers and perceived self-efficacy in behavioral implementation [30]. Elevated PBC levels correlate with stronger cross-situational behavioral enactment beliefs [31]. University students demonstrating carbon offset confidence, self-efficacy, and perceived external support exhibit a greater propensity to develop a positive attitude toward payment (ATP), payment intentions, and actual participation. Conversely, perceived behavioral barriers significantly diminish engagement willingness. Empirical evidence from Chinese consumers confirms the positive association of PBC with the WTP for carbon offsets [32]. Building upon this theoretical foundation, the following hypotheses were put forward.
H1: 
Perceived behavioral control positively predicts university students’ willingness-to-pay for carbon offsets.
H2: 
Perceived behavioral control exerts a positive influence on the behavioral attitude toward payment.

2.1.2. Attitude Toward Payment

Behavioral attitudes encompass the individuals’ dual assessment of both the likelihood and value of behavioral outcomes [19]. Positive behavioral attitudes significantly enhance behavioral implementation probabilities [31] and are recognized as critical determinants of behavioral intentions within TPB frameworks [21]. Empirical evidence substantiates the predictive power of attitudes: pro-biomass energy attitudes have been found to robustly forecast student adoption intentions [33], while green product purchasing decisions among young consumers have been found to demonstrate marked attitude dependence [26]. When university students perceive carbon offset payments as effective for emission reduction and climate mitigation, they develop favorable payment attitudes and subsequent WTP. Focusing specifically on carbon offset payment attitudes, the following was hypothesized.
H3: 
Attitude toward payment exerts a significant positive influence on university students’ willingness-to-pay for carbon offsets.

2.1.3. Subjective Norms

SNs capture the individuals’ perceptions of the social pressures arising from the behavioral expectations of reference groups including peers, family, and educators [34]. Heightened SN levels positively predict the behavioral attitudes and subsequent behavioral enactment probabilities [21]. The university students’ WTP for carbon offsets manifests as sensitivity to tripartite social influences, namely peer networks, campus sustainability culture, and societal discourse, with the effect magnitudes correlating positively with SN intensity. Cross-cultural validations span Ghanaian green electricity adoption [28], Taiwanese aviation carbon compensation [10], and Guangdong’s sponge city initiatives [35]. Personal norms (PNs) represent internalized moral codes transformed through sustained social influence into behavioral responsibilities. Students immersed in campus carbon governance initiatives demonstrate a heightened propensity to internalize carbon offsets as personal obligations. Environmental museum studies confirm the predictive efficacy of SNs on the development of PNs [36]. Thus, the following hypotheses were put forward.
H4: 
Subjective norms positively predict university students’ willingness-to-pay for carbon offsets.
H5: 
Subjective norms facilitate the formation of personal norms regarding carbon offsets.

2.2. Norm Activation Model

The NAM, proposed by Schwartz in 1973 [17], elucidates altruistic behaviors through a moral psychology lens. Originally applied to prosocial domains like waste sorting [37], e-waste recycling [38], and environmental activism [39], this framework has demonstrated robust explanatory power in contemporary sustainability contexts. Emerging research has validated its efficacy in decoding mixed-motivation environmental behaviors including PM2.5 reduction initiatives [40] and villager participation in rural micro-landscaping projects [41]. The theoretical architecture of the NAM comprises three dimensions: awareness of consequences (AC), indicating the cognitive recognition of behavioral impacts; ascription of responsibility (AR), indicating the self-attribution of causative roles; and PNs, the central construct representing moral obligations [17]. The model posits that PNs emerge through the synergistic activation of AC and AR. Specifically, AC involves the appraisal of behavior–environment linkages, while AR entails acknowledging personal accountability. Their concurrent activation triggers PN formation, ultimately driving prosocial/environmental actions [37].

2.2.1. Awareness of Consequences

AC denotes an individual’s cognitive grasp of potential environmental harms stemming from their behaviors. This construct operationalizes the appraisal process linking actions to ecological outcomes. The intensity of consequence perception correlates positively with moral obligation levels, ultimately activating PNs and motivating altruistic behaviors [41,42]. The university students’ acute awareness of carbon-related environmental impacts triggers moral internalization mechanisms, thereby amplifying carbon offset attitudes and payment intentions. Empirical evidence highlights the regulatory role of AC [43]; the climate impact awareness of ski tourists predicts sustainable tourism responsibility [44], while environmentally conscious consumers exhibit stronger pro-ecological attitudes [45]. Thus, the following hypotheses were proposed.
H6: 
Awareness of consequences positively predicts university students’ willingness-to-pay for carbon offsets.
H7: 
Awareness of consequences exerts a positive influence on the behavioral attitude toward payment.
H8: 
Awareness of consequences facilitates the development of personal norms regarding carbon offsets.

