How Media and Climate Perception Affect Residents’ Willingness to Pay for Environmental Protection: Evidence from China

Abstract

While scientific and technological advancements drive societal progress, they have concurrently contributed to environmental pollution and climate change. Given the intrinsic interconnection between communication and environmental studies, this research leverages data from the Chinese General Social Survey (CGSS 2021) as its sample. Employing structural equation modeling (SEM), the study investigates the impact of media usage and perception of climate change issues on willingness to pay (WTP) for environmental protection. This study aims to investigate the interrelationships between media usage, climate perception, and WTP for environmental protection among Chinese residents through innovative model construction and variable selection, seeking to contribute to the enhancement of environmental protection from the perspective of media usage. Results indicate that media usage frequency (MU) positively predicts environmental concern (EC), climate risk perception (CRP), and WTP. Media trustworthiness (MT) positively influences climate impact perception (CIP), EC, and environmental satisfaction (ES). Climate impact perception negatively predicts WTP, while climate risk perception negatively affects ES. Environmental concern positively predicts both ES and WTP, and ES further positively predicts WTP. To enhance public environmental awareness, improve ES, and strengthen WTP for sustainable climate governance, we recommend that media institutions intensify climate risk communication and construct science-based narrative frameworks, while governmental bodies should improve environmental governance systems to elevate public satisfaction.

1. Introduction

Rapid advancements in science and technology have fueled substantial economic growth, leading to a qualitative leap in material living standards and continuous societal transformation. However, these changes have concurrently triggered global climate change. Against this backdrop, relevant conventions and agreements have been successively established: The United Nations Framework Convention on Climate Change (UNFCCC), adopted in 1992, formally established the principle of “common but differentiated responsibilities” in legal form for the first time, laying the cornerstone for global climate governance. The Paris Agreement, adopted at the 2015 Paris Climate Change Conference, stands as a landmark international treaty on climate change, critical to the global response. Currently, climate change constitutes a major global concern, yet global climate action faces multiple challenges, such as funding gaps, barriers to technology transfer, and geopolitical disparities.

China accords high priority to addressing climate change. Since announcing the “Dual Carbon” goals, referring to carbon peak and carbon neutrality) in September 2020, the country has comprehensively implemented the new development philosophy. It upholds modernization featuring harmony between humanity and nature as a defining characteristic of Chinese modernization, advancing initiatives to reduce carbon emissions, curb pollution, expand green spaces, and foster economic growth while resolutely fulfilling its carbon neutrality targets. These efforts necessitate new climate action frameworks. In recent years, China has issued pivotal documents including The Opinions of the Central Committee of the Communist Party of China and the State Council on Comprehensively Promoting the Construction of a Beautiful China, The Opinions of the Central Committee of the Communist Party of China and the State Council on Accelerating the Comprehensive Green Transformation of Economic and Social Development, Key Tasks for Addressing Climate Change (2023–2025), Working Guidance for Carbon Dioxide Peaking and Carbon Neutrality in Full Implementation of the New Development Philosophy, Action Plan for Carbon Dioxide Peaking Before 2030, and Implementation Plan for Science and Technology to Support Carbon Peak and Carbon Neutrality (2022–2030).

Against the intensifying backdrop of global climate change, public awareness of climate issues and willingness to take action constitute a critical social foundation for achieving the dual-carbon goals (carbon peak and carbon neutrality). Numerous previous studies have explored the relationships between two of the three factors—media usage, climate change perception, and willingness to pay for environmental protection—yet few have investigated the interrelationships among all three. Based on our review of extensive literature, we posit that certain interconnections exist between these variables, which motivated the present study. Utilizing data from the 2021 Chinese General Social Survey (CGSS), this study extracts relevant variables to examine the relationships among Media Usage (MU), Media Trustworthiness (MT), Climate Risk Perception (CRP), Climate Impact Perception (CIP), Environmental Concern (EC), Environmental Satisfaction (ES), and willingness to pay for environmental protection (WTP).The findings reveal that: MU positively affects EC, CRP, and WTP; MT positively influences CIP, EC, and ES; CIP negatively affects WTP; CRP negatively affects ES; EC positively influences both ES and WTP; ES positively affects WTP. Based on these findings, we identify pathways to enhance public climate cognition, elevate EC and ES, and strengthen motivation for WTP.

