Silver-Haired, Carbon-Heavy? Director Age and Corporate Environmental Outcomes

Abstract

Corporate boards play a pivotal role in shaping firms’ environmental strategies, yet the influence of board demographics, particularly director age, on sustainability outcomes remains insufficiently understood. This study investigates how the age profile of board members affects corporate environmental performance, including greenhouse gas emissions. Analyzing a comprehensive panel of 1843US publicly listed firms (17,218 firm-year observations) from 1996 to 2018, primarily through panel regressions with firm and year fixed effects, we find consistent evidence that firms with older boards tend to exhibit poorer environmental performance and higher direct, indirect and value chain greenhouse gas emissions. We argue that this relationship is driven by age-related differences in risk tolerance, time horizons, and sensitivity to environmental concerns. Additionally, the study explores moderating factors such as poor governance oversight (board co-option), pressure for profitability from institutional ownership, CEO social and environmental consciousness (CEO gender), and managerial ability, revealing that these governance dynamics significantly influence the strength of the director age–environmental performance link. The results, robust to endogeneity concerns, underscore the importance of considering age diversity and board refreshment in corporate governance to foster more effective environmental stewardship. These insights offer valuable implications for board members, corporate leaders, and policymakers aiming to advance sustainable business practices, but also open up opportunities for further exploration in alternative institutional contexts.

1. Introduction

The role of corporate boards in shaping firms’ environmental strategies has attracted growing scholarly and policy attention in recent years [1,2]. As firms confront increasing pressure from stakeholders, notably regulators, to improve environmental performance and reduce carbon emissions, understanding how board composition influences sustainability outcomes has become a central question in corporate governance research. While prior studies have extensively examined the role of board independence [3], gender diversity [4], dedicated committees [2], and expertise [5], there is a lack of consensus about how the age profile of board members affects environmental performance [6,7,8,9]. Studies looking at the role of director age mainly focus on age diversity [7,8]. This paper addresses this gap by investigating the relationship between board age and firms’ environmental outcomes. Specifically, we explore the following research question: “Does board age matter for environmental outcomes?” Drawing on upper echelons theory [10] and life-cycle perspectives [11], we argue that older directors may exhibit greater risk aversion, shorter time horizons, and reduced sensitivity to environmental priorities, which in turn may hinder firms’ responsiveness to sustainability imperatives. We, therefore, hypothesize that firms with older directors may experience worse environmental outcomes.

Using a comprehensive panel dataset of 1843 US publicly listed firms (corporate boards) from 1996 to 2018 (a total of 17,218 board-year observations), we examine whether the average age of directors on corporate boards systematically influences environmental performance, measured through environmental scores and greenhouse gas emissions. Our central hypothesis posits that firms with older boards demonstrate poorer environmental outcomes and higher emissions. We further explore moderating factors such as governance oversight (captured using board co-option), outside pressure from institutional investors (which might incentivize board myopia), social and environmental consciousness (captured by the presence of a female CEO), and managerial ability, which may condition the strength of the relationship between director age and environmental performance.

Our empirical analysis employs rigorous panel regression techniques, incorporating firm and year fixed effects, with standard errors clustered at the firm level. To address potential endogeneity concerns, we also implement instrumental variable approaches. Across all specifications, we find robust evidence that firms with older boards exhibit significantly lower environmental scores and higher direct, indirect, and value chain greenhouse gas emissions. Additional analyses reveal that this negative association is amplified when board monitoring is weak, such as in firms with a high proportion of co-opted directors. This suggests that poor oversight may reinforce board short-termism, deterring long-term investments in sustainability. Moreover, pressure from institutional investors to maximize profitability further intensifies the tendency of older directors to prioritize financial outcomes over environmental initiatives. However, we also find that the presence of a socially and environmentally conscious leader (proxied by a female CEO), as well as high managerial ability, can mitigate these adverse effects.

This study contributes to the literature on corporate governance and sustainability by identifying director age as a critical yet underexplored dimension of board diversity that materially shapes firms’ environmental outcomes. By combining rich panel data with rigorous empirical methods, we provide robust evidence that older boards are associated with lower environmental performance and higher greenhouse gas emissions. The paper advances understanding of how demographic characteristics, alongside traditional governance structures, affect firms’ capacity to pursue sustainability goals. Specifically, it extends lifecycle theory [11] and upper echelons theory [10] by demonstrating that age-related differences in risk tolerance, time horizons, and sensitivity to environmental concerns translate into tangible differences in environmental outcomes.

Beyond establishing a direct link, the study offers novel insights into the moderating role of governance mechanisms. We show that weak board oversight (via co-option), varying levels of institutional ownership, CEO social and environmental orientation (proxied by gender), and managerial ability significantly shape how director age influences environmental performance. This highlights the complex interplay between board demographics and governance dynamics, suggesting that boardroom composition cannot be evaluated in isolation from institutional and managerial contexts.

From a practical standpoint, our findings provide actionable guidance for corporate leaders and policymakers. Boards can strategically leverage age diversity and targeted board renewal to enhance environmental accountability, while institutional investors and regulators may benefit from integrating demographic considerations into governance codes, sustainability reporting guidelines, and ESG oversight frameworks. Moreover, by revealing how governance mechanisms can amplify or mitigate age-related effects, the study informs the design of interventions aimed at aligning board decision-making with long-term sustainability objectives. Collectively, these contributions underscore the importance of considering both the demographic and governance architecture of boards in fostering environmentally responsible corporate behavior.

The remainder of the paper is structured as follows: Section 2 reviews the relevant theory and develops the study’s hypotheses. Section 3 outlines the data sources and empirical strategy. Section 4 presents the main findings, and Section 6 concludes.

2. Literature Review and Hypotheses

Understanding how corporate boards influence firm outcomes has been a longstanding focus within corporate governance research. A growing body of literature highlights the critical role of board dynamics in shaping strategic decisions and firm performance [3,4,12,13]. The literature suggests that factors such as board gender diversity [4], board independence [3,4], the presence of dedicated sustainability committees [2], the ability of managers [1], board environmental management experience or green background [5], amongst several other features, positively impact environmental performance and disclosure. Among the various board characteristics studied, age remains an understudied but potentially influential dimension, particularly in the context of environmental sustainability.

Prior studies exploring the impact of board or director age have focused on its effects on financial fraud [14], effective board monitoring and governance [15,16], stock price crash risk [17] and firm performance [18], amongst others. To our knowledge, no prior study directly explores how board age impacts sustainability outcomes. Related studies explore board age diversity (from a resource-based theoretical perspective) and its impact on environmental outcomes [19], suggesting that age diversity has a positive impact on environmental outcomes. Our study directly extends prior research by drawing on board age from the upper echelons and lifecycle theoretical perspectives.

Upper echelons theory [10] posits that organizational outcomes—strategic choices, performance, and responses to external challenges—are substantially shaped by the characteristics of top executives and board members. This theory emphasizes that executives’ experiences, values, cognitive bases, and psychological traits influence their interpretations of situations and consequent decision-making. Director age, as a demographic attribute, encapsulates variations in experience, risk preferences, and cognitive frames that are relevant for board-level decision processes.

