Eco-Innovation & Earnings Management

Updated 25 August 2025

The paper examines how eco-innovation, defined as the development of sustainable products and processes, interacts with earnings management to shape corporate reporting.
Methodological advances like text-based NLP and econometric models improve the measurement of true green innovation and its impact on discretionary accruals.
Regulatory frameworks and prudential policies play a key role in curtailing opportunistic earnings management while promoting transparent, sustainable business practices.

Eco-innovation is the development and application of innovative products, processes, or business models that yield environmental benefits, often in response to regulatory, market, or societal demands for sustainability. Earnings management entails discretionary manipulation, smoothing, or adjustment of financial statements by managers to achieve specific targets—whether for meeting benchmarks, projecting stability, or disguising economic volatility. The intersection of eco-innovation and earnings management reflects evolving corporate incentives, regulatory contexts, and methodological advances in empirical research. Recent research has provided rigorous evidence on how environmental initiatives interact with financial reporting strategies and how such interrelations are shaped by transparency, financial constraints, sectoral dynamics, and measurement methodology.

1. Strategic Transparency, ESG Disclosure, and Earnings Management

A foundational dimension of eco-innovation’s relationship with earnings management is the role of transparency in ESG (Environmental, Social, Governance) disclosures. Firms consistently delivering positive earnings surprises are empirically more transparent in their ESG communications than their negative-surprise counterparts (Kashyap et al., 2020). Higher ESG disclosure is both an operational signal of ethical, sustainable practice and a reputational buffer. Firms preferring transparency usually opt for comprehensive and voluntary reporting on their CSR and eco-innovation activities, which yields investor confidence and serves as an alternative strategic defense to earnings management. Conversely, firms with regular negative earnings surprises often exhibit lower ESG disclosure quality and volume, suggesting they are more prone to earnings management, selectively withholding both financial and non-financial information.

Statistically, this link is demonstrated by binary logistic regression models assessing the probability that a firm delivers positive earnings surprises as a function of ESG disclosure components ( $ESG_i$ , $Env_i$ , $Soc_i$ , $Gov_i$ ) and financial control variables. Additionally, idiosyncratic risk (via log-transformed residuals from Fama-French regressions) helps quantify financial uncertainty not explained by common risk factors. The empirical outcome is that none of the conventional financial metrics distinguish between transparency levels; instead, qualitative disclosure metrics and the number of negative news items are robust indicators of a firm's propensity for earnings management.

2. Eco-Systemic Prudential Policies and Socio-Environmental Solvency

Eco-systemic prudential policies, anchored in environmental and social objectives, aim to reorient the flow of capital towards sustainable firms—explicitly integrating “socio-environmental solvency” into macro, micro, and environmental regulatory decision-making (Chémali et al., 2022). The CARE-TDL model institutionalizes ecological debt in the liabilities section of the balance sheet, requiring accounting for human ( $H$ ), natural ( $N$ ), and financial ( $F$ ) capital such that:

$\text{Solvency}_{SE} = \frac{H + N + F}{\text{Total Liabilities}}$

Such frameworks enforce non-compensation principles, ensuring environmental degradation cannot be offset by financial gains alone. The model targets “transparent” reporting: making the costs of eco-innovation explicit, thus constraining earnings management opportunities. However, increased complexity in valuation and the introduction of non-financial metrics can also create ambiguities, offering new avenues for managerial discretion or even regulatory arbitrage.

Policy mechanisms include differential monetary incentives (e.g., lower refinancing rates for "green" banks), tailored capital and reserve requirements, and state-backed guarantees for long-term sustainable investments. These mechanisms aim to redirect investment from "brown" enterprises toward "virtuous" firms, simultaneously neutralizing systemic risk but also requiring vigilance against over-expansion of credit and greenwashing practices.

3. Eco-Innovation, Productivity, and Sectoral Earnings Management Incentives

Research employing extended CDM (Crépon–Duguet–Mairesse) frameworks demonstrates that the productivity effects of green patents differ markedly by industry and pollution intensity (Jiang et al., 29 Jan 2024). For pollution-intensive sectors, both green and non-green patents drive productivity gains, with green innovation leading to particularly strong results in the mid-to-high productivity percentiles (inverse U-shape in quantile regressions). In contrast, for low-polluting or non-polluting firms, green innovation frequently delivers negligible or negative productivity returns.

From the perspective of earnings management, these heterogeneities have strategic implications. In sectors where green innovation delivers strong productivity returns, its reporting serves as a credible signal rather than a tool for income smoothing. Yet, for firms facing low or negative productivity impacts from green innovation (primarily non-polluting sectors), managers might adjust R&D expense timing or discretionary accruals to offset diminished economic outcomes, thereby managing earnings outcomes to preserve external legitimacy.

The crowding-out effect—the strategic diversion of resources from productive non-green R&D to environmentally focused innovation—is thus not only an innovation allocation problem but also a reporting one, potentially incentivizing earnings management when productivity losses arise.