This article is structured as follows: Section 2 reviews the literature, formulates research hypotheses, and establishes the research model. Section 3 introduces the data sources, explains the variables, describes the data analysis process, and presents the descriptive statistics of the data. Section 4 systematically summarizes the findings of the study. Section 5 provides an in-depth analysis of the results and their potential implications. The Section 6 concludes the study, offers insights and suggestions based on the findings, and outlines the research limitations and future directions.

The main contributions of this study can be summarized as follows: First, it examines the relationships among MU, MT, climate perception (including CIP and CRP), EC, and ES from a media perspective. This reveals the significant role of media in environmental engagement and provides actionable guidance for enhancing media’s effectiveness in promoting environmental protection. Second, it investigates how CIP and CRP, as independent variables, influence ES and WTP. These findings elucidate the critical importance of safeguarding planetary climate health. Third, employing a structural equation model (SEM), it disentangles the complex interrelationships among multiple variables. This approach offers a valuable theoretical and methodological reference for subsequent research on climate change, climate cognition, and pro-environmental action.

2. Theoretical Framework, Hypotheses and Model

2.1. Theoretical Framework, Hypotheses

2.1.1. Impact of Media Usage on Climate Perception

Numerous studies have demonstrated that media usage influences public perception of climate issues. As noted by Burksiene and Dvorak, effective communication, particularly digital communication, plays a crucial role in promoting environmental actions [1]. Substantial research demonstrates that media usage shapes public climate cognition. Zhao, applying the reinforcing spirals model, found that media exposure—particularly to scientific content—indirectly promotes engagement with global warming and subsequent information-seeking behaviors by elevating perceived knowledge levels [2]. Rosenthal and Ai’s longitudinal study in Singapore revealed a bidirectional relationship between media usage and climate perception: traditional media consumption strengthens climate awareness, while heightened awareness in turn drives increased media engagement, aligning with reinforcing spirals theory [3]. Ophir et al. corroborated that science-oriented media use correlates with greater climate consensus and policy support [4]. Taddicken’s survey of German internet users indicated weak positive associations between traditional media (TV, radio) usage and both climate knowledge and issue awareness, whereas internet effects were more strongly moderated by individual information needs and media evaluations [5]. Bogert et al.’s meta-analysis further validated these findings, establishing that trust in science and media usage positively predict public acceptance of anthropogenic climate change (ACC), with new media/user-generated content exerting twice the impact of traditional media—effects exhibiting significant cross-national variation [6]. Research has found that watching television and cable TV news can influence public views, knowledge, and behavior regarding climate change [7]. Media framing significantly shapes public cognition of climate issues [8].

Trust in media significantly influences climate change perception. As a critical channel for disseminating climate change information, the credibility of media directly affects the public’s acceptance and depth of understanding of climate-related information [9]. Wang’s research in China emphasized that while climate-related media usage directly enhances policy support willingness, risk perception (particularly toward non-human environments) serves as a critical mediating variable. These findings collectively demonstrate how media type, trust levels, and individual cognitive evaluations jointly shape the complexity of climate perception [10]. Yang further revealed that media trust mitigates perceived living environmental risks [11]. Media’s influence on climate change perception (CIP) is not always positive. Studies indicate that individuals with low trust in media (MT) are more likely to reject scientific evidence of climate change [12].

Based on this foundation and our conceptual framework, we propose the following hypotheses:

H1. 

MU positively predicts CIP.

H2. 

MU positively predicts CRP.

H3. 

MT positively predicts CIP.

H4. 

MT positively predicts CRP.

2.1.2. Impact of Climate Perception on Willingness to Pay for Environmental Protection

Climate perception influences WTP through differentiated pathways. Wang’s empirical study demonstrated that Chinese public perception of non-human environmental risks significantly enhances climate policy support, whereas perception of risks to humans shows no such effect, suggesting ecocentric values may more readily translate into WTP [10]. Junsheng et al. further revealed that climate knowledge and attitudes affect pro-environmental behavior through full mediation effects, indicating a dual-driven mechanism of rational cognition and emotional identification [13]. Loy et al. established that groups with high climate risk perception show stronger support for carbon taxation policies, with WTP mediated by responsibility attribution—willingness significantly increases when individuals attribute climate crises to human activities rather than natural fluctuations [14]. However, this relationship is culturally constrained. Gadzekpo et al. found Ghanaian media practitioners, while acknowledging climate severity, face operational constraints that impede translating cognition into actionable WTP promotion [15].

