The Impact of Corporate Biodiversity Information Disclosure on Green Investment Confidence and Willingness of Retail Investors in China: The Moderating Roles of Risk Aversion and Climate Risk Awareness

Abstract

The growing emphasis on environmental sustainability and green finance has intensified the need for effective corporate disclosures, particularly regarding biodiversity. Despite the increasing relevance of biodiversity in global investment strategies, there remains a significant research gap in understanding how corporate biodiversity information disclosure influences retail investors, particularly in emerging markets such as China. Based on this, in order to fill this research gap, this study conducts an empirical analysis using valid sample data from 464 retail investors in China and the structural equation modeling method. The results indicate that: (1) Corporate biodiversity information disclosure (CD) has a positive impact on investors’ investment confidence (IC) and investment willingness (IW). (2) Investors’ IC positively influences their IW. (3) Risk aversion (QA) weakens (negatively moderates) the effect of CD on enhancing investors’ IC. (4) QA also weakens (negatively moderates) the effect of CD on promoting investors’ IW. (5) Climate risk awareness (CA) positively moderates the effect of CD on enhancing investors’ IC. (6) CA also positively moderates the effect of CD on promoting investors’ IW. This study enriches relevant theories by emphasizing how psychological factors influence investment behavior and provides important insights for companies, policymakers, and financial intermediaries to promote sustainable investment practices.

1. Introduction

The accelerating deterioration of global ecosystems and the intensifying manifestations of climate change have propelled sustainability to the forefront of financial and corporate decision-making (Iqbal et al., 2025; El Khoury et al., 2025; Aggarwal et al., 2025). The degradation of biodiversity, in particular, threatens not only ecological stability but also economic resilience, supply chain continuity, and long-term shareholder value (Keck et al., 2025; Flammer et al., 2025). Against this backdrop, green investment has emerged as a pivotal mechanism for allocating capital to environmentally responsible enterprises and projects that promote ecological restoration and carbon neutrality (Lin & Xie, 2025).

However, achieving a sustainable transition necessitates more than the mere creation of green financial products—it demands investor confidence grounded in transparent, credible, and comprehensive corporate environmental disclosures (Pokharel, 2025). Among these, biodiversity-related disclosures are gaining critical importance as they reveal firms’ dependencies and impacts on natural ecosystems, a dimension that is increasingly viewed as integral to long-term financial and environmental sustainability (Eluyela et al., 2025).

In recent years, corporate biodiversity disclosure has attracted substantial attention from both academia and industry as an essential component of corporate sustainability reporting (Bassen et al., 2025). With international frameworks such as the Taskforce on Nature-related Financial Disclosures and the Kunming-Montreal Global Biodiversity Framework, corporations are being urged to quantify, disclose, and mitigate their biodiversity impacts (Keckel et al., 2025). Biodiversity disclosure serves as a bridge between corporate ecological accountability and investor decision-making, reducing information asymmetry and enhancing investors’ ability to assess long-term sustainability risks (Toukabri & Toukabri, 2025). Previous research underscores that transparent environmental disclosures improve corporate reputation, lower perceived investment risks, and stimulate capital flows toward sustainable firms (Xi et al., 2025). Nevertheless, much of this scholarship has concentrated on institutional investors or corporate outcomes, leaving the behavioral responses of retail investors—a dominant yet underexamined group—largely unexplored in the context of biodiversity information.

Retail investors play an increasingly influential role in shaping the evolution of green finance (Li et al., 2025). Their decisions can amplify or dampen the diffusion of sustainable investment practices within financial markets (Aruga, 2025). However, retail investors typically face informational constraints, limited financial literacy, and behavioral biases that make them more susceptible to affective and cognitive factors than to purely rational assessment (Zhang et al., 2025). Understanding how biodiversity disclosure influences their green investment confidence and willingness is therefore essential for expanding green finance participation at the grassroots level (Ma et al., 2025). This is particularly relevant in China, where retail investors account for a large share of stock market activity and where government-led initiatives such as the “Beautiful China 2035” strategy and the “National Biodiversity Conservation Strategy and Action Plan” underscore the integration of ecological protection into economic governance (Su & Lee, 2025). Despite these national priorities, the psychological and perceptual mechanisms shaping Chinese retail investors’ reactions to corporate biodiversity information remain insufficiently understood.

Existing literature on ESG disclosure and investor behavior provides important foundations yet reveals a notable research gap. Most studies focus on carbon disclosure or environmental performance metrics, often overlooking biodiversity as a discrete dimension of ecological reporting (Del Gesso & Lodhi, 2025; Castilla-Polo et al., 2025). Furthermore, while institutional investors have been shown to invest in firms with robust environmental transparency (Yu et al., 2018; Ameli et al., 2020), the micro-level behavioral mechanisms driving retail investors’ green investment intentions remain ambiguous. Even less is known about how individual psychological traits—particularly risk aversion and climate risk awareness—moderate these relationships. Given that retail investors often exhibit heterogeneous risk preferences and varying sensitivity to environmental risks, these psychological variables may alter the way biodiversity disclosures are interpreted and translated into investment behavior. Thus, a critical empirical question arises: Do biodiversity disclosures strengthen retail investors’ confidence and willingness to invest in green assets, and how are these effects conditioned by their individual risk profiles and awareness of climate-related risks?

Risk aversion is a core psychological characteristic that reflects individuals’ tendency to avoid uncertainty and potential financial loss (Ananda et al., 2024). In the field of green investment, where returns are often long-term and project risks are perceived as relatively high, investors’ risk attitudes significantly influence how they process and respond to corporate environmental information (Özçelik & Kurt, 2024). Although biodiversity disclosure is generally designed to enhance corporate transparency and strengthen investors’ confidence by signaling responsible management and long-term sustainability, highly risk-averse investors may interpret such information with caution (Essiz et al., 2023). They are likely to focus on the potential risks, costs, and uncertainties implied by biodiversity protection activities—such as compliance pressures, ecological liabilities, or unstable policy environments—thereby perceiving greater investment risk (Kyaw et al., 2022). Recognizing this weakening effect helps explain the heterogeneous behavioral responses among retail investors and provides a deeper understanding of the psychological boundaries that may limit the effectiveness of biodiversity information in promoting green investment behavior.

Similarly, climate risk awareness is defined as the extent to which individuals recognize, understand, and are concerned about the potential environmental and societal consequences of climate change (Song & Yang, 2025). Retail investors with higher climate risk awareness are more likely to recognize the interconnection between biodiversity loss and climate change, and therefore, might be more attuned to the value of biodiversity-related disclosures when assessing corporate sustainability (Bassen et al., 2019; Scanlan, 2021). These investors may perceive biodiversity disclosures not only as a commitment to ecological preservation but also as an indicator of a company’s ability to adapt to climate-related regulatory changes, thus enhancing their confidence in green investments and willingness (Luo & Tang, 2021; Nafisa et al., 2023).