Older directors tend to possess more extensive professional experience, which may provide valuable knowledge and stability. However, empirical research suggests that advancing age is also associated with greater risk aversion and a preference for maintaining the status quo [20]. Such tendencies can lead to more conservative decision-making, limiting the board’s willingness to embrace innovative or transformational strategies, including those related to environmental sustainability.

Life-cycle perspectives in management research further illuminate how age influences decision-making through shifting priorities and temporal orientations [11]. Older individuals are more likely to exhibit shorter time horizons, focusing on near-term outcomes rather than long-term investments or risks. Environmental initiatives, often characterized by upfront costs and deferred benefits, may be deprioritized by older directors who weigh immediate costs more heavily than future gains. This dynamic can dampen board responsiveness to sustainability imperatives and delay meaningful environmental improvements.

The intersection of upper echelons theory [10,20] and life-cycle perspectives [11] suggests a clear theoretical link between director age and firms’ environmental outcomes. Older boards may be less attuned to the urgency of environmental issues and less proactive in pursuing sustainability innovations. Empirical studies have documented mixed effects of board age on corporate social responsibility and environmental performance [3], highlighting the need for more focused analysis specifically addressing greenhouse gas emissions and environmental scores.

Drawing on the above theoretical foundations, this paper hypothesizes that

H1: 

Ceteris paribus, firms with older boards (directors) are associated with lower environmental performance and higher greenhouse gas emissions.

This hypothesis reflects the anticipated influence of age-related risk aversion and shorter time horizons that inhibit proactive environmental strategies. It also aligns with the notion that age-related cognitive rigidity may reduce directors’ sensitivity to evolving stakeholder expectations around sustainability. Moreover, this relationship may be moderated by governance and board dynamics such as board co-option, institutional ownership, and CEO gender, which can either reinforce or mitigate the effects of director age on environmental outcomes. Exploring these moderating factors is crucial for a nuanced understanding of how demographic attributes translate into environmental performance.

3. Research Methodology

3.1. Sample and Data

We investigate how director age influences firms’ environmental performance and engagement, using an unbalanced panel of publicly listed U.S. firms spanning the period from 1996 to 2018. Our empirical analysis draws on data from multiple sources: MSCI (formerly KLD) for firm-level environmental performance, Trucost for greenhouse gas emissions, Compustat for accounting and financial information, and BoardEx for board and governance characteristics. We also incorporate data from prior research, including the managerial ability scores developed by Demerjian et al. [21]. Variable construction is discussed in detail in a subsequent section. Datasets are merged using standard firm identifiers—GVKEY, CUSIP, ticker symbols, and ISINs—matched by year. We retain only observations with available data across all relevant databases. We restrict our dataset to firms with available data from 1996 to 2018. The start year of 1996 reflects the availability of governance data (director age) from Boardex. The end year of 2018 is selected because MSCI ESG KLD STATS, accessed through Wharton Research Data Services (WRDS), was discontinued by the vendor in 2019. We exclude observations with insufficient data to estimate our baseline model. Specifically, we drop all observations without sufficient data for all variables used in our baseline model. The final sample comprises 17,218 firm-year observations across 1843 unique firms.

3.2. Econometric Specification

To isolate the effect of director age on environmental performance, we follow prior studies [2,22] and estimate the following baseline panel regression model:

$$\mathrm{Environmental}{\mathrm{Performance}}_{it} = \alpha + \beta \mathrm{Director}{\mathrm{Age}}_{it} + {\gamma }^{\prime }{\mathrm{Controls}}_{it} + {\mu }_i + {\lambda }_t + {\epsilon }_{it}$$

(1)

where $\mathrm{Environmental}{\mathrm{Performance}}_{it}$ denotes the environmental performance of firm i in year t, measured using MSCI (formerly KLD) scores or Trucost data on carbon emissions. The key independent variable, $\mathrm{Director}{\mathrm{Age}}_{it}$, captures the average age of board members. ${\mathrm{Controls}}_{it}$ is a vector of time-varying firm-level and governance characteristics that prior research has shown to influence environmental outcomes. These include proxies for profitability, liquidity, firm size, leverage, Tobin’s Q, capital expenditure, growth, audit quality, financial constraints, industry competition, and board structure, among others [2,13].

The specification includes firm fixed effects (${\mu }_i$) to account for unobserved, time-invariant heterogeneity across firms, and year fixed effects (${\lambda }_t$) to control for temporal shocks common to all firms. Standard errors are clustered at the firm level to correct for heteroskedasticity and serial correlation.

3.3. Environmental Performance and Director Age Measures

Our main measure of environmental performance, denoted as the E Score, is constructed using MSCI (formerly KLD) ESG data following prior studies [23,24,25,26]. The score is calculated as follows:

$$\mathrm{E}{\mathrm{Score}}_{it} = {\displaystyle \frac{{\mathrm{Strengths}}_{it}}{\mathrm{Total}\mathrm{Strength}{\mathrm{Items}}_{it}}} - {\displaystyle \frac{{\mathrm{Concerns}}_{it}}{\mathrm{Total}\mathrm{Concern}{\mathrm{Items}}_{it}}}$$

(2)

where ${\mathrm{Strengths}}_{it}$ and ${\mathrm{Concerns}}_{it}$ represent the number of environment-related strengths and concerns identified for firm i in year t, respectively. These are each normalized by the total number of potential strength and concern indicators assessed in that firm-year. This normalization ensures that the score reflects relative environmental engagement, accounting for variation in the number of items evaluated across firms and over time.

In addition, to corroborate our story, we capture environmental performance using Trucost greenhouse gas emissions data covering direct emissions (Scope 1), indirect emissions (Scope 2), and upstream and downstream value chain emissions (Scope 3 upstream and Scope 3 downstream). Following the literature, we take the natural log of the absolute value of these emissions as our measure of greenhouse gas emissions.

Consistent with prior research, our measure of director age is derived from the average age of directors on a firm’s board [14,15,16,18]. We measure director age as the natural log of the average age of directors on a firm’s board plus 1. Results remain qualitatively unchanged when using alternative measures, including the unlogged specification.

3.4. Control Variables

To isolate the effect of director age on corporate environmental outcomes, we incorporate a comprehensive set of control variables that capture firm-level financial conditions, structural characteristics, and governance attributes, all of which are shown in prior literature to influence environmental performance [1,2,4,5]. We begin by controlling for firm performance and valuation, including profitability (return on assets), loss dummy (an indicator for negative earnings), Tobin’s Q (a proxy for growth opportunities and market valuation), and sales growth. These variables account for the notion that firms in stronger financial positions may have more resources or incentives to invest in environmental initiatives. We further control for resource availability and capital structure through firm size (logarithm of total assets), leverage (total debt to total assets), and liquidity (current assets relative to total assets). These measures reflect a firm’s operational scale and financial flexibility, both of which can influence environmental engagement.

To capture investment intensity and asset structure, we include capital expenditure and tangible assets (the ratio of property, plant and equipment to total assets). Firms with higher tangible intensity may operate in more environmentally sensitive sectors and face different emission profiles. We also include industry concentration, which proxies for the competitive landscape in the firm’s operating environment. Firms in highly concentrated industries may face less pressure from competitors and stakeholders to improve environmental performance. We control for firms’ financial constraints using the Kaplan–Zingales (KZ) index [27] as financial constraints may impact environmental protection initiatives. In addition, we control for audit quality using a Big 4 auditor dummy, which may reflect not only the credibility of financial reporting but also potential access to consulting or sustainability advisory services.