Based on this foundation, we propose:

H5. 

CIP positively predicts WTP.

H6. 

CRP positively predicts WTP.

2.1.3. Impact of Media Usage on Willingness to Pay for Environmental Protection

Media influence on WTP exhibits platform heterogeneity and generational characteristics. Junsheng et al. found mass media indirectly promotes pro-environmental behavior by enhancing climate awareness and attitudes, particularly in developing economies [13]. Arnot et al. demonstrated that social media strengthens youth self-efficacy in climate action through practical guidance and global peer connections [16]. Experimental evidence from Happer and Philo revealed exposure to BBC climate documentaries increased WTP by 23%, whereas risk-dismissive media (e.g., UK’s Daily Mail) reduced it by 17% [17].

Social media directly mobilizes WTP through emotional engagement. Karimiziarani et al. analyzed 35 million disaster-related tweets, showing acute climate events temporarily boost public WTP for green infrastructure, though effects diminish rapidly post-crisis [18]. Conversely, Rousell et al.’s “digital dramatization” strategy—integrating gamification with action initiatives via climate apps—sustains WTP activation by transforming abstract issues into personal narratives [19].

Based on this empirical foundation, we propose:

H7. 

MU positively predicts WTP.

2.1.4. Interrelationships Among Media, Climate Perception, Environmental Satisfaction, and WTP

Interrelationships exist among media, climate perception, environmental satisfaction, and willingness to pay for environmental protection, which have been extensively studied by numerous scholars in prior research. Rosenthal and Ai’s longitudinal Singaporean model identified a closed loop: traditional media usage → climate perception → environmental satisfaction → further media engagement [3]. Junsheng et al. quantified a triple mediation pathway wherein media usage influences willingness to pay through climate knowledge, attitudes, and environmental satisfaction [13]. HAO et al. found that media use has a significant impact on environmental protection awareness and environmental protection intention of college students, while media use has no significant direct impact on their environmental protection behaviors, but environmental protection intention has a mediating effect between media use and environmental protection behaviors [20]. Media trust significantly influences public environmental concern. When the public holds a higher level of trust in media, they are more inclined to accept and prioritize environmental issues reported by media, such as climate change and water pollution [21]. Media trust exerts a complex influence on environmental satisfaction, moderated by multiple factors including media type, dimensions of trust, environmental regulations, and public cognition [22,23,24].

Based on this empirical synthesis, we propose:

H8. 

MU positively predicts EC.

H9. 

MT positively predicts EC.

H10. 

MT positively predicts ES.

H11. 

EC positively predicts WTP.

H12. 

EC positively predicts ES.

H13. 

CIP positively predicts ES.

H14. 

CRP positively predicts ES.

H15. 

ES positively predicts WTP.

2.2. Model Specification

Based on the proposed research hypotheses, research questions, existing conclusions, and defined variables, the following model is constructed. This model comprises 15 distinct influence pathways, capturing the interrelationships among the seven variables. The structural model is presented in Figure 1.

3. Methodology

3.1. Data Source

This study utilizes data from the Chinese General Social Survey (CGSS), China’s first comprehensive, nationally representative, longitudinal social survey project. The 2021 survey represents the 14th annual wave of CGSS, collecting 8148 valid responses nationwide across approximately 700 variables. This dataset includes metrics pertinent to media usage, climate perception, and environmental protection—the core domains of this research. After excluding invalid responses and incomplete data from the CGSS2021 dataset, the final analytical sample comprises 1943 observations.

3.2. Variable Description

3.2.1. Media Factors

Media Usage (MU): MU was measured by asking respondents: “How frequently did you use the following media in the past year?” covering six media types—newspapers, magazines, radio, television, internet (including mobile), and mobile push notifications—with responses coded on a 5-point scale from 1 (Never) to 5 (Very Frequently), where higher values indicate greater usage frequency.