Thus, the inclusion of these psychological moderators—risk aversion and climate risk awareness—is essential to understanding how biodiversity-related information is processed by retail investors and subsequently influences their investment decisions. These psychological traits provide a deeper, micro-level understanding of how information about corporate environmental performance translates into investor confidence and willingness to invest. Testing these moderating effects is not only novel but also necessary to uncover the mechanisms by which behavioral finance and sustainability disclosure intersect, particularly in emerging markets like China. This research is positioned to fill a critical gap by incorporating these moderators into the broader discourse on green investing, enhancing both the theoretical understanding of investment behavior and the practical insights needed to design more effective green finance strategies for retail investors.

Theoretically, this research contributes to the behavioral finance and sustainability disclosure literature by expanding the conceptual boundary of green investment behavior to include biodiversity-specific information and psychological moderating factors. It advances current knowledge by integrating ecological disclosure into the behavioral determinants of retail investment, thereby bridging the gap between environmental attentions and investor psychology. Moreover, it offers an empirically grounded framework that connects biodiversity disclosure to green financial behavior within emerging markets—a context that has been underrepresented in prior international research. In doing so, this study not only responds to global policy calls for mainstreaming biodiversity in financial decision-making but also extends the theoretical understanding of how ecological transparency fosters sustainable market transformation.

From a practical and managerial perspective, the findings of this research will yield actionable implications for corporations, policymakers, and financial intermediaries. For corporations, understanding how biodiversity disclosure shapes investor confidence can inform more effective communication strategies and strengthen stakeholder trust. For policymakers and regulators, the study provides evidence-based insights into how psychological factors influence retail investor participation in green finance, thereby supporting the design of policies that enhance environmental literacy and investment inclusivity. Financial institutions can also leverage these insights to design tailored investment products and educational initiatives that resonate with retail investors’ risk perceptions and environmental values. Ultimately, by unveiling the behavioral and psychological mechanisms linking biodiversity disclosure to green investment, this study contributes to the creation of a more transparent, resilient, and sustainable financial ecosystem in China and beyond.

The structure of the rest of the paper is as follows: Section 2 discusses the literature review and research hypotheses; Section 3 introduces the methodology; Section 4 presents the empirical analysis; Section 5 is the summary and discussion; and the final section covers the research limitations.

2. Literature Review and Research Hypotheses

2.1. Green Investment

Green investment is a funding approach aimed at promoting sustainable development, emphasizing the balance of economic, social, and environmental benefits. It encompasses areas such as environmental protection, clean energy, and the circular economy (Khalid et al., 2025). The core objective is to achieve long-term value through strategies such as pollution prevention and resource conservation, gradually evolving into a term that includes diverse directions such as environmental protection investment and social responsibility investment (Wu et al., 2025). Practices include the development of clean energy, the application of energy-saving and emission-reduction technologies, and innovation in financial products like green bonds, covering industrial sectors such as renewable energy, green buildings, and ecological agriculture (Mei & Zhang, 2025). Figure 1 illustrates the key characteristics of green investment.

2.2. Corporate Biodiversity Information Disclosure (CD), Investment Confidence (IC) and Investment Willingness (IW)

Corporate biodiversity information disclosure has become a critical aspect of environmental, social, and governance (ESG) reporting as global awareness of environmental sustainability intensifies (Bassen et al., 2025). Companies are increasingly incorporating biodiversity-related information in their annual reports or dedicated sustainability disclosures (Moses & Yahaya, 2024). These disclosures highlight not only the firm’s approach to managing and protecting biodiversity but also its strategies to address ecological challenges, such as habitat destruction, species extinction, and resource depletion (Mair et al., 2024). Through such detailed disclosures, companies provide investors with essential insights into their environmental practices, which can directly influence investors’ decision-making, particularly in the context of green investments (Ali et al., 2024).

From the perspective of signaling theory (Connelly et al., 2011), the act of disclosing biodiversity-related information is a key strategy to reduce uncertainty among investors. According to signaling theory, firms that provide clear and transparent information signal their commitment to responsible environmental practices, thereby enhancing their credibility in the eyes of investors (Komara et al., 2020; Connelly et al., 2025). For retail investors, who typically face higher levels of uncertainty and risk aversion, such disclosures serve as signals of the company’s environmental stewardship and risk management capabilities (Baldenius & Meng, 2010). By offering detailed insights into biodiversity management practices, companies demonstrate their proactive approach to minimizing future environmental risks (Alsos & Ljunggren, 2017; Senanayake et al., 2024). This, in turn, reassures investors that the company is effectively addressing potential ecological threats, making it a more attractive investment opportunity. Investors are more likely to view firms with strong, transparent biodiversity disclosures as stable, long-term players in their respective industries, which increases their overall investment confidence and willingness (Garel et al., 2024; Elliot et al., 2024).

Additionally, agency theory (Meckling & Jensen, 1976; Shapiro, 2005) provides further insight into the dynamics between information disclosure and investment willingness. In situations of information asymmetry, where investors may not have access to the detailed internal operations of a company, the provision of detailed biodiversity disclosures serves to bridge this gap (Muhammad & Ali, 2025). Agency theory suggests that information asymmetry between the firm and its investors often leads to higher agency costs, as investors are uncertain about the firm’s ability to manage long-term environmental risks (M. A. Khan et al., 2025). When companies disclose their biodiversity protection strategies, they signal transparency and reduce the risk perceived by investors. This lowered perception of risk directly influences investment willingness, as investors are more likely to allocate their capital toward firms that demonstrate an ability to manage both environmental and financial risks effectively (Yahaya, 2025; Shahwan, 2025). Clear and reliable biodiversity disclosures help mitigate concerns about potential environmental liabilities or future regulatory challenges, which are important factors influencing retail investors’ confidence and willingness to invest (Winingshe, 2025; Pervez et al., 2025).

Based on the above analysis, the following hypothesis is proposed:

Hypothesis 1:

Corporate biodiversity information disclosure (CD) has a positive effect on investment confidence (IC).

Hypothesis 2:

Corporate biodiversity information disclosure (CD) has a positive effect on investment willingness (IW).

2.3. Investment Confidence (IC) and Investment Willingness (IW)

The relationship between investment confidence and investment willingness has been widely recognized in the field of behavioral finance (Sharma et al., 2024). Confidence is a fundamental psychological determinant of individual investment behavior, influencing how investors perceive, evaluate, and act upon financial opportunities (Gede Nyoman Yetna et al., 2022). According to the investment decision process framework, investors are more likely to translate their intentions into actual investment actions when they possess a strong sense of confidence regarding the stability, profitability, and reliability of the investment target (Pervez et al., 2025). In the context of corporate sustainability, when investors hold high confidence in a company’s ability to manage environmental and biodiversity-related risks, they are more inclined to convert this confidence into concrete investment behavior.