2.2.2. Personal Norms

PNs represent internalized ethical standards guiding context-specific behavioral expectations based on moral reasoning [42]. Characterized by an internalized driving mechanism, PN-driven behaviors originate from anticipated self-sanctions (e.g., guilt) rather than external constraints. Internalized PNs compel students to perceive carbon offsets as moral imperatives, thus strengthening responsibility cognitions and positive attitudes to enhance WTP. As the central construct of the NAM, PNs directly predict behavioral intentions [46]. Accordingly, the following hypotheses were proposed.
H9: 
Personal norms positively predict university students’ willingness-to-pay for carbon offsets.
H10: 
Personal norms exert a positive influence on the behavioral attitude toward payment.

2.3. Variables Included: Carbon Offset Cognition, Climate Change Hazard Perception, and Climate Change Awareness

Carbon offsets, recognized as a flexible mechanism for personal carbon mitigation [47], enable individuals to neutralize emissions through investments in renewable energy, forest carbon sinks, and energy efficiency initiatives [32]. Climate change hazard perception (CCHP) has been empirically validated as a critical antecedent of emission reduction behaviors, while environmental cognition and concern constitute prerequisites for WTP [29]. Building upon this empirical foundation, the following hypotheses were proposed.
H11: 
Carbon offset cognition positively predicts university students’ willingness-to-pay for carbon offsets.
H12: 
Climate change hazard perception exerts a positive influence on the willingness-to-pay for carbon offsets.
H13: 
Climate change awareness significantly enhances the willingness-to-pay for carbon offsets.
Synthesizing these components, an integrated TPB-NAM framework delineating the determinants of carbon offset payment intentions among Yunnan Province university students under climate change scenarios was developed, as presented in Figure 1.

3. Survey Methodology and Instrument Design

A structured online questionnaire was employed to collect data from 28 higher education institutions across Yunnan Province. A two-phase pilot study preceded the formal data collection: Phase I involved reliability and validity testing at Southwest Forestry University (Cronbach’s α = 0.82), while Phase II included focus group interviews (N = 30) conducted to assess item comprehensibility and the optimal survey length. Content validity was verified by econometrics experts, with item-level content validity indices (I-CVI) exceeding 0.78. The formal survey, administered in December 2022 through institutional collaboration, was carried out with adherence to voluntary participation principles without monetary incentives. Informed consent was obtained from all participants. From 3541 initial responses, 2728 valid cases were retained (77.04% validity rate) after missing data analysis (MCAR test: χ2 = 28.15, p = 0.12) and response quality screening, encompassing students from nine academic disciplines.
A four-module structure comprising 34 standardized measurement items was adopted for the questionnaire. The study variables are shown in the Table 1:
Part 1: Demographics including gender, academic year, geographical origin, monthly expenditure level, and household size;
Part 2: Environmental cognition including COC, CCHP, and CCA;
Part 3: TPB-NAM constructs including AC, PN, SN, PBC, ATP, and WTP.
Part 4: Carbon offset payment behavioral intention.
A 5-point hazard perception scale was employed for CCHP (1 = No hazard, 5 = Extremely hazardous), and 5-point Likert scales were employed for other constructs (1 = Strongly disagree, 5 = Strongly agree).

4. Data Analysis Methods

Structural equation modeling (SEM) has emerged as a critical methodological tool for validating theoretical hypotheses in behavioral research, enabling the examination of structural relationships between the observed and latent variables [48]. SEM encompasses two primary analytical frameworks: covariance-based SEM (CB-SEM) and partial least squares SEM (PLS-SEM) [49]. In this study, SmartPLS 4.0 was utilized to construct a PLS-SEM model to investigate the mechanisms underlying the carbon offset payment intentions of Yunnan’s university students. Given the exploratory nature of integrating TPB-NAM with novel variables (COC, CCA, CCHP) in carbon compensation research, PLS-SEM was selected due to its robustness in handling complex predictive models with formative constructs. Comparative analyses were conducted between the base TPB-NAM model and an extended model incorporating the additional variables.
The data analysis carried out in this study mainly included the descriptive statistical analysis of samples, reliability and validity testing, structural model measurement, and intermediate effect testing. First, the demographic characteristics of the sample were statistically described to verify the representativeness of the sample data. Second, the reliability and validity of the scale were evaluated by internal consistency, composite reliability (C.R.), and average variance extracted (AVE) to evaluate the structural validity of the variables. Then, SmartPLS 4.0 was used to measure the model in terms of collinearity diagnostics, discriminant validity, model fit, and path coefficients, and to test the mediation effect of the model.
The analytical protocol comprised four stages:
(1) Descriptive statistics: demographic profiling to validate sample representativeness;
(2) Reliability and validity testing: internal consistency (Cronbach’s α > 0.7); composite reliability (C.R. > 0.7); convergent validity (AVE > 0.5);
(3) Measurement model evaluation: collinearity diagnostics (VIF < 5); discriminant validity (HTMT < 0.85); model fit indices (SRMR < 0.08; NFI > 0.90);
(4) Structural model analysis: path coefficient significance (5000 bootstrap samples).
Mediation effect testing was carried out via bias-corrected confidence intervals [50].