Media Trustworthiness (MT): MT was assessed through the question: “To what extent do you trust the following institutions?” specifically evaluating news media trust on an 11-point scale from 0 (Distrust completely) to 10 (Trust completely), with ascending values denoting stronger trust.

3.2.2. Climate Perception

Climate Impact Perception (CIP): CIP was measured through two dimensions: specifically, global impacts (“How beneficial or harmful is climate change to the world?”) and domestic impacts (“How beneficial or harmful is climate change to China?”), with responses coded on an 11-point scale from 0 (Very harmful) to 10 (Very beneficial), where higher values reflect greater optimism about climate impacts.

Climate Risk Perception (CRP): CRP was assessed using the question: “To what extent is global temperature rise caused by climate change damaging to the environment?”, recorded on a 5-point scale from 1 (No damage) to 5 (Extremely damaging), with ascending values indicating higher perceived risk severity.

3.2.3. Environmental Concern and Satisfaction

Environmental Concern (EC): EC was measured using the question: “Overall, how concerned are you about environmental issues?” with responses recorded on a 5-point scale from 1 (Not at all concerned) to 5 (Extremely concerned), where higher values indicate stronger environmental concern.

Environmental Satisfaction (ES): ES was evaluated through two governmental dimensions using parallel 5-point ordinal scales. Central government performance was measured by asking: “How would you evaluate the central government’s efforts in addressing China’s environmental issues over the past five years?” while local government performance was assessed via: “How would you evaluate your local government’s efforts in addressing environmental issues in your area over the past five years?” Both dimensions shared identical response options anchored as follows: 1 = “Prioritized economic development while neglecting environmental protection”, 2 = “Insufficient commitment with inadequate environmental investment”, 3 = “Made efforts but achieved limited effectiveness”, 4 = “Significant efforts with measurable results”, and 5 = “Outstanding achievements”, with higher values indicating greater satisfaction.

3.2.4. Willingness to Pay for Environmental Protection (WTP)

WTP was measured through two parallel items assessing payment intent: “To what extent are you willing to pay higher prices for environmental protection?” and “To what extent are you willing to pay higher taxes for environmental protection?” with responses coded on a 5-point scale (1 = Very unwilling; 5 = Very willing) where higher values indicate stronger payment willingness.

3.3. Date Analysis

This study employs Structural Equation Modeling (SEM), a statistical methodology capable of analyzing complex variable relationships, which has been widely applied in environmental science, social science, and other fields to investigate how various factors influence environmental behaviors and public attitudes [25]. In the field of environmental studies, SEM can be utilized to assess complex relationships among factors such as media coverage, public perception, and environmental policies, thereby providing a foundation for formulating more effective environmental protection strategies [26]. Numerous scholars have employed SEM to investigate the relationships between media, climate, and environmental protection. For instance, Wu et al. examined the impact of social media on climate change communication [27]. Park et al. investigated the relationship between urban environmental perception and life satisfaction [28], with the aforementioned studies all conducted based on SEM.

Based on the above, we conclude that Structural Equation Modeling (SEM) is highly suitable for this study. First, SEM simultaneously handles complex relationships between multiple independent and dependent variables, making it suitable for testing the multidimensional pathways proposed in this research (e.g., media usage, media trust, climate perception, environmental attitudes, and willingness to pay). Second, SEM allows the incorporation of latent variables, which are reflected through multiple observed variables, thereby enhancing measurement validity. Finally, the model evaluates the overall goodness-of-fit of the theoretical framework, enabling verification of both direct effects and analysis of mediation effects and indirect pathways, thus comprehensively revealing underlying mechanisms among variables.

This study will empirically validate the model using CGSS2021 data. Aligned with the research framework and hypotheses, and building upon prior scholarly work, we utilized AMOS 26 (Analysis of Moment Structures) for data analysis to derive findings and provide policy recommendations.

3.4. Descriptive Statistics

Descriptive statistics for the aforementioned variables are presented in

Table 1. Descriptive Statistics of Key Variables.