Confidence functions as a cognitive and emotional facilitator that reduces perceived uncertainty in investment decisions (Forbes & Kara, 2010). Behavioral finance literature suggests that investment decisions are not purely rational but are also shaped by psychological factors such as trust, perception of control, and emotional assurance (Hoffmann & Post, 2016). Higher levels of confidence lead investors to perceive lower risk and higher potential return, thereby strengthening their willingness to act on investment opportunities (Aren & Nayman Hamamci, 2023). This mechanism is particularly pronounced in green investment contexts, where investors often face elevated uncertainty due to long-term environmental challenges, evolving regulatory frameworks, and the difficulty of assessing sustainability performance (H. Khan & Upadhayaya, 2020). When investors are confident that a company can effectively manage biodiversity and environmental risks, they are more likely to believe that the firm’s long-term value and resilience are secure, which increases their willingness to invest (Zhang et al., 2025).

Moreover, empirical studies in behavioral economics and investment psychology consistently demonstrate that confidence plays a pivotal role in bridging the gap between investment intention and investment action (Aspara, 2013). Confidence enhances the perceived controllability of investment outcomes and reduces hesitation arising from risk aversion (Ye & Yuan, 2008). This is especially relevant to retail investors, who tend to rely on subjective judgments and emotional cues when assessing sustainable or green investment opportunities. When such investors feel confident about their evaluation of an environmentally responsible company, they are more likely to exhibit a stronger willingness to invest (Yao & Rabbani, 2021).

Based on this theoretical reasoning, the following hypothesis is proposed:

Hypothesis 3:

Investment confidence (IC) has a positive effect on investment willingness (IW).

2.4. The Moderating Effect of Risk Aversion (QA)

Risk aversion plays a pivotal role in shaping investor behavior, especially in the context of sustainable or green investments, where returns are often uncertain or subject to volatility (Cohn et al., 1975; Aren & Nayman Hamamci, 2020). Drawing on prospect theory (Levy, 1992; Barberis, 2013), which posits that individuals exhibit a stronger sensitivity to potential losses than to equivalent gains, risk-averse investors are generally more inclined to avoid investments they perceive as risky or uncertain. In this light, while corporate biodiversity information disclosure may signal a company’s commitment to environmental stewardship, it may simultaneously introduce concerns about the environmental and regulatory risks that could influence the firm’s future performance (Bohnett et al., 2022). For risk-averse individuals, the disclosure of biodiversity initiatives, despite its positive implications for corporate responsibility, may highlight potential vulnerabilities related to environmental factors, such as the introduction of new regulations, market shifts, or the firm’s susceptibility to environmental degradation.

Moreover, even though corporate biodiversity information disclosure might offer a positive signal regarding the company’s environmental sustainability efforts, risk-averse investors could perceive this disclosure as insufficient to offset the perceived risks associated with broader environmental and regulatory uncertainties (Velte, 2023). In particular, such investors may weigh the potential for adverse regulatory changes or unpredictable environmental consequences more heavily than the signal of corporate responsibility conveyed through biodiversity initiatives (Hambali & Adhariani, 2024). Consequently, the perceived risks may outweigh the benefits, and these investors might hesitate to invest in firms that prominently disclose their biodiversity-related efforts (Dutta & Dutta, 2024; Tian & Chen, 2025). Therefore, it is plausible that risk aversion would weaken the positive relationship between corporate biodiversity information disclosure and investment confidence, as risk-averse individuals are likely to focus on potential downsides, including long-term financial implications arising from environmental risks. This leads to the following hypothesis:

Hypothesis 4:

Risk aversion negatively moderates the relationship between corporate biodiversity information disclosure (CD) and investment confidence (IC), such that the positive effect of biodiversity disclosure on investor confidence is weakened for risk-averse investors.

In a similar vein, the relationship between corporate biodiversity information disclosure and investment willingness may also be attenuated by the presence of risk aversion. Investment willingness reflects the degree to which an investor is inclined to engage in or commit capital to a given investment opportunity (Eyraud et al., 2013). For risk-averse investors, despite the potential positive signaling of corporate biodiversity efforts, the perceived risks related to environmental volatility and regulatory uncertainties may overshadow the potential rewards of such investments (Lizarazo, 2013). Thus, risk-averse investors may be less willing to engage in investments, even those that are environmentally sound, due to the higher perceived exposure to unpredictable environmental risks. The decision to invest is not solely guided by the attractiveness of sustainability efforts but is significantly influenced by the investor’s perception of risk and the potential for financial loss (Cohn et al., 1975; Chan et al., 2020).

As such, risk-averse investors may exhibit diminished investment willingness when faced with Corporate Biodiversity Information Disclosure, despite the firm’s efforts to position itself as environmentally responsible. The perceived risks, including environmental uncertainties, regulatory complexities, and the potential for negative market reactions, could undermine the investor’s willingness to invest. Hence, the following hypothesis is proposed:

Hypothesis 5:

Risk aversion negatively moderates the relationship between corporate biodiversity information disclosure (CD) and investment willingness (IW), such that the positive effect of biodiversity disclosure on investment willingness is weakened for risk-averse investors.

2.5. The Moderating Effect of Climate Risk Awareness (CA)

Climate risk awareness has increasingly emerged as a critical determinant in shaping the investment behavior of both individual and institutional investors (T. M. Lee et al., 2015). As the awareness of climate-related financial risks intensifies, investors are more likely to interpret corporate biodiversity information disclosure as a key indicator of a company’s proactive stance in addressing climate risks, including regulatory shifts, evolving consumer preferences, and the growing environmental threats posed by climate change. According to environmental risk framework (Sinclair-Desgagne & Gozlan, 2003), investors with a heightened awareness of climate risk tend to favor companies that exhibit clear strategies for mitigating these risks, believing that such companies are better equipped for sustainable long-term success. In this context, climate risk awareness is expected to enhance the influence of biodiversity disclosures on investment confidence. Specifically, investors with higher levels of CA are more inclined to view these disclosures as a reflection of a company’s comprehensive approach to both environmental sustainability and climate resilience, which in turn bolsters their confidence in the company’s future prospects. Therefore, we propose the following hypothesis:

Hypothesis 6:

Climate risk awareness (CA) positively moderates the relationship between corporate biodiversity information disclosure (CD) and investment confidence (IC).

In addition to its effect on investment confidence, climate risk awareness also plays a pivotal role in shaping investment willingness (Kurowski et al., 2025). Investors who are acutely aware of the broader environmental and climate-related risks tend to prioritize investments in companies that are actively addressing these challenges (Li & Tian, 2024). Corporate biodiversity information disclosure serves as a tangible indicator of a firm’s commitment to environmental sustainability, making it particularly appealing to investors with strong climate risk awareness (Mirón Sanguino et al., 2025; Wasiuzzaman & Hj Ahmad, 2025). These investors are more likely to perceive biodiversity disclosures as a signal that a company is prepared for the implications of climate change, regulatory shifts, and evolving market trends. As such, they view investments in companies that demonstrate robust biodiversity management as less risky and more aligned with the trajectory of future regulatory and environmental developments. Thus, we propose the following hypothesis:

Hypothesis 7:

Climate risk awareness (CA) positively moderates the relationship between corporate biodiversity information disclosure (CD) and investment willingness (IW).

In summary, the model proposed in this study is illustrated in Figure 2, which includes five research variables based on the discussions and hypotheses mentioned earlier.