Finally, we include a range of governance-related variables. These include board ownership (managerial shareholding), female directors (proportion of women on the board), board size, board independence, board diversity, and CEO–Chair duality. These variables collectively account for differences in board structure and oversight quality that could shape a firm’s environmental strategy. All continuous variables are winsorized at the 1st and 99th percentiles to reduce the influence of outliers. Variable definitions and data sources are detailed in

Table A1. Definition of variables.

Variables	Variable Definition
Panel A: Main variables
Environmental score or E Score	A firm’s environmental performance or engagement score, computed as the difference between its total number of environment-related strengths, normalised by the total number of potential environment-related strengths assessed for that firm-year observation, and the firm’s total number of environment-related concerns, normalised by the total number of potential environment-related concerns. The data are obtained from MSCI (formerly KLD).
Environmental Score (Percentile)	A firm’s percentile E score, capturing its environmental performance relative to other firms.
Direct GHG emissions	The natural log of scope 1 absolute greenhouse gas emissions. The data are obtained from Trucost.
Indirect GHG emissions	The natural log of scope 2 absolute greenhouse gas emissions. The data are obtained from Trucost.
Value chain GHG emissions (U)	The natural log of scope 3 (upstream) absolute greenhouse gas emissions. The data are obtained from Trucost.
Value chain GHG emissions (D)	The natural log of scope 3 (downstream) absolute greenhouse gas emissions. The data are obtained from Trucost.
Director age	The average age of directors on a firm’s board.
Panel B: Control variables
Profitability	Return on assets; operating profit to total asset ratio.
Loss dummy	An identify for firms that report a loss (i.e., ROA less than zero).
Tobin’s Q	The market value of equity plus the book value of debt, scaled by the book value of total assets.
Firm size	The natural log of a firm’s total assets.
Sales growth	The ratio of change in sales to previous sales.
Leverage	The ratio of long-term debt to total assets.
Big 4 Auditor	An indicator for firms audited by a Big 4 audit firm.
Liquidity	The ratio of cash as a proportion of total assets.
Tangible assets	The ratio of property, plant and equipment to total assets.
Capital expenditure	The ratio of capital expenditure to total assets.
Kaplan–Zingales index	The Kaplan–Zingales (KZ) measure of financial constraints [27], computed as $$\begin{array}{c}{\mathrm{KZ}}_i = -1.002{\mathrm{CashFlow}}_i + 0.283{\mathrm{Q}}_i + 3.139{\mathrm{Leverage}}_i\hfill \\ -39.368{\mathrm{Dividends}}_i - 1.315{\mathrm{Cash}}_i\hfill \end{array}$$ where CashFlow is cash flow scaled by total assets, Q is Tobin’s Q, Leverage is the ratio of debt to total assets, Dividends is dividends paid scaled by total assets, and Cash is cash holdings scaled by total assets.
Industry concentration	The Herfindahl-Hirschman index (HHI) estimated from revenue-based market shares within 4-digit SIC code industries.
Discretionary accruals	Discretionary accruals captured using the modified Jones’ model.
Board ownership	Proportion of shares held by board members.
Female directors	Proportion of board members that are female
Board size	The natural log of the number of directors on the board plus 1.
Board independence	The proportion of board members that are independent directors.
Board diversity	The proportion of board members that are non-US citizens by nationality.
CEO Chair duality	An indicator for firms where the CEO and Chair role are held by the same individual.

.

4. Results

4.1. Descriptive Statistics and Correlation Analysis

Table 1. Descriptive statistics.

Variable	N	Mean	SD	P1	P25	Median	P75	P99
	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)
Panel A: Dependent variables
Environmental Score (E Score)	17,218	0.052	0.190	−0.429	0.000	0.000	0.000	0.800
Environmental Score (Percentile)	17,218	25.390	34.695	1.000	7.000	7.000	7.000	99.000
Direct GHG emissions	9030	11.447	2.572	6.120	9.723	11.198	12.946	17.937
Indirect GHG emissions	9032	11.345	1.795	7.426	10.101	11.236	12.565	15.556
Value chain GHG emissions (U)	9036	13.234	1.723	9.131	12.066	13.174	14.385	17.471
Value chain GHG emissions (D)	1774	11.986	3.666	2.216	9.925	12.697	14.478	18.943
Director age	17,218	4.132	0.064	3.954	4.092	4.136	4.174	4.277
Panel B: Control variables
Profitability	17,218	0.054	0.087	−0.229	0.024	0.054	0.091	0.243
Loss dummy	17,218	0.114	0.318	0.000	0.000	0.000	0.000	1.000
Tobin’s Q	17,218	2.048	1.374	0.797	1.206	1.627	2.377	7.459
Firm size	17,218	21.758	1.573	18.793	20.586	21.595	22.751	25.942
Sales growth	17,218	0.091	0.243	−0.399	−0.002	0.069	0.156	0.851
Leverage	17,218	0.194	0.178	0.000	0.041	0.175	0.297	0.667
Big 4 Auditor	17,218	0.926	0.263	0.000	1.000	1.000	1.000	1.000
Liquidity	17,218	0.148	0.159	0.001	0.031	0.089	0.214	0.688
Tangible assets	17,218	0.256	0.226	0.002	0.078	0.184	0.377	0.871
Capital expenditure	17,218	0.047	0.047	0.000	0.017	0.034	0.061	0.236
Kaplan–Zingales index	17,218	0.381	1.212	−3.456	−0.035	0.446	0.972	2.625
Industry concentration	17,218	0.067	0.050	0.023	0.035	0.055	0.080	0.240
Discretionary accruals	17,218	0.074	1.550	−0.899	−0.051	−0.004	0.054	1.633
Panel C: Additional governance control variables
Board ownership	17,218	0.073	0.188	0.000	0.008	0.023	0.070	0.597
Females directors	17,218	0.129	0.105	0.000	0.000	0.125	0.200	0.417
Board size	17,218	9.381	2.336	5.000	8.000	9.000	11.000	16.000
Board independence	17,218	0.754	0.141	0.333	0.667	0.778	0.875	0.923
Board diversity	17,218	0.822	0.315	0.000	0.750	1.000	1.000	1.000
CEO Chair duality	17,218	0.608	0.488	0.000	0.000	1.000	1.000	1.000

Notes: This table reports summary statistics for the full sample used in the analysis. All continuous variables are winsorized at the 1st and 99th percentiles to mitigate the influence of outliers. Environmental Score (E Score) is computed from MSCI KLD data as the net strength minus concern score, normalized by the number of assessed items. GHG emissions data (scope 1, 2, and 3) are sourced from Trucost and reported in natural logs. Director age is the natural log of the average age of directors on the board. All variable definitions are provided in Appendix A.

presents descriptive statistics for the variables used in the empirical analysis. Panel A shows the distribution of the dependent variables. The mean environmental performance score (E Score) is 0.052, with a high standard deviation (0.190), and a median of zero, indicating that many firms exhibit no net environmental strengths. The 99th percentile value of 0.800 suggests that some firms score highly on environmental metrics, although this is not typical. The percentile version of the E Score has a mean of 25.4, reinforcing that most firms are ranked in the lower quartile of the distribution.