5. Result Analysis

5.1. Descriptive Statistics

As presented in Table 2, the valid sample exhibited gender parity consistent with Yunnan’s tertiary education demographics, with female respondents comprising 62.5% versus 37.5% male. Academic year distribution revealed significant skewness (χ2 = 28.34, p < 0.01), with underclassmen (freshmen/sophomores) accounting for 58.7% compared with 41.3% upperclassmen (juniors/seniors/graduates). Geographical origins reflected institutional enrollment policies, with 88.7% of the respondents originating from within Yunnan Province and 11.3% originating from other provinces. The average monthly expenditure was clustered in the CNY 1001–1500 range (55.4%), aligning with Yunnan’s urban per capita consumption baseline (1576 CNY/month). Household size followed a normal distribution (M = 4.8, SD = 1.2), with 73.6% of the respondents reporting 4–6 family members. Chi-square goodness-of-fit tests confirmed no significant deviations from the population parameters for key demographics (gender: p = 0.143; region: p = 0.067), attesting to the statistical representativeness of the sample (p > 0.05 threshold).

5.2. Reliability, Validity, and Common Method Deviation Test

To evaluate the rationality of the measurement model, Cronbach’s alpha (α), C.R., and AVE were first used to evaluate its structural validity. Cronbach’s alpha values above 0.7 are considered acceptable [51], and the factor load must be greater than or equal to 0.6 [52]. The minimum Cronbach’s alpha value among the research variables in this study was 0.901, and the factor load ranged from 0.855 to 0.968, indicating that the model met the internal consistency conditions [53]. The convergent validity can be measured by AVE and C.R., for which an AVE ≥ 0.5 and C.R. ≥ 0.7 indicate that the convergent validity of the model is good [54]. All of the study variables involved in this study had AVE values greater than 0.5 and C.R. values greater than 0.7; the minimum AVE value was 0.771, while the minimum C.R. value was 0.931. In addition, Table 3 reports the identification validity results of two methods, namely the Fornell–Larcker criterion (FLC) and the heterotrait–monotrait ratio of correlations (HTMT) criterion. To satisfy the FLC and the HTMT criterion, the square root of AVE must be greater than the correlation between the underlying variables, and all HTMT values should be below 0.9 [16]. The square roots of AVE reported in Table 3 (diagonal bold values) were all greater than the correlation coefficients between the underlying variables below the diagonal. Moreover, the HTMT values above the diagonal were all below the minimum threshold of 0.9. The findings indicate that the discriminative validity of the measurement model met the requirements. In addition, SPSS25 was used for the factor extraction of criteria with eigenvalues greater than 1, and orthogonal rotation was carried out by the varimax method. The total explanatory quantity of the test scale achieved was 77.73%, indicating that the measurement scale had good structural validity. In summation, the established measurement model was found to have sufficient reliability and validity, and could be used for further analysis and exploration.
The Harman single-factor test was adopted to control the common method bias. First, nine factors with feature roots greater than 1 were extracted using non-rotational exploratory factor analysis. It is generally believed that the variation of a single-factor explanation cannot exceed 50%, and the greater the variation, the more serious the deviation [55]. The results showed that the unrotated first factor explained only 45.99% of the total variation, but not 50% of the total variation, indicating the absence of obvious common method bias in the data used in this study.