3. Methodology

3.1. Research Scale Development

This study is grounded in previous research and utilizes scientifically validated scales, with necessary adaptations and modifications to ensure alignment with the specific objectives of the investigation.

To achieve this, this research has implemented several methodological strategies to ensure the scale’s scientific rigor and precision. Initially, an extensive review of the literature was conducted, focusing on the role of corporate biodiversity information disclosure (CD) in shaping investor behavior. This literature review provided a foundational understanding of the key variables and their interrelationships, forming the basis for scale development. Subsequently, a systematic evaluation of existing scales related to investment confidence (IC), investment willingness (IW), risk aversion (QA), and climate risk awareness (CA) was carried out. Enhancements and adjustments to these scales were made, ensuring their relevance and accuracy in capturing the nuances of these constructs in the context of Chinese retail investors.

To further refine the scale, this research organized expert interviews and focus group discussions involving professionals from academia, industry practitioners, and experts in the fields of corporate sustainability, environmental economics, and behavioral finance. These discussions provided valuable insights into how investors perceive and respond to biodiversity-related disclosures, as well as the influence of climate risk awareness and risk aversion on investment decision-making. The feedback from these experts was instrumental in improving the scale’s content validity, ensuring that the research variables are captured in a manner that is both relevant and precise.

In addition to content refinement, the translation process was carefully managed to maintain the scale’s accuracy. Following the forward-backward translation methodology outlined by Sousa and Rojjanasrirat (2011), this research employed a professional translation team to translate the scale from English into Chinese and then back into English. This process ensured that the final version of the scale retained its conceptual integrity and cultural relevance, which is crucial given the study’s focus on Chinese retail investors.

Pre-testing of the research scale was another critical step in ensuring its reliability and validity. This research conducted a pilot test with a random sample of 100 participants, who provided feedback on the clarity and relevance of the measurement items. The data collected from this pre-test were subjected to reliability and validity tests, which led to the refinement of measurement items. Any items that did not meet the predefined reliability and validity thresholds were discarded, and adjustments were made based on participant feedback and the observations derived from the research process.

The scientific rigor and validity of the scale are further discussed in the empirical analysis section. The final measurement items for these five various constructs are presented in

Table 1. Measurement items.

Variables	Measurement Items	References
Corporate Biodiversity Information Disclosure (CD)	CD1: I believe the company provides detailed and specific disclosures regarding its biodiversity-related activities in its annual report or sustainability report.	Moses and Yahaya (2024); Tao (2025)
	CD2: I feel that the biodiversity information disclosed by the company includes quantifiable data (such as impact measurement metrics, geographic locations, affected species, etc.).
	CD3: I believe the company’s disclosures regarding biodiversity are transparent and can be verified by external stakeholders (for example, through references to third-party assessments, independent audits, or publicly available data).
Investment Confidence (IC)	IC1: Based on the biodiversity information disclosed by the company, I have confidence in its future environmental performance improvements.	Zhang et al. (2025); Mishra et al. (2025)
	IC2: I believe the company’s disclosures regarding biodiversity management indicate that its long-term operational risks are manageable, thereby increasing my confidence in the safety of my investment.
	IC3: After seeing the company’s disclosures on biodiversity, I am more confident that it can achieve stable green investment returns in the future.
Investment Willingness (IW)	IW1: If other conditions are the same, I would prefer to invest in companies that provide comprehensive disclosures of biodiversity information.	Mishra et al. (2025); Hemdan and Zhang (2025)
	IW2: Within my existing green investment portfolio, I am willing to increase the proportion of holdings in companies that have good disclosures related to biodiversity.
	IW3: I am willing to make green investments in companies that actively disclose biodiversity information.
Risk Aversion (QA)	QA1: Compared to seeking potentially higher but uncertain returns, I prefer to choose investments with lower but more certain returns.	Hunjra and Rehman (2016); Aumeboonsuke and Caplanova (2023)
	QA2: I believe I am a risk-averse person.
	QA3: When making investments, I dislike taking risks.
Climate Risk Awareness (CA)	CA1: I think that climate change will have profound effects on society, the economy, and human well-being.	Y. J. Lee et al. (2019); Todaro et al. (2021)
	CA2: I am aware that climate change may exacerbate the scarcity of natural resources (such as water and land).
	CA3: I have a certain understanding of the environmental risks posed by climate change (such as rising sea levels and biodiversity loss).

. To capture the degree of agreement with the items, a seven-point Likert scale was used, where respondents rated their agreement from 1 (strongly disagree) to 7 (strongly agree). This approach ensures that the data collected reflect the participants’ attitudes and perceptions with high precision, facilitating robust analysis of the relationships between the study’s key variables.

3.2. Respondent Recruitment and Data Collection

This research adopted a rigorous and independently managed respondent recruitment and data collection process to ensure the reliability, validity, and representativeness of the sample. The study focused on Chinese retail investors, who play a crucial role in the expansion of green financial markets and the transition toward sustainable investment behavior.

This study independently recruited participants following a structured and transparent screening procedure designed to ensure data quality and conceptual alignment with the research objectives. To guarantee that respondents were suitably qualified and capable of providing informed and reliable responses, several inclusion criteria were established. First, participants were required to be active retail investors with at least two years of continuous experience in green or sustainability-oriented investment activities within China’s capital market. This ensured that respondents possessed not only investment experience but also specific exposure to green finance instruments or companies with environmental and biodiversity-related practices.

Second, to assess the participants’ familiarity with corporate biodiversity information disclosure and related sustainability communication, a two-step screening process was employed prior to inclusion in the formal survey. In the first step, potential respondents were asked to complete a brief knowledge assessment, which consisted of five multiple-choice questions designed to test their understanding of biodiversity disclosure concepts, such as the meaning of biodiversity conservation reporting, the distinction between biodiversity and carbon disclosures, and the typical channels through which such information is released by corporations. Only participants who correctly answered at least three of the five questions were qualified for the subsequent step. In the second step, respondents were asked to rate their self-perceived familiarity with biodiversity and sustainability disclosures on a 7-point Likert scale (1 = not familiar at all, 7 = highly familiar). Those scoring below 3 were excluded from the study.

Finally, participants were required to demonstrate that they made independent investment decisions—that is, they relied primarily on personal judgment, publicly available information, and company disclosures, rather than institutional or third-party investment advisors. This requirement ensured that participants’ responses reflected individual perceptions and decision-making patterns rather than institutional influence. Retail investors who failed to meet any of the above criteria were excluded from the sample to reduce response bias, enhance the reliability of the data, and strengthen the robustness of the study’s conclusions.

In addition, respondents were asked to select one company from the broader capital market with which they had personal investment experience or interest. This approach allowed participants to choose a company based on their own portfolios or familiarity with its environmental practices, rather than being restricted to a predefined list.

However, to ensure the accuracy and integrity of the data, the selected companies underwent a validation process prior to inclusion in the study. After respondents submitted their company choices, the research team cross-checked these companies to confirm that they had recently disclosed relevant biodiversity information (such as biodiversity-related metrics or conservation efforts). Only those companies that met the disclosure criteria and were actively reporting on biodiversity in a clear and verifiable manner were included in the study. This verification process ensured that respondents’ evaluations were based on actual, up-to-date corporate disclosures, reducing potential biases from outdated or inaccurate information. Consequently, only companies meeting these criteria were considered valid for further analysis, thereby strengthening the overall robustness of the study’s findings.