Category	Variable	Code	Measurement Scale	Min	Max	Mean	SD
Demographics	Gender	/	1 = Male 2 = Female				/
	Age	/	2021-birth year	18	94	49.22	17.83
MU	Newspaper	M1	Range: 1–5	1	5	1.67	0.94
	Magazine	M2		1	5	1.66	0.86
	Radio	M3		1	5	1.91	1.09
	Television	M4		1	5	3.38	1.20
	Internet (mobile included)	M5		1	5	3.59	1.60
	Mobile push notifications	M6		1	5	2.14	1.38
MT	Media trustworthiness	/	Range: 1–10	0	10	6.27	2.29
CRP	Climate risk perception	/	Range: 1–5	1	5	3.49	0.85
CIP	Global climate impacts	C1	Range: 1–10	0	10	3.32	2.33
	China climate impacts	C2		0	10	3.41	2.36
ES	Local govt. satisfaction	S1	Range: 1–5	1	5	3.70	0.97
	Central govt. satisfaction	S2		1	5	4.02	0.91
EC	Environmental concern	/	Range: 1–5	1	5	3.62	0.88
WTP	Willingness to pay higher prices	P1	Range: 1–5	1	5	3.30	1.05
	Willingness to pay higher taxes	P2		1	5	3.11	1.09
Valid cases	N = 1943

:

The descriptive statistics indicate distinct patterns: in MU, internet engagement demonstrates the highest frequency while magazine usage shows the lowest, directly reflecting trends in the new media era, with an overall MU mean of 2.39; MT registers a mean of 6.27, indicating generally positive trust in news organizations; CRP averages 3.49, signifying substantial perceived environmental threats; CIP reveals closely aligned means of 3.32 (global) and 3.41 (domestic), confirming predominantly negative assessments; ES shows higher ratings for central government (mean = 4.02) than local authorities (mean = 3.70), though both reflect overall satisfaction; EC averages 3.62, denoting moderate public attention; WTP manifests moderate willingness (price premiums: 3.30, tax increases: 3.11). The sample (N = 1943) spans ages 18–94 (mean = 49.22 years) with 48.7% male and 51.3% female respondents.

4. Results

The model data were analyzed using AMOS 26 to quantify statistical relationships among variables, as presented in Figure 2.

The proposed model comprises 15 hypothesized paths, 11 of which demonstrate statistically significant coefficients. The goodness-of-fit indices for the structural equation model are summarized in

Table 2. Goodness-of-fit Indices of the Research Model.

Index Type	Fit Index	Threshold		Model Value	Assessment
		Acceptable	Good
Absolute Fit	GFI	≥0.7	≥0.9	0.948	Good
	AGFI	≥0.7	≥0.9	0.919	Good
	RMSEA	<0.08	<0.05	0.068	Acceptable
Incremental Fit	NFI	≥0.7	≥0.9	0.877	Acceptable
	CFI	≥0.7	≥0.9	0.888	Acceptable
	TLI	≥0.7	≥0.9	0.847	Acceptable
	IFI	≥0.7	≥0.9	0.888	Acceptable
	RFI	≥0.7	≥0.9	0.833	Acceptable
Parsimonious Fit	PGFI	>0.5	>0.8	0.608	Acceptable
	PNFI	>0.5	>0.8	0.643	Acceptable
	PCFI	>0.5	>0.8	0.651	Acceptable

, indicating an acceptable model-data fit.

4.1. Effects of MU and MT on CRP/CIP and EC

MU significantly and positively predicts CRP (β = 0.105, * p < 0.001), thereby supporting Hypothesis H2. MU significantly and positively predicts EC (β = 0.139, * p < 0.001), which confirms Hypothesis H8. MT significantly and positively influences CIP (β = 0.078, * p < 0.001), thus validating Hypothesis H3. Similarly, MT significantly and positively affects EC (β = 0.131, * p < 0.001), supporting Hypothesis H9. MU shows no significant effect on CIP (* p > 0.05), while MT exhibits no significant impact on CRP (* p > 0.05), leading to rejection of Hypotheses H1 and H4.