Regarding emissions, the average natural log of direct GHG emissions (scope 1) is 11.45, while indirect emissions (scope 2) average 11.35. Value chain emissions (upstream) are highest on average (mean of 13.23), while downstream emissions show substantial variation (SD of 3.67), albeit for a smaller sample of firms. These values highlight the importance of examining different dimensions of emissions separately due to the variation in coverage and scale.

The key independent variable, director age, has a mean of approximately 4.13 (in log scale), with relatively low variation (SD = 0.064), reflecting modest differences in board average age across firms.

Panel B reports the distribution of firm-level control variables. Firms are generally profitable (mean ROA = 5.4%), though 11.4% report a loss. The average Tobin’s Q is 2.05, suggesting strong market valuations relative to assets. Firms are relatively large (mean log assets = 21.76) and have moderate leverage (mean = 0.19). The Big 4 auditor indicator shows that over 92% of firms are audited by a Big 4 firm, indicating broad audit quality coverage. Financial flexibility also varies: the Kaplan–Zingales index and discretionary accruals show wide dispersion, suggesting differences in financing constraints and earnings management practices. These results mirror those reported in recent US studies using a similar sample [13,28].

Panel C summarizes board and governance characteristics. The average board holds a 7.3% ownership in the firm, and female representation is relatively low, with a mean of 12.9% and a median of 12.5%. Boards are moderately sized (mean of 9.4 directors), with high independence (mean = 75.4%) and high nationality diversity (mean = 82.2%). CEO–chair duality is present in approximately 61% of firms, indicating a concentration of leadership roles in a majority of sample firms.

Together, these statistics provide a broad snapshot of the firm characteristics in the sample and highlight the variation in environmental and board-level features across US publicly listed firms.

Table 2. Pairwise correlations.

Variables	(1)	(2)	(3)	(4)	(5)	(6)	(7)	(8)	(9)	(10)	(11)	(12)	(13)	(14)
(1) Director age	1.00
(2) Profitability	0.00	1.00
(3) Loss dummy	−0.03 *	−0.59 *	1.00
(4) Tobin’s Q	−0.13 *	0.41 *	−0.13 *	1.00
(5) Firm size	0.13 *	−0.04 *	−0.08 *	−0.19 *	1.00
(6) Sales growth	−0.06 *	0.18 *	−0.14 *	0.15 *	−0.03 *	1.00
(7) Leverage	0.02 *	−0.13 *	0.08 *	−0.11 *	0.24 *	−0.01	1.00
(8) Big 4 Auditor	−0.02	−0.02 *	0.00	−0.04 *	0.17 *	−0.03 *	0.06 *	1.00
(9) Liquidity	−0.11 *	0.07 *	0.06 *	0.35 *	−0.28 *	0.04 *	−0.32 *	−0.04 *	1.00
(10) Tangible assets	0.03 *	−0.03 *	0.01	−0.14 *	0.12 *	−0.05 *	0.24 *	0.01	−0.37 *	1.00
(11) Capital expenditure	−0.06 *	0.06 *	−0.01	0.05 *	−0.03 *	0.05 *	0.06 *	−0.01	−0.19 *	0.70 *	1.00
(12) Kaplan–Zingales Index	−0.07 *	−0.23 *	0.12 *	−0.07 *	0.12 *	0.09 *	0.43 *	0.04 *	−0.21 *	0.07 *	0.05 *	1.00
(13) Industry concentration	0.07 *	0.03 *	−0.01	0.01	−0.05 *	−0.01	0.03 *	−0.02	−0.06 *	−0.06 *	−0.04 *	−0.01	1.00
(14) Discretionary accruals	0.00	0.03 *	−0.01	0.03 *	0.00	0.03 *	−0.01	0.01	0.02	0.01	0.01	0.00	−0.02	1.00
Variance inflation factors	1.06	1.96	1.60	1.47	1.20	1.08	1.42	1.03	1.48	2.39	2.14	1.34	1.03	1.00

Notes: This table reports pairwise correlation coefficients and variance inflation factors for variables in the baseline model. * denotes statistical significance at the 10% level. Variable definitions are provided in Table A1.

reports the pairwise correlations among the main explanatory variables used in the regression models. Most correlations are modest in magnitude, suggesting limited risk of multicollinearity. The focal independent variable, director age, exhibits small negative correlations with Tobin’s Q (−0.13), sales growth (−0.06), and the Kaplan–Zingales index (−0.07), and a small positive correlation with firm size (0.13). These are consistent with expectations: older boards may be more prevalent in larger, more established firms that are less growth-oriented or financially constrained.

The strongest correlations in the matrix are between profitability and the loss dummy (−0.59) and between capital expenditure and tangible assets (0.70). These associations are expected, given that loss-making firms by definition have negative profitability, and capital expenditure is often higher in firms with more tangible assets. Importantly, none of the correlations exceed 0.80, which is often considered a threshold for multicollinearity concerns [28].

The variance inflation factors (VIFs) reported in the bottom row further support this conclusion. All VIF values are below 2.5, with the highest observed for tangible assets (2.39) and capital expenditure (2.14), both of which remain well within acceptable limits. These results confirm that multicollinearity is not a concern in the empirical models [28,29], and the coefficient estimates are unlikely to be biased due to linear dependence among regressors.

4.2. Regression Analysis: Director Age and Environmental Performance

Table 3. Director age and corporate environmental performance.

	Environmental Performance (E Score)
Variables	(1)	(2)	(3)	(4)
Director age	−0.140 ***	−0.198 ***	−0.224 ***	−0.224 ***
	(0.019)	(0.019)	(0.035)	(0.065)
Profitability		0.027	−0.000	−0.000
		(0.018)	(0.015)	(0.018)
Loss dummy		−0.013 ***	−0.014 ***	−0.014 ***
		(0.005)	(0.004)	(0.005)
Tobin’s Q		0.002 **	−0.009 ***	−0.009 ***
		(0.001)	(0.001)	(0.002)
Firm size		0.033 ***	−0.028 ***	−0.028 ***
		(0.001)	(0.004)	(0.008)
Sales growth		−0.027 ***	−0.002	−0.002
		(0.006)	(0.004)	(0.004)
Leverage		0.023 **	0.071 ***	0.071 ***
		(0.011)	(0.012)	(0.020)
Big 4 Auditor		0.021 ***	−0.003	−0.003
		(0.004)	(0.007)	(0.011)
Liquidity		0.037 ***	0.076 ***	0.076 ***
		(0.009)	(0.014)	(0.024)
Tangible assets		−0.024 **	−0.029	−0.029
		(0.011)	(0.027)	(0.055)
Capital expenditure		0.280 ***	0.154 ***	0.154 **
		(0.041)	(0.044)	(0.063)
Kaplan–Zingales index		−0.011 ***	−0.001	−0.001
		(0.002)	(0.001)	(0.002)
Industry concentration		0.040	−0.143	−0.143
		(0.074)	(0.091)	(0.185)
Discretionary accruals		−0.001 *	−0.002 *	−0.002 *
		(0.001)	(0.001)	(0.001)
Constant	0.632 ***	0.123	1.590 ***	1.590 ***
	(0.077)	(0.079)	(0.159)	(0.310)
Observations	17,218	17,218	17,218	17,218
R-squared	0.168	0.233	0.540	0.540
Industry FE	Yes	Yes	Yes	Yes
Firm FE	No	No	Yes	Yes
Year FE	Yes	Yes	Yes	Yes

Notes: This table reports coefficient estimates from panel regressions examining the relationship between board director age and corporate environmental performance. The dependent variable is the firm-level Environmental Score (E Score), computed from MSCI KLD data. Column (1) includes only director age along with industry and year fixed effects. Column (2) adds firm-level financial controls, while Columns (3) and (4) further introduce firm fixed effects. Robust standard errors, clustered at the firm level, are reported in parentheses. ***, **, and * indicate significance at the 1%, 5%, and 10% levels, respectively. All variable definitions are provided in Table A1.

presents the results from panel regressions examining the relationship between board director age and firm-level environmental performance, measured by the MSCI-based Environmental Score (E Score). Across all model specifications, director age is negatively and significantly associated with environmental performance, supporting the hypothesis that older boards are less responsive to environmental concerns.