Participants were recruited primarily through offline investor seminars, financial literacy workshops, and local investment discussion groups, as well as postings on reputable financial community boards that cater specifically to verified individual investors. This recruitment method ensured voluntary participation and authenticity of investor identity, while minimizing sampling bias associated with anonymous online surveys or email-based recruitment.

This study further ensured the geographical and socioeconomic diversity of the sample by selecting respondents from 5 different provinces and municipalities across China, including Beijing, Shanghai, Guangdong, Anhui, and Jiangsu. These regions represent diverse economic structures, financial development levels, and exposure to environmental policies, thus ensuring that the data captured a wide range of investor perspectives. The multi-regional design enhances the generalizability of the findings and mitigates potential bias.

To reduce common method bias (CMB) and strengthen causal inference, the research adopted a three-wave time-lagged data collection design, following the methodological recommendations of Podsakoff et al. (2003). Data were collected at three different time points, approximately six weeks apart. In the first wave, participants assessed their perceptions of corporate biodiversity information disclosure (CD). The second wave focused on risk aversion (QA) and climate risk awareness (CA), and the third wave measured investment confidence (IC) and investment willingness (IW). This temporal separation of variables minimizes respondents’ tendency to provide consistent or socially desirable answers across different constructs.

The questionnaire was administered in Chinese, the official language of the country, to ensure semantic accuracy and participant comprehension. To maintain confidentiality and data integrity, completed questionnaires were collected in sealed envelopes and coded anonymously. Appendix A shows the details of the final and official English version of the survey questionnaire.

This careful and transparent recruitment and data collection strategy ensured that all respondents met stringent inclusion criteria, thereby minimizing sampling and measurement bias. The methodological rigor of this approach enhances the credibility, robustness, and external validity of the findings.

Figure 3 shows the data collection process. In the initial stage, a total of 500 participants took part in the survey. After excluding 16 questionnaires with excessive missing data and poor quality, 484 valid questionnaires were retained, resulting in a valid response rate of 96.800%. In the second stage, questionnaires were sent again to the 484 respondents who completed valid questionnaires in the first stage, and 472 valid questionnaires were collected, yielding a valid response rate of 97.521%. In the third stage, the survey was distributed again to the 472 respondents, resulting in the collection of 464 valid questionnaires, with a valid response rate of 98.305%. In total, 464 valid questionnaires were collected after three rounds of surveys.

Table 2. Information of 464 respondents.

Categories	Options	Frequency	Percentage (%)
Gender	Female	232	50.000%
	Male	232	50.000%
Marriage	Singles and others	221	47.629%
	Married	243	52.371%
Area	Urban	260	56.034%
	Rural	204	43.966%
Location	Beijing city	95	20.474%
	Shanghai city	93	20.043%
	Guangdong province	90	19.397%
	Anhui province	91	19.612%
	Jiangsu province	95	20.474%
Age	20–30	120	25.862%
	31–40	99	21.336%
	41–50	94	20.259%
	51–60	85	18.319%
	>60	66	14.224%
Education level	Junior college and below	157	33.836%
	Undergraduate	179	38.578%
	Postgraduate and above	128	27.586%
Average monthly income (CNY)	<8000	117	25.216%
	8000–10,000	120	25.862%
	10,001–12,000	135	29.095%
	>12,000	92	19.828%

presents the statistical data that describes the respondents. Moreover, ethical guidelines were strictly adhered to throughout the process of recruiting participants and collecting samples, which ensured the protection of the respondents’ rights and the confidentiality of their personal information during the survey. Additionally, informed consent was obtained from all participants before they took part in the study, with assurances regarding the anonymity and confidentiality of their responses, along with a clear explanation that the research was carried out solely for academic purposes.

4. Empirical Analysis

4.1. Common Method Bias (CMB) Test

This study employed two strategies to evaluate the likelihood of CMB within the research framework. First, for the statistical analysis, the Harman single-factor test was utilized, in addition to conducting an exploratory factor analysis of all measurement items through SPSS software. The results obtained after reducing dimensionality indicated that the first unrotated factor explained 31.867% of the variance, which falls short of the 50% threshold (Podsakoff et al., 2003). As a result, it can be concluded that CMB does not exist.

Next, following the research conducted by Mossholder et al. (1998), a comprehensive assessment for CMB was carried out utilizing the confirmatory factor analysis (CFA) comparison technique. First, all measurement items were integrated into a single-factor framework referred to as Model 1 (single-factor model). Subsequently, the original relevant variables and measurement items grounded in theory were employed as Model 2 (multi-factor model). The evaluation involved comparing the variations in degrees of freedom and chi-square statistics between the two models. The analysis revealed (see

Table 3. CMB test result.

Model	χ2	Df	∆χ2	∆Df	p
Single-factor	3654.848	90	3556.615	10	0.000
Multi-factor	98.233	80

) that with ∆χ² = 3556.615, ∆Df = 10, and p = 0.000, which is less than 0.05, it was further confirmed that no CMB was present.

4.2. Exploratory Factor Analysis

An exploratory factor analysis was conducted using SPSS 27.0 software, employing the principal component analysis method on the scale utilized in this study. The factor analysis produced a Kaiser–Meyer–Olkin (KMO) coefficient of 0.808, which exceeds the acceptable minimum of 0.7. Furthermore, the results from the Bartlett Test of Sphericity indicated a value of 5578.866, with 105 degrees of freedom (df) and a significance level of 0.000, well below the 0.05 threshold. These results imply that the data is suitable for performing exploratory factor analysis (Tucker & MacCallum, 1997; Cudeck, 2000).

Regarding the quantity of elements detected through the gravel diagram, a maximum variance rotation method was applied to ascertain the eigenvalues of these elements, using a threshold above 1. Following the extraction of five elements and the removal of measurement items that exhibited cross-loadings across various factors, the distinct measurement indicators were categorized based on their related factors. The outcomes of the exploratory factor analysis are presented in

Table 4. Exploratory factor analysis results.

Variables	Items	Loading	Eigenvalues	Explain the Variation Amount/%	Explain the Cumulative Variation Amount/%	Cronbach’s α
Investment Confidence (IC)	IC1	0.903	2.695	17.967	17.967	0.940
	IC2	0.897
	IC3	0.922
Investment Willingness (IW)	IW1	0.881	2.688	17.918	35.885	0.950
	IW2	0.902
	IW3	0.919
Risk Aversion (QA)	QA1	0.928	2.612	17.411	53.296	0.922
	QA2	0.932
	QA3	0.919
Climate Risk Awareness (CA)	CA1	0.917	2.592	17.277	70.573	0.915
	CA2	0.928
	CA3	0.912
Corporate Biodiversity Information Disclosure (CD)	CD1	0.826	2.320	15.468	86.042	0.845
	CD2	0.873
	CD3	0.877

, revealing that every measurement item has factor loadings greater than 0.600, and all Cronbach’s α values are above 0.700. These results suggest that the exploratory factor analysis conducted in this research yielded a favorable analytical result.

