The study’s central question sits at the intersection of two research traditions that rarely speak to each other. On one side is the economics of artificial intelligence, a field dominated by questions of productivity, labor demand, and inequality. Landmark work by Daron Acemoglu and colleagues has shown that automation can displace workers, compress wages, and concentrate wealth, while other studies using firm-level data from Taiwan, China, and Europe find that AI adoption raises productivity and can create new, complementary tasks. On the other side is happiness economics, which since Richard Easterlin’s famous 1974 paradox has debated whether economic growth actually improves the human lot. The Delhi researchers brought these threads together by asking whether AI readiness shows up not just in gross domestic product, but in how people evaluate their own lives.

To measure subjective well-being, the authors drew on both of its classical components. The cognitive component, often called life satisfaction or life evaluation, was captured using the Cantril Ladder scores familiar from the World Happiness Report. In those surveys, respondents are asked to imagine a ladder whose top rung, scored 10, represents the best possible life and whose bottom rung, scored 0, represents the worst, and then to place themselves on that scale. National averages are built from roughly 1,000 respondents per country each year, with population-representative weights and three-year averaging used to sharpen the estimates. The affective component, reflecting day-to-day emotions and experiences, was measured alongside it, following the standard practice in the well-being literature established by psychologists such as Ed Diener.

Measuring AI itself posed a subtler challenge, because there is no single dial that reads a nation’s artificial intelligence capacity. The researchers settled on the Government AI Readiness Index produced by Oxford Insights, which assesses how prepared governments are to deploy and govern AI across dimensions such as infrastructure, skills, and institutional capacity. Using a government-focused index has a conceptual advantage for the study’s purpose: it captures policy-relevant readiness rather than private-sector hype, and it is available for a broad cross-section of countries. The 29 economies examined span an enormous development range, from Afghanistan, Bangladesh, Nepal, and Myanmar to Singapore, Japan, Australia, and the United Arab Emirates, with China, India, Indonesia, South Korea’s regional neighbors, and many others in between.

The methodological heart of the paper is its confrontation with endogeneity, the perennial headache of cross-country economics. Richer, better-governed countries are both more AI-ready and happier for many reasons that have nothing to do with AI itself, so a naive regression would overstate the causal role of technology. To address this, the authors employed instrumental-variable two-stage least squares estimation, a technique with roots in the classic work of Sargan, Basmann, Durbin, Wu, and Hausman. In the first stage, the AI readiness variable is predicted from instruments that are correlated with AI preparedness but plausibly uncorrelated with the error term driving happiness; in the second stage, the predicted values are used to estimate the effect on well-being. Specification tests drawn from the Wooldridge econometrics tradition guided the validity of the instruments, giving the positive AI-well-being association a firmer causal footing than ordinary least squares could provide.

The headline result is striking: even after controlling for a battery of macroeconomic variables, AI readiness retains a positive and statistically significant effect on subjective well-being across the 29 economies over 2019 to 2024. This is not simply a story of AI making countries richer and richer countries happier. The multivariate framework allowed the authors to probe how AI interacts with the traditional drivers of well-being, and they identified four mediating channels: economic growth, unemployment, health expenditures, and climate change. In other words, part of AI’s happiness effect flows through faster growth, part through labor-market conditions, part through expanded health spending, and part through environmental outcomes, consistent with a growing literature on AI applications in water treatment, environmental sustainability, and climate mitigation.

Each of these mediating channels carries its own tension. The unemployment channel is the most contested. Reviews of the AI-employment literature, including work by Virgelio and colleagues and econometric analyses by Mutascu, Guliyev, and Masoud, reach mixed conclusions about whether intelligent technologies destroy more jobs than they create. The OECD’s surveys of employers and workers document real anxieties about workplace AI, and studies of industrial robots in China find that public trust moderates whether automation translates into happiness at all. Yet the task-based framework of Acemoglu and Restrepo suggests that automation also creates new tasks in which human labor holds comparative advantage, and firm-level evidence from Taiwan and China shows AI-adopting firms expanding rather than contracting. The new study’s finding that AI readiness ultimately supports well-being suggests that, in the Asian context over this period, the positive channels outweighed the displacement effects.

The health channel is less ambiguous. A rapidly growing body of work documents AI’s contributions to medical diagnostics, from explainable deep-learning models for ocular disease classification to AI applications deployed during the COVID-19 pandemic and reviews of AI in mental health and positive psychology. Studies linking artificial intelligence, health expenditure, and digital financial inclusion to life expectancy reinforce the plausible pathway: AI-ready governments can deliver better health services, and better health is one of the most robust predictors of life satisfaction in the happiness literature. The climate channel cuts both ways, since AI carries a substantial energy footprint even as it offers powerful tools for optimizing energy systems, modeling climate risk, and automating environmental monitoring, a duality explored in depth by Cowls, Taddeo, Floridi, and colleagues.