In Column (1), which includes only director age and fixed effects, the coefficient on director age is −0.140 and statistically significant at the 1% level. This negative relationship becomes stronger in Columns (2) through (4) as additional firm-level control variables are introduced. In the fully specified model (Columns 3 and 4), which includes firm fixed effects, the coefficient remains negative and significant at −0.224. These results suggest that the observed relationship is robust to the inclusion of various firm characteristics, industry and year fixed effects, and unobserved time-invariant firm heterogeneity.

Among the controls, loss-making firms are significantly less likely to score well on environmental performance [3,4,6], while liquidity, capital expenditure, and firm size (in Column 2) show a positive relationship with the E Score, consistent with the idea that resource availability enables firms to pursue environmental initiatives [2,6]. Interestingly, Tobin’s Q and firm size switch signs once firm fixed effects are introduced, reflecting the importance of accounting for unobserved heterogeneity. Discretionary accruals have a small but consistently negative effect, possibly reflecting lower transparency or earnings management among less environmentally committed firms.

The R-squared improves substantially from 0.168 in Column (1) to 0.540 in Columns (3) and (4), indicating that the full model explains a large portion of the variation in environmental performance. Overall, the results provide strong support for the argument that board age is a meaningful predictor of environmental outcomes.

4.3. Regression Analysis: Director Age and Greenhouse Gas Emissions

Table 4. Director age and greenhouse gas emissions (GHG).

	Direct GHG	Indirect GHG	Value Chain GHG	Value Chain GHG
	(Scope 1)	(Scope 1)	(Scope 3 Upstrm)	(Scope 3 Dnstrm)
Variables	(1)	(2)	(3)	(4)
Director age	0.709 ***	0.907 ***	0.545 ***	−0.320
	(0.253)	(0.214)	(0.106)	(1.301)
Profitability	0.320 *	0.341 **	0.467 ***	0.653
	(0.168)	(0.148)	(0.154)	(0.674)
Loss dummy	−0.012	−0.025	−0.054 **	0.090
	(0.032)	(0.029)	(0.022)	(0.127)
Tobin’s Q	0.058 ***	0.028 ***	0.028 ***	0.024
	(0.012)	(0.008)	(0.006)	(0.043)
Firm size	0.588 ***	0.647 ***	0.738 ***	0.106
	(0.026)	(0.021)	(0.015)	(0.215)
Sales growth	0.154 ***	0.066 **	0.053 **	0.311 **
	(0.032)	(0.028)	(0.023)	(0.127)
Leverage	−0.245 ***	−0.266 ***	−0.190 ***	0.016
	(0.074)	(0.069)	(0.047)	(0.430)
Big 4 Auditor	−0.032	−0.187 **	0.036	0.691
	(0.069)	(0.078)	(0.036)	(0.435)
Liquidity	−0.231 *	−0.285 ***	−0.585 ***	0.473
	(0.128)	(0.088)	(0.067)	(0.498)
Tangible assets	0.632 ***	0.851 ***	−0.425 ***	−0.043
	(0.176)	(0.180)	(0.130)	(1.193)
Capital expenditure	−0.141	1.084 ***	0.649 ***	−0.128
	(0.328)	(0.288)	(0.214)	(1.288)
Kaplan–Zingales index	0.000	−0.010 **	−0.026 ***	−0.002
	(0.005)	(0.004)	(0.006)	(0.011)
Industry concentration	−1.320 ***	0.965 **	0.526 **	41.022 ***
	(0.512)	(0.384)	(0.223)	(8.194)
Discretionary accruals	−0.006 *	−0.006	−0.003	−0.320 ***
	(0.004)	(0.005)	(0.002)	(0.113)
Constant	−4.812 ***	−7.078 ***	−5.536 ***	7.488
	(1.138)	(0.977)	(0.557)	(7.429)
Observations	8979	8981	8985	1596
R-squared	0.960	0.927	0.982	0.981
Industry FE	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes

Notes: This table presents coefficient estimates from panel regressions examining the relationship between director age and firm-level greenhouse gas (GHG) emissions. The dependent variables are the natural logs of scope 1 (direct), scope 2 (indirect), and scope 3 (value chain upstream and downstream) absolute emissions, sourced from Trucost. All models include firm, industry, and year fixed effects. Column (1) models scope 1 emissions; Column (2) scope 2; Column (3) upstream scope 3 and Column (4) downstream scope 3 emissions. Robust standard errors clustered at the firm level are reported in parentheses. ***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively. Variable definitions are provided in Table A1.

presents panel regression results assessing the association between director age and firm-level greenhouse gas (GHG) emissions, covering scope 1 (direct), scope 2 (indirect), and scope 3 (value chain) emissions. These models extend the baseline analysis reported in Table 3, which demonstrated a robust negative relationship between director age and environmental performance (E Score). Here, the results offer further insight into the underlying environmental mechanisms, particularly emissions intensity.

Across Columns (1) through (3), the coefficient on director age is positive and statistically significant, indicating that firms with older boards tend to emit more greenhouse gases through direct operations (scope 1), indirect operations (scope 2), and upstream value chain activities (scope 3). Specifically, a one-unit increase in the log of director age is associated with a 0.71-unit increase in log scope 1 emissions, a 0.91-unit increase in log scope 2 emissions, and a 0.545-unit increase in log scope 2 emissions, holding other factors constant. These findings reinforce the view that older boards are less proactive or effective in managing emissions, which aligns with earlier results showing poorer overall environmental performance. However, our results in Column (4) show no statistically significant relationship between director age and downstream value chain (scope 3) emissions. This may reflect reduced influence over downstream supply chain activities, which are often less visible and less controllable from the firm’s perspective.

Taken together with Table 3, these results provide robust evidence that board aging is associated not only with lower environmental scores but also with significantly higher greenhouse gas emissions, particularly in core operational areas. This strengthens the case for viewing director age as a critical dimension of board composition in the context of environmental governance.

4.4. Cross-Sectional Analysis

Table 5. Cross-sectional analysis.