4.3. Confirmatory Factor Analysis

Based on the research sample, five distinct first-order structural models were developed using AMOS 23.0 software. A comparison of these models aimed to identify the most appropriate factor structure. As indicated in

Table 5. Confirmatory factor analysis results.

Fit Indicators	c2	df	c2/df	RMSEA	CFI	IFI	TLI	GFI	AGFI	SRMR
Five-factor model	98.233	80	1.228	0.022	0.997	0.997	0.996	0.973	0.959	0.020
Four-factor model	701.749	84	8.354	0.126	0.889	0.889	0.861	0.828	0.755	0.105
Three-factor model	1668.922	87	19.183	0.198	0.715	0.716	0.656	0.681	0.561	0.133
Two-factor model	2701.154	89	30.350	0.252	0.529	0.530	0.444	0.579	0.432	0.181
One-factor model	3654.848	90	40.609	0.292	0.357	0.359	0.250	0.507	0.342	0.215

Note: Five-factor model: CD, IC, IW, QA, CA; Four-factor model: CD + IC, IW, QA, CA; Three-factor model: CD + IC + IW, QA, CA; Two-factor model: CD + IC + IW + QA, CA; One-factor model: CD + IC + IW + QA + CA.

, the five-factor model displayed the optimal fit (χ²/df = 1.228, CFI = 0.997, TLI = 0.996, IFI = 0.997, GFI = 0.973, AGFI = 0.959, SRMR = 0.020, RMSEA = 0.022). This particular model validates that the five unique first-order factors analyzed exhibit substantial discriminant validity and represent distinct constructs. Thus, employing a five-factor model in this study aligns with the research objectives.

4.4. Model Fit Test

The evaluation results of the model fit reveal that the values for χ²/DF are 2.216; GFI is 0.976; AGFI is 0.954; CFI reaches 0.992; TLI is at 0.987; RMSEA measures 0.051; and SRMR stands at 0.022. Each of these fit indices meets the necessary threshold criteria, with many attaining an excellent classification (Bentler & Bonett, 1980; Bagozzi & Yi, 1988; Malhotra et al., 2014). Therefore, it can be concluded that the research model utilized in this study exhibits a strong fit.

4.5. Measurement Model Analysis

Table 6. Scale reliability and validity test results.

Variables	Items	Unstd.	S.E.	Z	p	Std.	Cronbach’s α	AVE	CR
Corporate Biodiversity Information Disclosure (CD)	CD1	1.000				0.687	0.845	0.654	0.849
	CD2	1.377	0.089	15.547	***	0.860
	CD3	1.471	0.095	15.558	***	0.866
Investment Confidence (IC)	IC1	1.000				0.912	0.940	0.842	0.941
	IC2	1.105	0.035	31.414	***	0.910
	IC3	1.073	0.033	32.967	***	0.930
Investment Willingness (IW)	IW1	1.000				0.919	0.950	0.865	0.951
	IW2	1.004	0.028	35.896	***	0.940
	IW3	0.933	0.027	34.944	***	0.931
Risk Aversion (QA)	QA1	1.000				0.902	0.922	0.801	0.923
	QA2	1.049	0.037	28.528	***	0.912
	QA3	1.081	0.041	26.463	***	0.870
Climate Risk Awareness (CA)	CA1	1.000				0.888	0.915	0.789	0.918
	CA2	1.194	0.045	26.658	***	0.900
	CA3	1.180	0.046	25.674	***	0.876

Note: *** indicates a p-value less than 0.01.

presents the results from the evaluations of reliability and validity pertaining to the research scale, indicating that the Cronbach’s alpha for each dimension is greater than 0.7, which demonstrates a high level of reliability (Thanasegaran, 2009). Additionally, the composite reliability (CR) for every dimension also exceeded the 0.7 threshold (Raubenheimer, 2004), and the average variance extracted (AVE) for each dimension was higher than the minimum standard of 0.5 (Fornell & Larcker, 1981). Therefore, it can be inferred that the research scale exhibits strong validity. Moreover, the square root of the AVE for all variables was greater than the correlation coefficients among those variables (refer to

Table 7. Distinctive validity test results.

	CA	QA	IW	IC	CD
CA	0.888
QA	−0.013	0.895
IW	0.257	−0.170	0.930
IC	0.159	−0.162	0.517	0.917
CD	−0.027	0.052	0.338	0.272	0.809

Note: Values in bold are the AVE open root value.

), indicating that the scale shows substantial discriminant validity (Zaiţ & Bertea, 2011).

4.6. Structural Model Analysis

Table 8. Direct effect test results.

Hypothesis	Path	Unstd. (β)	S.E.	Z	p-Value	Std.	Test Results
H1	CD → IC	0.414	0.079	5.241	***	0.272	Validated
H2	CD → IW	0.334	0.073	4.552	***	0.213	Validated
H3	IC → IW	0.472	0.047	10.117	***	0.459	Validated

Note: *** indicates a p-value less than 0.01.

displays the hypotheses of the research along with the results from the path analysis. The results indicate that each of the direct impact hypotheses proposed in this study shows significant positive relationships, as the Z-value for every pathway is greater than 1.96 and the p-value is less than 0.01. Therefore, all the direct impact hypotheses in this research have been validated.

4.7. Moderation Effects Analysis

This study utilized SPSS 27.0 software and the process 4.0 plugin to examine the moderating effects. The test results are shown in

Table 9. Moderation effects test.

Dependent Variable	Independent Variable	Unstd.	SE	T	p	LLCI	ULCI
The moderation effect of QA
IC	CD	0.307	0.053	5.809	0.000	0.203	0.410
	QA	−0.156	0.045	−3.455	0.001	−0.244	−0.067
	CD × QA	−0.140	0.041	−3.386	0.001	−0.222	−0.059
IW	CD	0.348	0.049	7.109	0.000	0.252	0.445
	QA	−0.164	0.042	−3.912	0.000	−0.246	−0.081
	CD × QA	−0.122	0.039	−3.158	0.002	−0.197	−0.046
The moderation effect of CA
IC	CD	0.312	0.051	6.083	0.000	0.211	0.413
	CA	0.145	0.043	3.346	0.001	0.060	0.230
	CD × CA	0.256	0.042	6.137	0.000	0.174	0.338
IW	CD	0.357	0.046	7.717	0.000	0.266	0.448
	CA	0.224	0.039	5.735	0.000	0.147	0.300
	CD × CA	0.263	0.038	7.006	0.000	0.189	0.337

. For the moderating effect of risk aversion (QA), the unstandardized value of the interaction between CD and QA is −0.140, with a p-value less than 0.05. Additionally, the 95% confidence interval does not include 0 (LLCI: −0.222; ULCI: −0.059). Therefore, it can be inferred that QA suppresses the effect of CD on IC, confirming research hypothesis 4. Similarly, when the outcome variable is IW, the unstandardized value of the interaction between CD and QA is −0.122, with a p-value less than 0.05. The 95% confidence interval also does not include 0 (LLCI: −0.197; ULCI: −0.046), allowing us to infer that QA suppresses the effect of CD on IW, thus confirming research hypothesis 5.