4.2. Effects of CRP/CIP and EC on ES and WTP

CIP significantly negatively affects WTP (β = −0.049, * p < 0.05), thereby rejecting Hypothesis H5. However, CIP shows no significant effect on ES (* p > 0.05), rejecting Hypothesis H13. CRP significantly negatively affects ES (β = −0.059, * p < 0.05), which leads to rejection of Hypothesis H14. Conversely, CRP exhibits no significant effect on WTP (* p > 0.05), rejecting Hypothesis H6. EC significantly positively affects both ES (β = 0.138, * p < 0.001) and WTP (β = 0.269, * p < 0.001), thus supporting Hypotheses H12 and H11. Additionally, ES significantly positively predicts WTP (β = 0.130, * p < 0.001), confirming Hypothesis H15.

4.3. Effects of MU and MT on ES and Willingness to WTP

MU significantly positively affects WTP (β = 0.079, * p < 0.005), thereby supporting Hypothesis H7. MT significantly positively affects ES (β = 0.236, * p < 0.001), which confirms Hypothesis H10.

5. Discussion

This study aims to investigate the influence of media usage and climate change perception on WTP for environmental protection. Building upon previous research, we introduced novel variables and applied rigorous analytical methods. Among the 15 pathways examined in the research design, 11 showed statistically significant path coefficients (9 positively significant, consistent with hypotheses; 2 negatively significant, contrary to hypotheses), while the remaining 4 pathways were not statistically significant.

First, MU significantly and positively predicts CRP, meaning that higher frequency of media usage leads to a stronger belief that global temperature rise caused by climate change poses severe environmental hazards. MU also significantly and positively predicts EC, indicating that increased media exposure enhances concern for environmental issues, as coverage of topics such as climate change and water pollution raises public awareness of these challenges [29]. MT significantly and positively predicts CIP. That is, higher levels of trust in media correlate with more positive perceptions of climate change impacts. Conversely, when public trust in media is low, individuals may become skeptical of or even resistant to climate change information reported by media, ultimately influencing their cognitive assessment of climate issues [12]. MT significantly and positively predicts EC. Specifically, higher levels of media trust are associated with greater concern for environmental issues. When the public has greater trust in media, they are more inclined to believe environmental problems reported by media, which in turn facilitates the development of environmental awareness and promotes engagement in environmentally focused behaviors [30]. However, MU has no significant effect on CIP, and MT has no significant effect on CRP.

Second, CIP significantly negatively predicts WTP for environmental protection. That is, the more positively people perceive the impacts of climate change, the lower their willingness to pay. This finding is consistent with the research by Mayer et al., suggesting that people may reduce their willingness to take action when they perceive climate change as unstoppable [31]. However, CIP does not affect ES. CRP significantly negatively affects ES, meaning that the stronger people perceive the environmental hazards caused by climate change-induced global temperature rise to be, the lower their satisfaction with environmental protection. However, CRP has no significant effect on WTP. EC significantly positively affects both ES and WTP, meaning that the higher people’s concern for environmental protection, the greater their satisfaction and willingness to pay. Additionally, ES significantly positively affects WTP, indicating that higher satisfaction with environmental actions leads to stronger willingness to pay.

Third, MU significantly positively affects WTP. Groups with higher frequency of media usage demonstrate stronger WTP for environmental protection, which aligns with findings from numerous scholars indicating that MU significantly influences public WTP through multiple potential pathways. Increased MU, particularly engagement with media focusing on environmental issues, is generally associated with heightened environmental awareness and stronger WTP [29,32]. MT significantly positively affects ES, meaning that higher trust in media correlates with greater satisfaction with environmental protection. Since ES in turn positively influences WTP, this indirectly demonstrates that MT also positively affects WTP. This aligns with the perspective of Lee et al., indicating that public trust in media further influences their environmental protection behaviors [33].

6. Conclusions and Recommendations

In recent years, environmental issues have become critical global concerns. China has consistently strived to address climate change and advance environmental improvement. Based on CGSS2021 data, this study thoroughly investigates the interrelationships among MU, MT, CRP, CIP, EC, ES, and WTP. The results indicate: (1) MU positively predicts CRP; (2) MU positively predicts EC; (3) MU positively predicts WTP; (4) MT positively predicts CIP; (5) MT positively predicts EC; (6) MT positively predicts ES; (7) CIP negatively predicts WTP; (8) EC positively predicts WTP; (9) EC positively predicts ES; (10) CRP negatively predicts ES; (11) ES positively predicts WTP.