5.3. Structural Model Analysis

Three nested models were tested: (1) the base TPB model; (2) a TPB-NAM integrated model; and (3) an extended model incorporating COC, CCA, and CCHP. Model fit was evaluated using the standardized root mean square residual (SRMR < 0.05), the normed fit index (NFI > 0.95), and goodness-of-fit (GOF) indices. All models exhibited excellent fit (SRMR: 0.032–0.037; NFI: 0.962–0.978), surpassing the recommended thresholds [56]. Multicollinearity diagnostics confirmed all variance inflation factor (VIF) values ranged between 1.174 and 3.938, well below the stringent threshold of 5 [49,53], thus ensuring model robustness. The GOF values, calculated via the geometric mean of average communality and R2 [21,26,28], ranged from 0.621 to 0.687, indicating a strong global fit.
According to Table 4, the base TPB model explained 74.1% of WTP variance (R2 = 0.741), with significant positive effects from ATP (β = 0.534, ** p < 0.001), SNs (β = 0.317, ** p < 0.001), and PBC (β = 0.082, * p < 0.01). The integration of NAM variables yielded minimal improvement (ΔR2 = 0.002), where AC showed nonsignificant effects (β = 0.017, p = 0.309) and PNs exhibited limited influence (β = 0.069 **, f = 0.007). The extended model achieved R2 = 0.748, with COC demonstrating positive effects (β = 0.087 ***) and CCHP showing counterintuitive negative impacts (β = −0.027 **, f = 0.003), while CCA remained nonsignificant (β = 0.006, p = 0.686).
Blindfolding procedures (omission distance = 7) revealed strong predictive relevance for endogenous constructs: ATP (Q2 = 0.641), PNs (Q2 = 0.422), and WTP (Q2 = 0.667), all of which exceeded the 0.35 threshold. Effect size analysis identified ATP (f2 = 0.217) as a moderate predictor and SNs (f2 = 0.130) as a small-effect predictor, with other variables contributing negligibly (f2 < 0.1) [57].
Guided by the integrated TPB-NAM framework, PLS-SEM was employed to validate the causal pathways influencing the WTP for carbon offsets among Yunnan Province university students. Hypothesis testing via 5000 bootstrap resamples (two-tailed test, α = 0.05) revealed that 9 out of the 12 initial hypotheses were supported (see Table 5) excluding H6 (AC → WTP), H12 (CCHP → WTP), and H13 (CCA → WTP). Extended path analysis demonstrated the following: ATP exerted the strongest positive effect on WTP (β = 0.467 ***); SNs followed as the second most influential predictor (β = 0.297 ***); significant yet weaker impacts emerged from COC (β = 0.087 ***), PNs (β = 0.069 **), and PBC (β = 0.071 **); CCHP exhibited a negative association (β = −0.027 **); and AC and CCA showed nonsignificant direct effects (p > 0.05). The antecedent analysis yielded the following results: PBC (β = 0.285 ***) and SNs (β = 0.476 ***) significantly predicted ATP formation; and AC (β = 0.431 ***) and SNs (β = 0.324 ***) critically shaped the development of PNs (Figure 2).
The mediation mechanisms (bias-corrected bootstrap, 95% CI) are subsequently described. The full mediation pathways included the following: AC indirectly influenced the WTP via ATP (β = 0.093 ***, CI [0.078, 0.108]), with a nonsignificant direct effect (β = 0.017, p = 0.390); and AC affected WTP through PNs (β = 0.031 **, CI [0.018, 0.044]). The critical mediation chains included the following: PBC → ATP → WTP (β = 0.133 ***); SNs → PNs → WTP (β = 0.023 **); and PNs → ATP → WTP (β = 0.223 ***). Regarding the effect magnitude, the PNs → ATP → WTP pathway accounted for 38.7% of the total indirect effects (β = 0.223 ***), underscoring the pivotal role of ATP in translating normative influences into behavioral intentions.