What makes the study timely is the policy moment in which it lands. Governments across the Asia-Pacific, from APEC member economies drafting AI governance frameworks to national programs in India, Singapore, China, and the Gulf states, are racing to build AI capacity. The authors’ recommendation is direct: greater national preparedness for AI is expected to benefit the overall well-being of individuals in these economies, so investment in readiness is worthwhile on happiness grounds alone, not merely on competitiveness grounds. The finding also speaks to the developing-world dimension of the AI debate. Research on generative AI in developing countries suggests these technologies could accelerate development goals, and the new evidence indicates that the well-being stakes of that transition are real and measurable.

Caveats remain, as the authors themselves acknowledge through their careful framing. The country sample and time period were governed by data availability, the observation count is modest by the standards of panel econometrics, and the 2019 to 2024 window includes the COVID-19 pandemic, which disrupted both labor markets and self-reported well-being worldwide. Subjective well-being scores also vary in sample composition from year to year, and the Government AI Readiness Index measures preparedness rather than actual deployment, leaving open the question of whether realized AI adoption would show the same relationship. Still, the study marks a meaningful step: it moves the conversation about artificial intelligence beyond productivity statistics and job-loss headlines into the territory where economists have long measured what actually matters to people, and it finds that, across a diverse stretch of the Asia-Pacific, societies that prepare for AI appear to grow happier as they do so.

Subject of Research: The relationship between national artificial intelligence readiness and subjective well-being in Asia-Pacific economies

Article Title: Understanding the AI and subjective well-being relationship: the influence of macroeconomic factors in Asian economies

Article References: Khandelwal Garg, N., & Kher, G. (2026). Understanding the AI and subjective well-being relationship: the influence of macroeconomic factors in Asian economies. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03347-5

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03347-5

Keywords: artificial intelligence, subjective well-being, happiness economics, Asia-Pacific economies, Government AI Readiness Index, IV-2SLS regression, economic growth, unemployment, health expenditure, climate change, Cantril Ladder, endogeneity

The study focuses on a balanced panel of 972 non-financial listed firms drawn from the United States, China, Germany, Japan, and India, tracked over the period from 2020 to 2024. This five-country design is deliberate. By spanning advanced economies with mature regulatory frameworks and disclosure regimes, such as the United States, Germany, and Japan, alongside emerging economies with different institutional pressures, such as China and India, the authors can ask whether the governance–emissions relationship is a universal phenomenon or one that depends on where a firm happens to operate. The answer, according to the paper, is that certain board characteristics appear to matter for carbon performance across this international institutional spectrum, providing what the authors describe as novel cross-country evidence on how board structures influence corporate carbon mitigation.

The headline findings concern three governance attributes in particular. Boards with greater gender diversity are associated with lower carbon emissions intensity and improved carbon emissions performance. So are boards with a higher proportion of independent directors, and boards that meet more frequently. A fourth attribute, CEO non-duality—meaning the roles of chief executive officer and chairperson of the board are held by different people rather than concentrated in a single individual—is likewise associated with better carbon emissions performance. In contrast, one widely discussed governance lever appears to do little for the climate: board size. Larger boards, the study finds, do not translate into improved carbon performance, a result that challenges the assumption that simply adding directors gives a firm more collective capacity to oversee environmental strategy.

Each of these mechanisms has a plausible logic rooted in corporate governance theory. Gender-diverse boards bring a wider range of perspectives, risk sensitivities, and stakeholder orientations into strategic deliberation, and a substantial body of prior research has linked diverse leadership to stronger environmental and social outcomes. Independent directors, who are not embedded in the firm’s management hierarchy, are theorized to monitor executives more effectively and to push for long-horizon investments—including decarbonization—that a management-dominated board might defer. Meeting frequency captures the intensity of board engagement: boards that convene more often have more opportunities to scrutinize emissions data, question management on climate targets, and oversee the capital allocation decisions that determine whether sustainability pledges become reality. CEO non-duality, meanwhile, addresses the fundamental agency problem of concentrated power. When one person both runs the company and chairs the body meant to supervise that person, oversight weakens; separating the roles creates an independent check that can extend to environmental accountability as well as financial performance.