	Environmental Performance (E Score)
Variables	(1)	(2)	(3)	(4)
Director age	−0.108 **	0.613 ***	−0.230 ***	−0.212 ***
	(0.050)	(0.116)	(0.035)	(0.036)
Co-option	0.975 ***
	(0.261)
Director age × Co-opted board	−0.233 ***
	(0.063)
Institutional ownership		4.486 ***
		(0.563)
Director age × Institutional ownership		−1.095 ***
		(0.137)
Female CEO			−1.088 **
			(0.450)
Director age × Female CEO			0.266 **
			(0.110)
Managerial ability				−2.604 ***
				(0.675)
Director age × Managerial ability				0.617 ***
				(0.164)
Profitability	0.002	−0.003	−0.000	0.007
	(0.017)	(0.015)	(0.015)	(0.017)
Loss dummy	−0.014 ***	−0.013 ***	−0.014 ***	−0.010 **
	(0.005)	(0.004)	(0.004)	(0.005)
Tobin’s Q	−0.010 ***	−0.009 ***	−0.009 ***	−0.009 ***
	(0.001)	(0.001)	(0.001)	(0.001)
Firm size	−0.029 ***	−0.027 ***	−0.028 ***	−0.027 ***
	(0.004)	(0.004)	(0.004)	(0.004)
Sales growth	−0.004	−0.001	−0.002	0.003
	(0.005)	(0.004)	(0.004)	(0.005)
Leverage	0.069 ***	0.069 ***	0.070 ***	0.071 ***
	(0.013)	(0.012)	(0.012)	(0.013)
Big 4 Auditor	−0.008	−0.003	−0.003	0.000
	(0.007)	(0.007)	(0.007)	(0.007)
Liquidity	0.072 ***	0.081 ***	0.075 ***	0.078 ***
	(0.015)	(0.014)	(0.014)	(0.015)
Tangible assets	−0.019	−0.026	−0.030	−0.092 ***
	(0.029)	(0.027)	(0.027)	(0.028)
Capital expenditure	0.168 ***	0.151 ***	0.153 ***	0.202 ***
	(0.047)	(0.044)	(0.044)	(0.046)
Kaplan–Zingales index	−0.001	−0.001	−0.001	−0.002 *
	(0.001)	(0.001)	(0.001)	(0.001)
Industry concentration	−0.135	−0.120	−0.140	−0.242 **
	(0.100)	(0.091)	(0.091)	(0.096)
Discretionary accruals	−0.001 *	−0.002 *	−0.002 *	−0.002 *
	(0.001)	(0.001)	(0.001)	(0.001)
Constant	1.145 ***	−1.862 ***	1.615 ***	1.540 ***
	(0.221)	(0.482)	(0.160)	(0.169)
Observations	15,628	17,218	17,218	14,691
R-squared	0.538	0.542	0.540	0.551
Industry FE	Yes	Yes	Yes	Yes
Firm FE	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes

Notes: This table presents coefficient estimates from panel regressions assessing how firm- and board-level characteristics moderate the relationship between director age and corporate environmental performance. The dependent variable is the Environmental Score (E Score), constructed from MSCI KLD data. Each column introduces an interaction term between director age and a specific moderating variable: board co-option (Column 1), institutional ownership (Column 2), female CEO (Column 3), and managerial ability (Column 4). All models include firm, industry, and year fixed effects, along with financial and governance control variables. Robust standard errors clustered at the firm level are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Variable definitions are provided in Table A1.

reports cross-sectional moderation analyses that explore how various governance and managerial characteristics influence the relationship between director age and environmental performance. Across all specifications, the dependent variable is the firm’s Environmental Score (E Score), and the models include firm, industry, and year fixed effects, as well as the full set of financial controls used in the baseline analysis.

In Column (1), we investigate the role of board co-option, which proxies for board capture by the CEO or the lack of oversight from board members due to allegiance to the CEO [22,30]. Board co-option is captured as the fraction of directors appointed after the current CEO took office [30], with a higher fraction representing weaker board oversight. The interaction term between director age and co-option is negative and statistically significant, suggesting that the environmental underperformance associated with older boards is even more pronounced in firms with co-opted boards. This implies that board independence moderates the impact of director age, with older directors being particularly ineffective in driving environmental improvements when they are more aligned with or influenced by the CEO.

The moderating role of co-option is intuitive when considered alongside the characteristics of older directors. Life-cycle theory [11] posits that directors in later stages of their careers tend to be more risk-averse and operate with shorter horizons, reducing their willingness to challenge prevailing managerial strategies, especially when these prioritize short-term profitability over long-term sustainability. In addition, reputational and social dynamics may discourage older directors from contesting CEO authority or advocating for progressive environmental practices, as they have fewer incentives to accumulate reputational capital late in their careers. Finally, older directors may be less equipped to assess emerging environmental and technological issues, leaving them more dependent on information supplied by management. In highly co-opted boards, where the CEO dominates both appointments and information flows, these tendencies are amplified. As a result, the negative impact of director age on environmental performance is most acute when board independence is weakened by co-option.

Column (2) examines institutional ownership as a potential moderating factor. Prior research suggests that institutional shareholders may play a dual role by increasing board monitoring, thereby reducing agency problems, but also increasing pressure on managers to demonstrate firm profitability, thereby incentivizing managerial short-termism [31]. Our results indicate that while institutional ownership is positively associated with overall environmental performance, the interaction term between director age and institutional ownership is significantly negative. This suggests that in firms with lower levels of institutional ownership, the negative effects of older boards are amplified, whereas higher institutional ownership may constrain the behavior of older directors and mitigate their tendency toward environmental conservatism.

The moderating effect of institutional ownership can be understood in several ways. Older directors, consistent with life-cycle perspectives [11], often prioritise stability and short-term performance, making them less inclined to support costly or uncertain environmental initiatives. In firms with low levels of institutional ownership, these tendencies face little resistance, amplifying the detrimental effects of director age. By contrast, institutions with significant and long-term stakes impose reputational and legitimacy pressures that counteract board inertia and provide strategic incentives for sustainability. Institutional investors may also bring ESG expertise and informational resources that reduce older directors’ reliance on limited or outdated perspectives. Together, these mechanisms suggest that institutional ownership disciplines older boards, limiting their bias toward environmental conservatism, while firms with a weak institutional presence remain more vulnerable to the negative influence of board age.

Column (3) investigates the role of female leadership. Prior research highlights that women in executive positions often emphasize sustainability and socially conscious business practices [4,13]. Our results show that while firms with female CEOs are, on average, associated with lower environmental scores, the interaction between director age and female CEO presence is positive and significant. This indicates that under female leadership, the adverse effect of older directors on environmental outcomes is attenuated.

The moderating influence of female CEOs reflects several mechanisms. Women in leadership roles are frequently associated with participatory and socially oriented leadership styles, which may reduce the dominance of risk-averse tendencies among older directors and encourage more inclusive decision-making. Female CEOs are also more likely to prioritize sustainability as part of broader commitments to legitimacy and long-term stakeholder engagement, which can counterbalance board-level conservatism. Furthermore, female executives face heightened scrutiny and reputational pressures, creating strong incentives to pursue visible sustainability initiatives that constrain resistance from older board members. Taken together, these dynamics suggest that female CEOs help redirect the strategic focus of older boards toward environmental responsibility, thereby mitigating the inertia that typically accompanies director age.

Finally, Column (4) considers managerial ability as a moderating factor. We find that the interaction between director age and managerial ability is positive and significant, indicating that more competent managers can offset the adverse impact of older directors on environmental performance.