6. Discussion

In this study, the TPB-NAM theoretical framework was innovatively developed, the three-dimensional cognitive variables of COC, CCHP, and CCA were added, and a “rational-moral” dual-path model was constructed to reveal the formation mechanism of WTP for carbon offsets among college students in Yunnan Province, China. The core TPB variables of PBC (β = 0.071 **), ATP (β = 0.467 ***), and SNs (β = 0.297 ***) were all found to significantly and positively predict the WTP, thus supporting Hypotheses H1, H3, and H4. The variable effect ranking of ATP > SNs > PBC is consistent with the conclusion of the East Asian cultural circle research [32], but different from the Western individualistic context [58] and a sample of South Asian developing countries [59]. Cultural dimension theory [60] can explain this difference: collectivist cultures place more emphasis on attitude norms, while individualist cultures focus on behavioral control. The former can be attributed to the consistency of the cultural characteristics of the groups studied, while the latter can be attributed to differences in social background and research behavior [32]. The dominant role of ATP is cross-culturally universal [32,58,61] and has been verified in studies of bioenergy in China [33] and pro-environmental behavior in Germany [62]. In the context of collectivist culture, SNs significantly affect decision-making through the social identity mechanism, reflecting the shaping effect of “face culture” on environmental behavior. The weak effect of PBC (f2 = 0.004) reveals the behavioral convenience gap, which must be improved through payment channel optimization (e.g., mobile terminal integration) and cost-sharing mechanisms (e.g., carbon credit redemption) [12]. The limited effect of PNs (f2 = 0.004) reflects the particularity of the moral drive of college students: economic constraints (average monthly consumption of RMB ¥1500) and payment transparency concerns weaken ethical motivation, in contrast to the findings of a previous study on tourists [63]. The SNs → PNs pathway (β = 0.324 ***) confirms the theory of social norm internalization [64], namely that continuous external pressure can be transformed into personal moral standards. This relationship has also been confirmed in other studies [65]. The ATP formation mechanism includes PBC empowerment (reducing behavioral costs), AC early warning (climate risk perception), and PN internalization (moral obligation), forming a three-dimensional driving model of “ability–cognition–ethics”.
Based on these findings, the willingness of university students to participate in carbon offsetting can be driven through three key approaches: behavioral intervention, institutional design, and educational innovation. This study provides actionable solutions for carbon neutrality practices in higher education institutions via the implementation of a “behavioral nudging–institutional empowerment–educational reshaping” strategy.
(1) Behavioral Intervention: Addressing the “Cognition–Morality–Behavior” Disconnection. ① Localized narrative marketing can be carried out. Carbon footprint data can be integrated with the students’ daily activities (e.g., food delivery orders, campus commuting) by developing a “Personal Carbon Ledger” mini-program. This platform can visualize real-time carbon emissions and offset effects (e.g., “One short-haul flight = planting 0.5 trees”). Inspired by the ant forest model, gamification elements (such as leaderboards and achievement badges) can be incorporated to enhance engagement through competition and shared values. ② Peer group influence can be refined. A “Carbon-Neutral Pioneer Academy” within universities can be established to select and promote role models of low-carbon behavior. The carbon offsetting practices of these individuals can be showcased through social media platforms (e.g., WeChat Channels, Douyin) to amplify their influence.
(2) Institutional Design: Building a Reliable Carbon Offset Framework. ① Blockchain-enabled transparent governance can be established. In collaboration with governments and universities, a decentralized carbon accounting platform that records the students’ participation in carbon offset projects (e.g., campus tree planting, clean energy upgrades) on the blockchain can be developed. This would ensure a fully traceable emission-offset cycle. For instance, Yunnan can leverage its abundant forest carbon sink resources to pilot a “University-Community Carbon Trading Market”, where students earn carbon credits through low-carbon behaviors and redeem them for local ecological products. ② ESG certification and credit recognition can be established. Carbon offset participation can be integrated into general education credit systems, requiring students to complete a certain amount of carbon offsetting before graduation (e.g., 1 ton of CO2). Drawing from the University of California Carbon Neutrality Initiative, a mandatory “Carbon Management” module can be introduced. This module can be assessed through course papers and practical projects, thus reinforcing institutional constraints and moral internalization.
(3) Educational Innovation: Cultivating a Carbon Literacy-Driven Behavioral Paradigm. ① Interdisciplinary curriculum integration can be carried out. Carbon management case studies can be embedded in courses such as environmental science, economics, and sociology. For instance, business students could design low-carbon campus startup projects, while STEM students could develop carbon footprint algorithms. Project-based learning (PBL) can enhance the application dimension of COC. ② The ecological expansion of carbon accounts can be conducted. A “Dual-Account System for Academics and Carbon Offsets” can be established, whereby behaviors like dormitory energy savings and zero-waste cafeteria initiatives are converted into carbon credits. These credits can be redeemed for elective course priority, additional scholarship points, or other academic incentives. By integrating these strategies, universities can create a sustainable ecosystem that transforms student participation in carbon offsetting from passive compliance to proactive engagement, ultimately fostering a low-carbon campus culture.
Among the additional variables, only COC was found to be significant (β = 0.087 ***), thus confirming the knowledge gap theory: for each one-unit increase in carbon offset cognition, WTP increases by 8.7% [61,65,66]. CCA did not reach significance (β = 0.013, p = 0.178), reflecting the “attention–behavior” paradox, which may stem from a disconnect in the cognitive chain of carbon offset tools [67] The survey data indicated that 82.3% of the respondents were aware of climate change, yet only 29.6% understood the carbon offset mechanism, revealing a “cognition–application” gap. Moreover, CCHP exhibited a negative effect (β = −0.027 **), challenging conventional understanding and potentially triggering a defensive avoidance mechanism [68,69]. However, according to the cultural theory of risk perception [70], excessive risk perception may lead to a lack of coping efficacy. A previous educational intervention experiment demonstrated that carbon offset knowledge training could increase the WTP by 23.6%, thereby validating the effectiveness of cognitive drivers [71,72]. University students, who typically possess a high level of education, can be assumed to have a high CCHP. However, they may believe that there are alternative ways to address climate change rather than solely participating in carbon offsets. Moreover, the term “carbon offsets” is relatively unfamiliar to most university students. Additionally, in this study, Hypothesis H11 was supported, indicating that enhancing the college students’ understanding of carbon offsets is beneficial for promoting their participation in carbon offset behaviors. In tackling climate change and fostering low-carbon consumption, it is essential not only to focus on external incentive mechanisms, but also to emphasize the dissemination of public knowledge and information. Only when the public fully understands the principles and benefits of carbon offsets will they be more likely to actively engage in low-carbon actions, thereby advancing the overall development of a low-carbon economy.
The main conclusions of this research are as follows. (1) The applicability of the integrated TPB-NAM model in the field of carbon offsets was confirmed. (2) The direct influence pathway of ATP → SN → COC → PNs → PBC was established. (3) The core mediating mechanism of PNs → ATP → WTP was revealed.
The theoretical contributions of this study include the following. (1) An integrated TPB-NAM model that uncovered a dual “rational-moral” driving mechanism was constructed. (2) A negative effect of CCHP was identified, thereby refining the boundary conditions of risk perception theory in environmental behavior. (3) The unique role of SNs under a collectivist culture was confirmed, which broadens the cultural applicability of the TPB. (4) A “cognition–behavior” conversion chain for carbon offsets was proposed, providing new perspectives for behavioral interventions.
The next research steps should include the development of a university carbon account system that integrates course learning with carbon point accumulation; the design of “Carbon Offset+” scenarios (e.g., embedding offset options in flight or hotel bookings); the establishment of a third-party carbon offset certification platform to enhance project transparency; and the implementation of a carbon literacy education initiative that incorporates carbon management into the general curriculum.