Similarly, based on the aforementioned testing criteria, the results also indicate that CA positively moderates the effect of CD on IC, confirming research hypothesis 6. Additionally, CA positively moderates the effect of CD on IW, confirming research hypothesis 7.

4.8. Model Robustness Test (Cross-Validation)

Based on the study of Cudeck and Browne (1983), this study employed the group comparison capability of Amos to randomly partition the sample into two distinct groups, followed by cross-validation to evaluate the robustness of the findings.

The findings presented in

Table 10. Cross-validation test results.

Model	ΔDF	ΔCMIN	p	ΔNFI	ΔIFI	ΔRFI	ΔTLI	ΔCFI
Measurement weights	6.000	3.052	0.802	0.001	0.001	−0.002	−0.002	0.000
Structural weights	3.000	10.118	0.018	0.003	0.003	0.002	0.002	0.002
Structural covariances	1.000	0.046	0.829	0.000	0.000	0.000	−0.001	−0.001
Structural residuals	2.000	1.886	0.390	0.001	0.001	0.000	0.000	0.000
Measurement residuals	9.000	5.217	0.815	0.001	0.002	−0.002	−0.002	−0.001

reveal that all comparable settings are acceptable (p > 0.05), except for the structural weights (p = 0.018, which is below 0.05), fulfilling the standards outlined by Collier (2020). Additionally, the results indicate that ΔCFI ≤ |0.01| and ΔTLI ≤ |0.05|, which align with the criteria suggested by Little (1997) and Cheung and Rensvold (2002) for indicating no significant difference between the two models when p < 0.05. Consequently, the model satisfies the requirements for convergent validity and showcases stability.

5. Summary and Discussion

5.1. Theoretical Contributions

This study makes several significant theoretical contributions that enhance our understanding of the intersection between corporate biodiversity information disclosure, investor behavior, and environmental sustainability, particularly in the context of Chinese retail investors. These contributions are multi-dimensional, addressing key gaps in existing literature and offering novel insights into the mechanisms that shape investment decisions in the context of green investments.

First, extending signaling theory in the context of biodiversity disclosures. While signaling theory has been widely applied to general corporate disclosures, particularly in the areas of financial performance and ESG practices, this study extends its application to biodiversity-related information disclosures. The research demonstrates that corporate biodiversity disclosure not only signals a company’s commitment to environmental sustainability but also serves as an effective tool for reducing investor uncertainty and enhancing perceived corporate credibility. This finding addresses a critical gap in the literature by providing empirical evidence that biodiversity disclosure, as a specific dimension of ESG reporting, can positively influence investor confidence. By applying signaling theory to the environmental dimension of corporate disclosures, this study contributes to the broader theoretical understanding of how firms use environmental signals to build trust and mitigate informational asymmetries, particularly in markets where investors are more prone to uncertainty, such as in China.

Second, behavioral finance and the psychological mechanisms driving investment willingness. The second key theoretical contribution lies in integrating behavioral finance theories with environmental disclosures. While traditional finance theories focus on rational decision-making, behavioral finance emphasizes the psychological factors that influence investment decisions. By showing that investment confidence promotes investment willingness in the biodiversity disclosures background, this study adds to the body of work that examines how environmental information can affect not only cognitive assessments of risk but also the emotional and psychological factors influencing investor decisions. Specifically, the finding that investor confidence—shaped by perceptions of a company’s environmental stewardship—can translate into increased investment willingness provides a new lens for understanding how individual investors, especially retail investors, engage with green investments. This contribution builds upon existing investment decision models and highlights the role of psychological factors, such as trust and perceived control, in determining investment behavior in the green investment domain.

Third, clarifying the role of risk aversion in green investment decisions. A particularly innovative aspect of this study is its exploration of the moderating role of risk aversion, which offers new insights into the dynamics of sustainable investing. While risk aversion has been widely studied in traditional investment contexts, its interaction with green or sustainable investments, especially in the context of biodiversity disclosures, remains underexplored. This research shows that risk-averse investors may view biodiversity disclosures as insufficient to offset the perceived risks of environmental uncertainties, regulatory changes, and long-term financial volatility associated with sustainability efforts. By highlighting the negative moderating effect of risk aversion on both investment confidence and willingness, this study deepens our understanding of how investor risk preferences can attenuate the positive effects of corporate disclosures. It challenges the conventional assumption that environmental information will always lead to increased investor confidence and willingness to invest, suggesting that investor risk profiles must be taken into account when designing corporate sustainability communications.

Fourth, the role of climate risk awareness as a moderator in green investments. Another novel theoretical contribution of this study is the inclusion of climate risk awareness as a moderating variable that enhances the relationship between corporate biodiversity disclosure and investor behavior. While previous research has addressed the role of general environmental awareness in investment decisions, this study makes a significant advance by focusing specifically on climate risk awareness and its influence on green investment decisions. The study shows that investors with higher levels of climate risk awareness are more likely to interpret biodiversity disclosures as a signal of a company’s preparedness to manage both environmental and regulatory risks. This, in turn, strengthens their investment confidence and willingness. By incorporating climate risk awareness into the analysis, the study enriches our understanding of how investor knowledge of climate risks shapes the evaluation of corporate sustainability efforts. This contribution is especially relevant given the growing importance of climate-related financial disclosures in global markets, and it underscores the need for companies to align their biodiversity-related reporting with broader climate risk narratives to effectively engage environmentally conscious investors.

Fifth, theoretical synthesis of multiple frameworks: signaling, agency, and behavioral finance theories. In addition to its specific contributions, this study also offers a theoretical synthesis by integrating multiple established frameworks, including signaling theory, agency theory, and behavioral finance. By combining these perspectives, the study provides a comprehensive understanding of how corporate biodiversity disclosures function as both a signal of corporate responsibility (signaling theory) and a means of reducing agency costs associated with information asymmetry (agency theory), while also accounting for the psychological and emotional drivers of investment behavior (behavioral finance). This multi-theoretical approach enhances the robustness of the study’s conclusions and contributes to a more holistic understanding of the factors that drive green investment behavior, especially in the context of emerging markets like China.

Sixth, empirical insights into the Chinese investment context. This study makes a valuable contribution by focusing on the Chinese market, which has been largely underrepresented in the literature on green investments and biodiversity disclosures. By using data from 464 Chinese retail investors, this study provides empirical insights into the specific mechanisms that drive green investment decisions in a rapidly developing economy with unique environmental, regulatory, and cultural contexts. This contextual contribution is crucial for the broader understanding of how sustainability information influences investment behavior in different institutional and cultural settings, adding a unique dimension to the global conversation on sustainable finance.