What distinguishes this study methodologically is the battery of econometric techniques the authors deploy to make sure the correlations they report are not statistical mirages. The core analysis uses fixed effects models estimated with Driscoll–Kraay standard errors, an approach designed for panel data in which observations may be cross-sectionally dependent—in plain terms, when shocks or trends affecting firms in one country or industry spill over to others, as is clearly the case with global carbon regulation, energy prices, and the pandemic-era disruptions that shaped the 2020–2024 window. Driscoll–Kraay standard errors are robust to heteroskedasticity, serial correlation, and spatial dependence, making them well suited to a dataset in which firms across five economies face common global forces. The authors complement this with pooled ordinary least squares regressions that include industry, year, and country dummy variables, allowing them to absorb the average differences between sectors, calendar years, and national institutional environments, and to check that the governance effects survive once those broad confounders are controlled.

Perhaps most importantly, the study also employs two-stage least squares estimation, a standard instrumental-variable technique for addressing endogeneity. Endogeneity is the central headache in this literature: it is entirely possible that firms which are already committed to cutting emissions also choose to diversify their boards, appoint more independent directors, and separate the CEO and chair roles, rather than the governance arrangements causing the emissions improvements. Reverse causality, omitted variables, and self-selection can all masquerade as a governance effect. By using 2SLS to isolate variation in governance that is plausibly exogenous to carbon performance, the authors strengthen the causal interpretation of their results, moving the evidence closer to the claim that boardroom structure genuinely shapes emissions outcomes rather than merely co-moving with them.

The choice of carbon emissions intensity as the key outcome variable is also significant. Rather than measuring absolute emissions, which mechanically rise and fall with firm size and economic activity, emissions intensity expresses emissions relative to output, typically revenue. This normalization allows a fair comparison between a giant utility and a mid-sized manufacturer, and it captures what climate policy analysts often care most about: how efficiently a firm produces value per unit of carbon released. The finding that gender diversity, board independence, meeting frequency, and CEO non-duality are associated with lower intensity and better overall emissions performance suggests these governance features influence the underlying carbon efficiency of the business, not merely its scale.

The implications ripple outward in several directions. For investors, the results feed directly into the rapidly growing field of environmental, social, and governance investing. If board composition is a statistically reliable predictor of carbon performance, then governance metrics that are already disclosed in annual reports and proxy statements become cheap, early signals of a firm’s likely decarbonization trajectory—signals that can be incorporated into portfolio screens and engagement strategies well ahead of emissions data, which often arrives with a lag and uneven quality across markets. For regulators and standard-setters, the study speaks to ongoing debates about board mandates, including gender quotas and independence requirements, by suggesting that such rules may carry climate dividends alongside their governance rationale. And for boards themselves, the findings offer a checklist of internal levers: recruit for diversity, protect director independence, meet often enough to exercise real oversight, and keep the CEO and chair roles separate.

The study also carries a cautionary note about the limits of governance reform. The null result for board size is a reminder that structural tinkering is not automatically consequential. A larger board can dilute accountability, slow decision-making, and fragment responsibility, offsetting any gains from added expertise. What appears to matter is not the number of directors but the quality and independence of their oversight and the diversity of perspectives they bring. This nuance matters for policymakers in emerging economies in particular, where corporate governance codes are still evolving and where the temptation to copy formal structures without the underlying substance—independent minds, genuine deliberation, real accountability—can produce compliance on paper without change in practice. The fact that the beneficial governance attributes hold across both advanced and emerging economies in the sample suggests the mechanisms are portable, but only if implemented meaningfully.

Set against the broader climate challenge, the research lands at a moment when corporate emissions disclosure is becoming mandatory in major jurisdictions and when the gap between net-zero pledges and actual emissions reductions has drawn intense scrutiny. Firm-level governance is one of the few levers that operates continuously, inside the organization, shaping decisions long before they appear in a sustainability report. By demonstrating, with a large panel, multiple robust estimators, and a design spanning the world’s biggest economies, that specific and fixable boardroom characteristics track with lower carbon intensity, the study gives both advocates and skeptics of governance reform something concrete to argue about. The five-year window, ending in 2024, captures a period of unprecedented climate policy momentum and disclosure reform, and the authors’ framework provides a template that future research can extend to more countries, longer horizons, and finer-grained measures of board behavior. For now, the message is strikingly simple: the composition and conduct of the boardroom may be among the most consequential climate decisions a corporation makes.

Subject of Research: The relationship between corporate governance mechanisms and carbon emissions performance in advanced and emerging economies

Article Title: The relationship between corporate governance mechanisms and carbon emissions performance in advanced and emerging economies

Article References: Kanojia, S., Jain Goel, S., & Jhawar, N. (2026). The relationship between corporate governance mechanisms and carbon emissions performance in advanced and emerging economies. Discover Sustainability. https://doi.org/10.1007/s43621-026-04883-2

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04883-2

Keywords: corporate governance, carbon emissions performance, carbon intensity, board diversity, board independence, CEO duality, climate change, greenhouse gas emissions, emerging economies, panel data, endogeneity, ESG investing