Building on these findings, we contend that enhancing Environmental Concern, Environmental Satisfaction, and Willingness to Pay is critical for China’s sustainability goals, necessitating coordinated efforts. Media and government must implement evidence-based interventions. First, regarding media aspects, efforts should focus on strengthening climate risk communication and constructing a science-based narrative framework. Specific approaches include: (1) Optimizing Content Design to Highlight the Authenticity and Urgency of Climate Risks. A progressive “Impact-Attribution-Action” narrative framework should be adopted. Visual tools—such as case studies of extreme weather disasters and comparative charts of historical climate data—can be employed to vividly illustrate the negative consequences of climate change, thereby strengthening public risk perception. Tailored to new media user preferences, lightweight science communication products should be developed, including mini-programs (e.g., carbon footprint calculators) and interactive gamified features (e.g., low-carbon lifestyle challenge campaigns). These initiatives integrate climate knowledge into daily living scenarios. (2) To establish a “Media-Science Institution” collaboration mechanism for the joint release of authoritative reports, experts from meteorology, environmental science, and related fields should be systematically engaged to interpret specialized content. This should be complemented by sustained journalistic follow-up reporting to improve public accessibility of scientific information. Regularly scheduled climate science popularization live streams should be conducted, featuring dedicated expert Q&A sessions. This initiative facilitates deeper public understanding of climate science and reduces cognitive biases stemming from information ambiguity. (3) Cultivating a Credible and Rigorous Image through Transparency in Sources, Communication, and Error Correction. A rigorous information verification mechanism should be established, with news sources publicly disclosed. Specialized environmental knowledge must be communicated using accessible language. Any identified errors in reporting require timely correction statements accompanied by clear explanations of their causes. These practices enhance media credibility and foster public engagement in environmental initiatives.

Next, regarding governmental aspects, the environmental governance system should be improved to enhance public satisfaction. Specific measures include: (1) Strengthening Climate Information Dissemination and Public Trust Mechanisms. This study confirms that MT significantly positively affects CIP, EC, and ES, indicating that enhancing the credibility of official and authoritative media in climate communication is crucial for improving public environmental cognition and policy acceptance. The government could collaborate with research institutions and mainstream media to establish a platform for scientific interpretation and dissemination of climate information. By releasing consensus reports, expert interpretations, and visualized data products, the accuracy, accessibility, and reliability of climate information can be improved. Additionally, a regular communication mechanism should be established to promptly address public concerns, thereby systematically enhancing rational public understanding of climate issues and willingness to engage in environmental actions. (2) Promoting Environmental Governance Transparency and Public Participation Mechanism Design. The results demonstrate that ES significantly positively affects WTP, and MU also exerts positive influences on EC and WTP. Therefore, the government should further expand channels for open environmental information and pathways for public participation. It is recommended to establish an integrated, real-time open environmental data platform covering key indicators such as air quality, water quality monitoring, and carbon emissions, complemented by user-friendly data interpretation modules. Simultaneously, robust mechanisms for public involvement in environmental decision-making and supervision should be developed, such as instituting public consultation procedures for environmental policies and community deliberative meetings on environmental affairs. These measures would enhance the inclusivity of policy formulation and implementation processes while improving feedback responsiveness, thereby strengthening public environmental efficacy and satisfaction, ultimately fostering their support and participation in sharing environmental governance costs.

This study can partially enrich the theoretical development of environmental communication and provide theoretical references for enhancing environmental dissemination, raising public environmental awareness, and increasing willingness to pay for environmental protection in China. Of course, this study has certain limitations. First, the data reflect only macro-level manifestations, while individual variations exist but were not specifically addressed in the research. Furthermore, the media usage component encompasses frequency of using internet, mobile push notifications, newspapers, radio, television, and magazines—including both new and traditional media—yet all were incorporated into the single variable of “media usage” without examining differences across media types. Future research will incorporate these dimensions to strive for more comprehensive investigations. Third, the sample size of this study is relatively small compared to China’s overall population, meaning the results can only reveal correlations between variables rather than definitive causal relationships. Finally, the survey data was collected in 2021, which is already four years ago; thus, the timeliness of the data may be limited. We fully recognize that these issues need to be effectively addressed, and in future research, we will take all of them into consideration to strive for more comprehensive studies.