Managerial ability moderates this relationship through several mechanisms. High-ability managers possess stronger strategic, analytical, and communication skills, enabling them to present environmental initiatives as value-enhancing investments rather than discretionary costs. This framing is particularly effective in persuading older directors, who are often more skeptical of sustainability projects. Competent managers also command greater credibility and authority within the boardroom, which allows them to counterbalance the conservative tendencies of older directors and steer board discussions toward long-term considerations. In addition, able managers are better equipped to reconcile the competing interests of stakeholders, bridging the gap between the short-term priorities often emphasized by older boards and the long-term benefits of environmental stewardship. Taken together, these mechanisms explain why high-ability managers can neutralize the environmental inertia of older boards and mobilize them in support of sustainability agendas.

Taken together, these results provide nuanced insight into the conditions under which director age influences environmental outcomes. They suggest that the negative association between director age and environmental performance is not uniform but is shaped by broader governance and leadership structures. The presence of strong external monitors, capable executives, or more independent (or less co-opted) boards can offset the environmentally conservative tendencies of older directors. These findings add depth to the main result in Table 3, highlighting the importance of institutional context in shaping the boardroom’s role in corporate sustainability.

4.5. Robustness Checks

Table 6. Robustness checks.

	Percentile	Lagged IVs	Governance Controls	2SLS
	E Score	E Score	E Score	Director Age	E Score
Variables	(1)	(2)	(3)	(4)	(5)
Director age	−28.115 ***	−0.198 ***	−0.167 ***		−0.661 ***
	(6.081)	(0.040)	(0.035)		(0.174)
Mean age of directors in state (t − 5)				0.683 ***
				(0.032)
Profitability	1.280	0.044 **	−0.005	0.019 **	0.018
	(2.863)	(0.019)	(0.015)	(0.009)	(0.034)
Loss dummy	−1.827 **	−0.009	−0.014 ***	−0.006 ***	−0.012
	(0.798)	(0.005)	(0.004)	(0.002)	(0.008)
Tobin’s Q	−1.127 ***	−0.010 ***	−0.009 ***	−0.003 ***	0.005 **
	(0.233)	(0.001)	(0.001)	(0.001)	(0.002)
Firm size	−2.978 ***	−0.026 ***	−0.026 ***	−0.000	0.051 ***
	(0.678)	(0.005)	(0.004)	(0.000)	(0.002)
Sales growth	−0.388	0.002	−0.004	0.003	−0.054 ***
	(0.807)	(0.005)	(0.004)	(0.004)	(0.010)
Leverage	15.023 ***	0.080 ***	0.067 ***	−0.009 **	0.034 **
	(2.343)	(0.015)	(0.012)	(0.004)	(0.015)
Big 4 Auditor	0.756	−0.004	−0.006	−0.018 ***	0.033 ***
	(1.186)	(0.008)	(0.007)	(0.003)	(0.011)
Liquidity	5.941 **	0.080 ***	0.067 ***	−0.017 ***	0.061 ***
	(2.646)	(0.016)	(0.014)	(0.006)	(0.018)
Tangible assets	−11.960 ***	−0.031	−0.033	0.014 ***	−0.012
	(4.529)	(0.030)	(0.027)	(0.005)	(0.018)
Capital expenditure	17.290 **	0.155 ***	0.123 ***	−0.070 ***	0.230 ***
	(8.339)	(0.048)	(0.045)	(0.023)	(0.075)
Kaplan–Zingales index	−0.719 ***	−0.002	−0.002 *	−0.002 **	−0.016 ***
	(0.219)	(0.001)	(0.001)	(0.001)	(0.002)
Industry concentration	−3.070	−0.032	−0.108	0.097 **	0.022
	(13.795)	(0.106)	(0.090)	(0.039)	(0.144)
Discretionary accruals	−0.186	−0.001	−0.001 *	0.000	−0.003 *
	(0.135)	(0.001)	(0.001)	(0.000)	(0.002)
Board ownership			0.011
			(0.012)
Females directors			0.009
			(0.022)
Board size			0.001
			(0.001)
Board independence			−0.053 ***
			(0.013)
Board diversity			0.102 ***
			(0.007)
CEO Chair duality			0.005
			(0.003)
Constant	207.088 ***	1.441 ***	1.259 ***	1.354 ***	1.541 **
	(27.598)	(0.183)	(0.162)	(0.131)	(0.715)
Observations	17,218	14,635	17,218	8967	8968
R-squared	0.574	0.550	0.546	0.219	0.278
Firm FE	Yes	Yes	Yes	No	No
Industry FE	Yes	Yes	Yes	Yes	Yes
Year FE	Yes	Yes	Yes	Yes	Yes

Notes: This table reports coefficient estimates from robustness tests examining the relationship between director age and corporate environmental performance. The dependent variable is the Environmental Score (E Score), constructed from MSCI KLD data, except in Column (1), which uses the percentile rank of E Score. Column (2) uses lagged independent variables to address potential endogeneity. Column (3) includes additional board-level governance controls. Columns (4) and (5) report two-stage least squares (2SLS) estimates using the average age of directors in the same state, lagged by five years, as an instrument for director age. All models include industry and year fixed effects. Columns (1) through (3) additionally include firm fixed effects. Robust standard errors clustered at the firm level are reported in parentheses. ***, **, and * denote statistical significance at the 1%, 5%, and 10% levels, respectively. Variable definitions are provided in Table A1.

reports a series of robustness checks designed to validate the baseline finding that older board directors are associated with poorer environmental performance. The analyses apply alternative specifications, variable definitions, and estimation strategies to confirm the consistency of the core results presented in Table 3.

Column (1) uses the percentile version of the E Score as the dependent variable, which measures firms’ environmental performance relative to their peers. As suggested by prior studies [23,24,25], this percentile measure is less prone to measurement error as it considers ranks rather than absolute scores. The coefficient on director age is large, negative, and highly significant (−28.115), confirming that older boards rank lower in terms of environmental standing across the sample. This result reinforces the interpretation that older boards underperform not just on an absolute basis but also relative to industry peers. Column (2) introduces lagged independent variables to address potential reverse causality or simultaneity concerns. The coefficient on lagged director age remains significantly negative (−0.198), indicating that prior board age is a strong predictor of current environmental outcomes. This strengthens the temporal argument for causality from board composition to environmental performance.

Director age may be correlated with other governance characteristics, leading to omitted variable bias in our base model. Column (3), therefore, adds governance control variables to the baseline model, including board ownership, board size, gender diversity, independence, and CEO-chair duality. The coefficient on director age remains negative and significant (−0.167), confirming that the effect is not driven by omitted board-level governance characteristics. Among the added controls, board independence is negatively associated with environmental performance, while board diversity is positively associated, suggesting diversity may offset some negative governance effects [4].