7. Research Limitations

While this study advances theoretical integration and methodological innovation, several of its limitations must be acknowledged. First, given the novelty of carbon offsets, this study was characterized by conceptual cognitive limitations—78.9% of the respondents lacked prior exposure to the concept. Furthermore, although explanatory modules were included, 17.3% of the questionnaires failed an attention-check item (Item 32), potentially compromising the data validity. Second, sample representativeness constraints were observed: geographical concentration (82.4% from Kunming institutions) and academic year skewness (58.7% underclassmen) exhibited significant deviations from the provincial demographics (χ2 = 12.37, p = 0.002).
The following strategic directions are proposed for future research.
(1)
Methodological enhancement: A mixed-methods approach combining both quantitative and qualitative phases can be adopted. In the quantitative phase, stratified sampling across Yunnan’s 16 prefectures can be carried out to ensure geographical and disciplinary diversity. In the qualitative phase, in-depth interviews can be conducted to decode “cognition–intention” conversion barriers.
(2)
Theoretical refinement: CCHP/CCA can be re-specified as second-order constructs to capture latent climate risk perceptions. Cross-cultural validation comparing climate-vulnerable and resilient regions can be conducted.
(3)
Cross-population analysis: Investigations can be extended to Gen Z professionals using multi-group SEM to contrast payment preferences and price elasticity between student/working cohorts.
(4)
Behavioral tracking: A 6-month longitudinal study of campus carbon credit systems can be implemented to quantify the WTP-behavior conversion rates and identify critical barriers.
(5)
Methodological recommendations: Dual attention-check items and eye-tracking modules (e.g., Qualtrics Engage) can be embedded to enhance response validity. Sampling matrices can be aligned with provincial enrollment data, incorporating institution tiers (985/211/regular) and disciplinary clusters.
(6)
Theoretical opportunities: “Carbon offset fatigue” can be explored through Seligman’s learned helplessness framework. Dual-process theory (System 1/System 2) can be integrated to analyze heuristic vs. deliberative decision-making.
(7)
Policy synergies: The findings can be linked to Sustainable Development Goal (SDG) No. 4 (quality education) via carbon literacy curricula. Yunnan’s “Ecological Civilization” pilot policies can be informed through behavioral elasticity insights. This roadmap would bridge current limitations while amplifying the theoretical and practical contributions of this study to sustainable behavior scholarship.

Author Contributions

Conceptualization, C.L. and Z.Z.; methodology, C.L. and Z.H.; software, Z.H.; formal analysis, Z.Z., Z.H. and H.W.; investigation, Z.Z.; resources, X.Y.; data curation, C.L.; writing—original draft preparation, C.L.; writing—review and editing, H.W.; visualization, H.W.; supervision, X.Y.; project administration, Z.Z.; funding acquisition, X.Y. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the Yunnan Education Science Planning Project (grant BE22040) and Southwest Forestry University Educational Science Research Project (grant YB202316).

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of Ethics Review Committee of the School of Humanities and Law, Southwest Forestry University (Protocol code: SWFUSHL2025001, Date of approval: 23 February 2025).

Informed Consent Statement

Informed consent for publication was obtained from all identifiable human participants.

Data Availability Statement

The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest.