Text-based NLP approaches have introduced higher precision in measuring “true” green innovation, distinguishing genuine environmental technologies from those ambiguously coded in traditional patent databases (Santarlasci et al., 3 Jul 2025). Using a Word2Vec-trained neural network and custom green vocabulary, the share of “true” green patents is estimated at about 20% of the prior literature’s broader counts.

Econometric models relating patenting to firm outcomes demonstrate that holding at least one “true” green patent is associated with pronounced gains in sales, market share, and labor productivity, but not with statistically robust operating profit improvements when controlling for reverse causality via propensity score matching (PSM). For high-novelty green patents, sales gains are even higher, supporting the economic rationale for targeted support of high-quality eco-innovation. The implication is that earlier measures exaggerate the financial impact of eco-innovation due to classification error, which can be exploited in earnings management or greenwashing. Methodological advances improve both academic rigor and policy relevance, allowing for more granular monitoring and more effective regulatory targeting.

5. Earnings Management: Financial Constraints, Opacity, and Strategic Motivations

Empirical analysis of FTSE All-Share firms demonstrates that eco-innovation intensity (measured by Eco-Innovation Score, EIS) is positively associated with earnings management (proxied by discretionary accruals, DACC), particularly under financial constraints and opacity (Sastroredjo et al., 19 Aug 2025). Managers incurring significant eco-innovation costs may smooth income via discretionary adjustments, signaling both environmental commitment and financial stability. This relationship is amplified where Whited-Wu (WW) scores are high (indicating financial stress) and where financial transparency is low.

Robustness is established via entropy balancing, PSM, and Heckman correction models—confirming the dual strategic role of eco-innovation. Notably, this dual role is less pronounced in highly transparent firms, emphasizing the importance of disclosure in constraining opportunistic managerial behavior.

The principal equations for earnings management estimation include variants of the Jones, Dechow, Kasznik, and Kothari models, e.g.:

$TA_{it} = \delta_0 + \delta_1 \frac{AREV_{it} - AREC_{it}}{A_{it-1}} + \delta_2 \frac{PPE_{it}}{A_{it-1}} + \delta_3 \frac{ROA_{it}}{A_{it-1}} + \varepsilon_{it}$

The findings suggest that, especially under constrained funding or limited transparency, eco-innovation can be strategically leveraged both for sustainability signaling and for earnings management.

6. Regulatory and Governance Implications

The interplay between eco-innovation and earnings management necessitates regulatory oversight and methodological rigor in reporting frameworks. Enhanced disclosure requirements, harmonized standards for socio-environmental solvency, and tighter regulation in contexts of financial opacity are recommended (Chémali et al., 2022, Sastroredjo et al., 19 Aug 2025). Investors and fund managers should calibrate their evaluation frameworks to account for both environmental initiatives and governance structures, recognizing that high eco-innovation scores may coexist with active earnings management.

Boards and management must ensure that sustainability initiatives are aligned with transparent, sound financial reporting practices, deploying internal controls to safeguard both environmental integrity and financial transparency. Methodological improvements—such as text-based classification of innovation—can further aid regulators and researchers in distinguishing genuine sustainability investment from opportunistic signal enhancement.

7. Methodological Table: Principal Models and Measures

Model/Measure	Functional Form / Metric	Context of Use
Logistic Regression	$P(Y_i=1) = \frac{1}{1 + e^{-(\beta_0 + \ldots )}}$	Linking ESG scores to earnings surprises (Kashyap et al., 2020)
Eco-Systemic Solvency (CARE-TDL)	$\frac{H + N + F}{\text{Total Liabilities}}$	Assessing socio-environmental solvency (Chémali et al., 2022)
CDM Model w/ Quantile Regression	Extended CDM, UQR, RIF estimations	Productivity impact of green/non-green patents (Jiang et al., 29 Jan 2024)
Earnings Management (Kothari et al.)	$TA_{it} = \delta_0 + \delta_1 \ldots$	Estimation of discretionary accruals (Sastroredjo et al., 19 Aug 2025)
Text-Based Patent Classification	Neural network + custom green vocabulary	Identifying “true” green patents (Santarlasci et al., 3 Jul 2025)

Each model offers methodological clarity for assessing distinct dimensions of eco-innovation and its impact on earnings management, underscoring the technical variety required to address firm heterogeneity, sectoral context, and information quality.

In sum, current research illuminates the complex relationship between eco-innovation and earnings management, mediated by transparency, industry context, financial constraints, and methodological rigor in measurement. Regulatory frameworks and investment strategies must adapt to the dual role of environmental initiatives: promoting sustainability while guarding against opportunistic financial reporting. Advances in empirical methodology and integrated prudential policies present pathways toward aligning environmental and financial performance, with implications for management practice, policy design, and academic inquiry.