Despite our use of lagged IVs and inclusion of additional controls, our results may still be prone to endogeneity from omitted variables or reverse causality. To address this more formally and evidence causality between director age and environmental performance, we adopt a two-stage least squares (2SLS) regression approach in Columns (4) and (5). We use the average age of directors in the same state lagged by five years as an instrument for firm-level director age. The argument is that a firm draws its directors from the pool of directors in its geographic region and, therefore, the distribution of the director’s age within a firm should reflect the historical age distribution of directors in the firms’ State [32]. The instrument satisfies the exogeneity criterion because past age structures within a State influence current board age dynamics, but not current firm-level environmental performance directly. This instrument is strongly significant in the first stage (Column 4), with a coefficient of 0.683, supporting its relevance. The 2SLS diagnostics, including under identification (Kleibergen–Paap rk LM Statistic), the weak identification test statistic (Cragg-Donald Wald F statistic), and our over-identification test (Hansen J statistic), confirm instrument validity. The second-stage results in Column (5) show that instrumented director age remains significantly negatively associated with E Score (−0.661), reinforcing the claim that the observed relationship is unlikely to be driven by endogeneity.

Overall, the results from Table 6 demonstrate that the negative association between director age and environmental performance remains robust to alternative outcome variables, various lag structures, expanded governance controls, and corrections for potential endogeneity. These robustness checks provide further support for the core argument that older boards are systematically less effective in promoting strong environmental outcomes.

5. Discussion

Our findings provide robust evidence that director age is a critical determinant of firms’ environmental outcomes, both in terms of aggregate environmental performance (E Score) and operational greenhouse gas emissions. These results are consistent with the predictions of upper echelons theory [10], which posits that the characteristics of top executives and board members—such as age, cognitive frames, and experiences—shape strategic decision-making. Older directors may rely on established routines, exhibit greater risk aversion, and possess shorter remaining professional horizons, reducing their propensity to champion long-term sustainability initiatives. This theoretical lens helps explain why boards with higher average age consistently underperform in environmental metrics, as observed across our panel regressions.

Life-cycle perspectives [11] further clarify the mechanisms underlying this relationship. As directors advance in their careers, they may prioritize stability, legacy, and immediate financial outcomes over long-term strategic investments, including environmental initiatives. Our evidence that older boards are associated with higher direct and indirect greenhouse gas emissions reinforces this argument: older directors appear less proactive in implementing measures that mitigate operational emissions, particularly in areas under their direct control (scope 1 and scope 2). The lack of a significant relationship with downstream emissions (scope 3) aligns with the view that influence over distant or complex parts of the supply chain is limited, regardless of board composition.

The moderation analyses extend these theoretical insights by demonstrating how governance and managerial factors interact with board age. Board co-option exacerbates the negative impact of director age, consistent with the notion that older directors may be more susceptible to CEO influence due to shorter time horizons, risk aversion, and reliance on CEO-provided information. Institutional ownership, by contrast, mitigates the adverse effects of older boards, as external monitoring and long-term investment pressures constrain conservative or short-termist tendencies. Similarly, the presence of a female CEO or a high-ability manager reduces the environmental inertia associated with older directors, suggesting that leadership and managerial competence can counterbalance age-related biases in strategic decision-making.

Our results also contribute to the empirical literature on board demographics and environmental governance. While prior studies have focused primarily on gender, independence, and expertise [3,4,5], relatively few have examined the influence of board age or its interaction with governance structures. By linking director age to both broad environmental performance metrics and operational emissions, our study offers a more granular understanding of how demographic factors shape sustainability outcomes. These findings align with recent research suggesting that boards with heterogeneous characteristics are more effective in addressing complex strategic challenges, including climate risk [7,9].

Finally, the U.S. institutional context provides additional interpretation. U.S. publicly listed firms operate under regulatory frameworks that emphasize environmental disclosure and shareholder accountability, yet the degree of board engagement in environmental strategy varies widely. Our findings imply that in this setting, demographic traits such as age can substantially influence how boards respond to institutional pressures, stakeholder expectations, and emerging sustainability imperatives. This highlights the practical importance of considering director age alongside traditional governance mechanisms when evaluating environmental governance effectiveness.

6. Conclusions

This paper investigates the relationship between director age and corporate environmental performance, employing a large panel dataset of US publicly listed firms and multiple measures of greenhouse gas emissions and environmental scores. We adopt robust methodological approaches, notably, panel regressions with firm and year fixed effects, addressing endogeneity through instrumental variable (two-stage least squares) approaches and the use of lagged data structures, amongst others. Our analysis reveals a consistent and robust negative association between the average age of directors on the board and firms’ environmental performance. Specifically, firms with older boards tend to have lower environmental scores and higher direct and indirect greenhouse gas emissions. These findings hold across a range of specifications, including alternative dependent variables, lagged regressors, additional governance controls, and instrumental variable estimation addressing endogeneity concerns.

By linking board demographics to environmental outcomes, this study contributes to the growing literature on corporate governance and sustainability. Unlike prior research focusing primarily on gender diversity [4,13] or board independence [4], our work highlights the critical but underexplored role of director age as a determinant of environmental stewardship. The cross-sectional analysis further uncovers several moderating factors—such as board co-option, institutional ownership, female CEO presence, and managerial ability—that influence the strength and direction of this relationship. This nuanced understanding enriches theoretical perspectives on how board composition shapes corporate environmental strategy and performance.

From a managerial standpoint, our findings suggest that board renewal and age diversification could serve as strategic levers to enhance environmental accountability. Boards with younger directors may be more open to innovation, responsive to stakeholder concerns about sustainability, and better equipped to integrate environmental risks into corporate strategy. To make this actionable, firms might consider implementing structured succession planning to ensure a steady pipeline of younger directors with expertise in sustainability and emerging technologies. Rather than advocating for wholesale replacement of older directors, boards could adopt staggered refreshment policies that gradually introduce age diversity while retaining valuable experience. Additionally, pairing older directors with younger counterparts through mentorship and cross-committee assignments may reduce resistance to environmental initiatives by fostering intergenerational learning.

For policymakers and regulators, the results underscore the importance of considering demographic attributes in corporate governance codes and sustainability reporting guidelines. Practical measures could include recommending disclosure of board age profiles and turnover policies in annual governance reports, or introducing soft quotas or “comply-or-explain” requirements for age diversity. Regulators might also promote training and certification programs focused on ESG competencies, ensuring that directors of all ages remain equipped to evaluate environmental risks and opportunities. Taken together, these actions provide clearer pathways for firms and regulators to optimize board age structures, mitigating the environmental inertia often associated with older directors while preserving the benefits of boardroom experience.

As is typical in research of this nature, our work has a number of limitations, which open up opportunities for future research. First, this study focuses exclusively on U.S.-listed companies from 1996 to 2018, limiting its generalizability. Different regions or periods may have varying market conditions and regulatory requirements affecting corporate environmental performance. Future studies may explore whether these results hold in other institutional contexts. Secondly, while we have used various strategies to address endogeneity and evidence causality, our results must still be interpreted with caution due to the complexity of factors affecting corporate environmental outcomes. Thirdly, our sample coverage ends in 2018 due to the discontinuation of MSCI Sustainability data after 2019. Future studies may investigate whether our conclusions remain robust in a more recent period, marked by the COVID-19 pandemic and the rapid growth of technology, by utilizing data from other databases. Finally, our sample is based on firms with available environmental performance data. Data for GHG from Trucost is particularly limited. MSCI also does not report environmental scores for several firms, leading to potential selection bias.

In sum, this study provides novel empirical evidence that director age is a significant predictor of environmental outcomes, with important implications for how firms govern sustainability challenges in an era of increasing environmental scrutiny.