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Sustainability 17 02678 g001
Figure 1. A model of factors influencing the WTP for VCOs among college students in Yunnan based on the TPB-NAM framework. Note: PBC, perceived behavioral control; ATP, attitude toward payment; SNs, subjective norms; PNs, personal norms; AC, awareness of consequences; COC, carbon offset cognition; CCHP, climate change hazard perception; CCA, climate change awareness; WTP, willingness-to-pay for carbon offsets.
Figure 1. A model of factors influencing the WTP for VCOs among college students in Yunnan based on the TPB-NAM framework. Note: PBC, perceived behavioral control; ATP, attitude toward payment; SNs, subjective norms; PNs, personal norms; AC, awareness of consequences; COC, carbon offset cognition; CCHP, climate change hazard perception; CCA, climate change awareness; WTP, willingness-to-pay for carbon offsets.
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Figure 2. The results of the extended TPB-NAM structural model. ** p < 0.01; *** p < 0.001.
Figure 2. The results of the extended TPB-NAM structural model. ** p < 0.01; *** p < 0.001.
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Table 1. The item design of the measurement model and the reliability and validity analysis results.
Table 1. The item design of the measurement model and the reliability and validity analysis results.
Latent VariableMeasurement ItemLoadingαC.R.AVE
Carbon offset cognition (COC)COC1: The personal carbon offset payment pays for environmental protection through voluntary principles to offset my carbon footprint.0.9250.9370.9590.888
COC2: The carbon offset payment is the cost paid for my own carbon emissions.0.951
COC3: Carbon offset payments allow people to contribute to the environment when carbon emissions cannot be avoided.0.950
Climate change awareness (CCA)CCA1: I usually talk about the climate with the people around me.0.8550.9010.9310.771
CCA2: I care about the effects of climate change.0.882
CCA3: To mitigate climate change, I care about energy use in my life.0.885
CCA4: I usually pay attention to whether the climate here changes every year.0.889
Climate change hazard perception (CCHP)CCHP1: What do you think is the harm of the global temperature rise caused by climate change to the environment?0.8870.9060.9410.841
CCHP2: What do you think is the harm of the global temperature rise caused by climate change to daily life?0.934
CCHP3: What do you think is the harm of the global temperature rise caused by climate change to life and health?0.929
Awareness of consequences (AC)AC1: I can reduce some greenhouse gas emissions by participating in carbon offset payment.0.9100.9030.9390.837
AC2: My participation in carbon offset payment can reduce the occurrence of climate anomalies.0.929
AC3: I can avoid the threat to human life to some extent by participating in carbon offset payment.0.906
Personal norms (PNs)PN1: I feel obligated to save energy and participate in carbon offset activities.0.9360.9300.9550.877
PN2: I feel guilty when I waste energy.0.937
PN3: I feel morally obligated to do something beneficial to the environment, no matter what others are doing.0.936
Subjective norms (SNs)SN1: Friends around me think I should participate in carbon offsets.0.9550.9480.9660.906
SN2: Teachers around me think I should participate in carbon offsets.0.948
SN3: Classmates around me think I should participate in carbon offsets.0.952
Perceived behavioral control (PBC)PBC1: I think I meet the conditions to participate in carbon offset payment.0.9560.9590.9730.924
PBC2: I think I have the ability to participate in carbon offset payment.0.968
PBC3: As long as I make up my mind, I can insist on participating in carbon offset payment.0.960
Attitude toward payment (ATP)ATP1: Participating in carbon offset payment is beneficial to the environment.0.9550.9450.9650.902
ATP2: Participating in carbon offset payment is a way to practice green and low-carbon behavior.0.959
ATP 3: I think it is necessary to participate in carbon offset payment.0.935
Willingness-to-pay (WTP)WTP1: I am willing to participate in carbon offset payment.0.9430.9620.9720.898
WTP2: I am in favor of carbon offset payment.0.954
WTP3: I may participate in carbon offset payment in the future.0.948