5.2. Practical and Managerial Implications

This study offers valuable insights into the practical and managerial implications for corporations, investors, and policymakers involved in sustainable finance and biodiversity governance, particularly in the context of Chinese retail investors (see Figure 4). By empirically establishing the positive effects of corporate biodiversity information disclosure on investment confidence and willingness, with moderating effects from risk aversion and climate risk awareness, the research highlights both strategic opportunities and challenges for firms seeking to engage investors in the green economy.

From a corporate management perspective, the findings emphasize the increasing importance of biodiversity disclosure as a key strategy to enhancing investor trust and attracting capital. In an environment where biodiversity loss is emerging as a critical financial risk, companies can use transparent biodiversity reporting to signal both their environmental responsibility and long-term business viability. By providing clear, data-backed disclosures—such as the firm’s impact on biodiversity and its strategies for mitigating environmental risks—companies can effectively reduce investor uncertainty and increase their appeal to sustainability-conscious investors. Therefore, companies should prioritize developing robust, standardized biodiversity reporting frameworks that provide measurable outcomes and align with best practices. This will ensure that their biodiversity efforts are not only visible but also credible and impactful.

In terms of investor communication, this study emphasizes the importance of tailoring biodiversity disclosures to meet the psychological needs of different investor groups. The research shows that investment confidence plays a central role in shaping investment decisions, suggesting that firms need to go beyond basic sustainability reporting. In particular, they should focus on both cognitive and emotional aspects of their communication strategies. Using clear, investor-friendly formats—such as performance indicators or third-party certifications—companies can make their biodiversity actions more tangible and engaging. By adopting such a holistic communication approach, firms can strengthen investor confidence and foster greater investment willingness, enhancing the alignment between business strategy and investor expectations.

A particularly noteworthy finding is the moderating role of risk aversion, which suggests that highly risk-averse investors may be less responsive to biodiversity disclosures. This highlights the need for companies to complement their environmental communication with risk management strategies that address investor concerns. For example, firms might include detailed explanations of how they mitigate financial or regulatory risks associated with biodiversity initiatives, or they might offer financial instruments such as green bonds with guarantees tied to biodiversity performance. By addressing these concerns, companies can enhance the appeal of their environmental efforts, particularly among more conservative investors.

This study also highlights the role of climate risk awareness in enhancing the positive effects of biodiversity disclosures. This suggests that investors with higher levels of climate risk awareness are more likely to view biodiversity disclosures as indicators of a firm’s resilience to climate change. Therefore, educational efforts aimed at increasing investor awareness of the interconnectedness between biodiversity loss and climate risks could play a significant role in facilitating green investments. In this context, financial institutions, regulators, and corporations can collaborate to promote environmental literacy, ensuring that investors are equipped to make informed decisions based on a comprehensive understanding of both climate and biodiversity risks.

From a regulatory perspective, this study suggests that integrating biodiversity disclosure into existing ESG reporting frameworks could offer significant benefits. While many reporting standards have been focused primarily on carbon emissions and climate change, there is a growing recognition that biodiversity is an equally critical area of environmental risk. Policymakers could encourage or require companies to disclose biodiversity-related metrics as part of their broader sustainability reports. This would provide investors with a more complete picture of a company’s environmental performance and risk exposure, ultimately fostering more sustainable investment flows. The establishment of clear, consistent guidelines for biodiversity disclosure would also reduce discrepancies in reporting practices and enhance the comparability of data across firms.

Based on these insights, this study provides several innovative recommendations for firms and other stakeholders to consider:

First, develop integrated biodiversity performance dashboards. Companies should create dashboards that combine both ecological and financial indicators, offering investors a comprehensive view of how biodiversity efforts contribute to the firm’s overall sustainability and risk management strategy. These tools could facilitate more transparent and meaningful communication with stakeholders.

Second, adopt investor segmentation approaches. Given the varied responses to biodiversity disclosures based on investor profiles, companies can segment their investor communications by risk tolerance and climate awareness levels. For example, risk-averse investors may be more responsive to clear communication about financial safeguards, while environmentally conscious investors may prioritize a company’s biodiversity impact metrics.

Third, introduce third-party assurance and certification. To address potential concerns over the credibility of biodiversity claims, companies could seek third-party verification of their biodiversity data or acquire internationally recognized certifications.

Fourth, collaborate with financial institutions to create biodiversity-linked financial products. Companies should work with financial institutions to design products such as biodiversity-linked bonds or green funds, where returns are tied to tangible environmental outcomes. These products could provide investors with more concrete ways to align their portfolios with sustainable and biodiversity-positive investments.

Finally, promote investor education and environmental literacy. Firms, regulators, and financial institutions should collaborate to raise awareness about the interdependencies between climate change and biodiversity loss. This could include targeted investor education programs and incorporating biodiversity metrics into financial literacy campaigns, ensuring that investors are better equipped to evaluate both environmental and financial risks.

In conclusion, this study provides significant practical and managerial insights into the relationship between biodiversity disclosure and investor behavior. By demonstrating the importance of clear, transparent biodiversity reporting and highlighting the psychological factors that shape investment decisions, the research offers a roadmap for companies, regulators, and financial institutions seeking to encourage more sustainable investment practices. The findings suggest that by integrating biodiversity disclosure into broader reporting frameworks and tailoring communication strategies to investor profiles, firms can better engage with a growing cohort of sustainability-oriented investors, ultimately driving positive environmental outcomes and contributing to long-term financial stability.

6. Research Limitations

Despite the valuable insights provided by this study, several limitations warrant attention. First, the sample data is exclusively from certain regions in China, which may limit the generalizability of the findings to other regions with differing economic conditions, environmental awareness, or cultural attitudes toward green investments. Expanding the sample to include diverse geographic regions would provide a more comprehensive understanding of the phenomenon.

Second, this study focuses solely on Chinese data, without incorporating international perspectives. Including cross-country data could offer comparative insights into how corporate biodiversity information disclosure influences investor behavior in different regulatory, environmental, and economic contexts, enhancing the robustness of the conclusions.

Furthermore, this study does not incorporate demographic or experiential control variables such as income, education, age, investment experience, or environmental values. These factors may influence how investors perceive and respond to sustainability information; future research should integrate these control variables to provide a more comprehensive explanation of the determinants of green investment behavior.

Additionally, while this study investigated several key variables, other potential factors influencing investment confidence and willingness, such as environmental protection practice experience, job responsibilities, and content, were not explored. Future research could consider these variables to capture a more holistic view of investor decision-making processes.

The cross-sectional nature of this research also presents a limitation, as it does not account for changes in investor behavior over time. A longitudinal approach would allow for the examination of how the relationships between corporate biodiversity disclosure, investor confidence, and willingness evolve in response to changes in market conditions or environmental policies.

One limitation of this study is that while expert interviews and pilot tests were conducted to inform the survey design, future research could further validate the findings through qualitative triangulation, such as investor interviews or content analysis of biodiversity disclosure reports, to better understand how investors interpret and engage with biodiversity information in real-world contexts.

Moreover, the reliance on survey data from a single source introduces potential biases inherent in self-reported data. Incorporating data from multiple sources, including financial records or market performance data, would help mitigate these limitations.