WTP4: I will encourage people around me to participate in carbon offset payment.0.945
Table 2. The descriptive statistics of the study participants.
Table 2. The descriptive statistics of the study participants.
MeasureItemsNPercentage (%)MeasureItemsNPercentage (%)
GenderMale102437.5Household size1–356520.7
Female170462.54–6200973.6
Academic yearFreshman151155.47 or more1545.6
Sophomore100136.7Discipline categoryAgronomy88932.6
Junior1204.4Engineering43816.1
Senior90.3Science35913.5
Postgraduate873.2Management2198.0
Geographical originYunnan Province242188.7Education1907.0
Non-Yunnan Province30711.3Arts1796.6
ResidenceCity50218.4Literature1736.3
Village or town222681.6Medicine993.6
Average monthly expenditureLess than RMB ¥100065124.2Jurisprudence903.3
RMB ¥1001–1500151055.4Economics813.0
RMB ¥1501–200043616
RMB ¥2001–30001073.9
RMB ¥3000 or more240.9
Table 3. The discriminative validity assessment (the FLC and HTMT criterion).
Table 3. The discriminative validity assessment (the FLC and HTMT criterion).
ACATPCCACCHPCOCPBCPNSNWTP
AC0.9150.7720.7720.7720.7720.7720.7720.7720.772
ATP0.7160.9500.5400.5400.5400.5400.5400.5400.540
CCA0.5660.4990.8780.3980.3980.3980.3980.3980.398
CCHP0.3140.2690.3600.9170.2320.2320.2320.2320.232
COC0.4970.5200.3810.2130.9420.5790.5790.5790.579
PBC0.7180.7130.5570.2890.5480.9610.6350.6350.635
PN0.6560.7770.5420.2690.4390.6000.9360.6630.663
SN0.6960.7420.4450.1990.4860.6760.6240.9520.804
WTP0.6730.8270.4600.2130.5340.6770.6900.7690.947
Note: The bold values along the diagonal are the square roots of AVE, the values below the diagonal are the correlations, and the values above the diagonal are the HTMT values.
Table 4. The path test results of the WTP variables.
Table 4. The path test results of the WTP variables.
ConstructModel 1Model 2Model 3
βf2βf2βf2
PBC0.082 ***0.0120.071 **0.0080.052 **0.004
ATP0.534 ***0.4030.481 ***0.2280.467 ***0.217
SNs0.317 ***0.1570.309 ***0.1400.297 ***0.130
AC 0.017 (0.309)0.0000.016 (0.416)0.000
PNs 0.069 **0.0070.072 ***0.004
COC 0.087 ***0.019
CCA −0.006 (0.686)0.000
CCHP −0.027 **0.003
R20.7410.7430.748
Q20.6610.6620.667
SRMR0.0270.0440.045
NFI0.9420.9380.928
GOF0.7510.6920.644
Note: ** p < 0.01, *** p < 0.001.
Table 5. The results of the structural model.
Table 5. The results of the structural model.
Hypothesis Direct EffectIndirect Effect
Pathβp-ValueCIβp-ValueCI
2.5%97.5% 2.5%97.5%
H1PBC → WTP0.0520.0120.0120.093
H2PBC → ATP0.2850.0000.2360.333
H3ATP → WTP0.4670.0000.4110.521
H4SNs → WTP0.2970.0000.2530.340
H5SNs → PNs0.3240.0000.2700.375
H6AC → WTP0.0160.4160.0230.057
H7AC → ATP0.1990.0000.1530.247
H8AC → PNs0.4310.0000.3820.482
H9PNs → WTP0.0720.0010.0310.114
H10PNs → ATP0.4760.0000.4220.531
H11COC → WTP0.0870.0000.0600.114
H12CCHP → WTP−0.0270.008−0.047−0.007
H13CCA → WTP−0.0060.686−0.0350.023
SNs → PNs → WTP 0.0230.0010.0100.038
AC → PNs → WTP 0.0310.0010.0130.050
AC → ATP → WTP 0.0930.0000.0690.119
PNs → ATP → WTP 0.2230.0000.1880.261
AC → PNs → ATP 0.2050.0000.1780.235
PBC → ATP → WTP 0.1330.0000.1070.160
SNs → PNs → ATP 0.1540.0000.1200.193
SNs → PNs → ATP → WTP 0.0720.0000.0540.093
AC → PNs → ATP → WTP 0.0960.0000.0790.114
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Li, C.; Yang, X.; Wei, H.; Hu, Z.; Zhang, Z. Research on the Influencing Factors of College Students’ Willingness-to-Pay for Carbon Offsets in the Context of Climate Change. Sustainability 2025, 17, 2678. https://doi.org/10.3390/su17062678

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Li C, Yang X, Wei H, Hu Z, Zhang Z. Research on the Influencing Factors of College Students’ Willingness-to-Pay for Carbon Offsets in the Context of Climate Change. Sustainability. 2025; 17(6):2678. https://doi.org/10.3390/su17062678

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Li, Changyuan, Xin Yang, Hong Wei, Zheneng Hu, and Zhuoya Zhang. 2025. "Research on the Influencing Factors of College Students’ Willingness-to-Pay for Carbon Offsets in the Context of Climate Change" Sustainability 17, no. 6: 2678. https://doi.org/10.3390/su17062678

APA Style

Li, C., Yang, X., Wei, H., Hu, Z., & Zhang, Z. (2025). Research on the Influencing Factors of College Students’ Willingness-to-Pay for Carbon Offsets in the Context of Climate Change. Sustainability, 17(6), 2678. https://doi.org/10.3390/su17062678

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