Category: 3. Business

28 October 2025

Compared with August 2025:

• median consumer perceptions of inflation over the previous 12 months remained unchanged, as did median expectations for inflation three years ahead and five years ahead, while median inflation expectations for the next 12 months decreased slightly;
• expectations for nominal income growth over the next 12 months were unchanged, while expectations for spending growth over the next 12 months increased;
• expectations for economic growth over the next 12 months and the expected unemployment rate in 12 months’ time were both unchanged;
• expectations for growth in the price of homes over the next 12 months increased, as did expectations for mortgage interest rates 12 months ahead.

Inflation

In September, the median rate of perceived inflation over the previous 12 months remained unchanged at 3.1% for the eighth consecutive month. Median expectations for inflation over the next 12 months decreased to 2.7%, from 2.8% in August. Expectations for inflation three years ahead were unchanged at 2.5%, as were those for five years ahead, which remained unchanged at 2.2%. Uncertainty about inflation expectations over the next 12 months remained unchanged in September. Respondents in lower income quintiles continued to report on average slightly higher inflation perceptions and short-horizon expectations than those in higher income quintiles, a trend observed since 2023. However, the broad evolution of inflation perceptions and expectations remained relatively closely aligned across income groups. Younger respondents (aged 18-34) continued to report lower inflation perceptions and expectations than older respondents (aged 35-54 and 55-70).

Inflation results

Income and consumption

Consumers’ nominal income growth expectations over the next 12 months remained at 1.1% in September, unchanged from August. Perceived nominal spending growth over the previous 12 months decreased to 4.9%, from 5.0% in August. Expected nominal spending growth over the next 12 months increased to 3.5% in September, from 3.3% in August, with respondents in the lowest three income quintiles showing slightly higher spending growth expectations than those in the highest two quintiles.

Income and consumption results

Economic growth and labour market

Economic growth expectations for the next 12 months remained stable in September at -1.2%. Expectations for the unemployment rate 12 months ahead also remained stable, standing at 10.7% in September. As in previous months, lower-income households expected the highest unemployment rate 12 months ahead (12.7%), while higher-income households expected the lowest rate (9.4%). Consumers continued to expect the future unemployment rate to be only slightly higher than the perceived current unemployment rate (10.2%), suggesting a broadly stable labour market outlook.

Economic growth and labour market results

Housing and credit access

Consumers expected the price of their home to increase by 3.5% over the next 12 months, up from 3.4% in August. Compared with previous months, home price growth expectations were more aligned across income categories, standing at 3.5% and 3.4% for the lowest and highest income quintiles respectively. Expectations for mortgage interest rates over the next 12 months increased to 4.6%, from 4.5% in August. As in previous months, lower-income households expected the highest mortgage interest rates 12 months ahead (5.3%), while higher-income households expected the lowest rates (4.0%). The net percentage of households reporting a tightening (relative to those reporting an easing) of access to credit over the previous 12 months declined marginally, while the net percentage of households expecting tighter credit conditions over the next 12 months increased for the third consecutive month.

Housing and credit access results

The microdata underlying the aggregate results are available in the Data and methodological information section of the Consumer Expectations Survey (CES) web page.

The release of the CES results for October is scheduled for 28 November 2025.

For media queries, please contact: Benoit Deeg, tel.: +49 172 1683704.

Notes

• Unless otherwise indicated, the statistics presented in this press release refer to the 2% winsorised mean. For further details, see ECB Consumer Expectations Survey – Guide to the computation of aggregate statistics.
• The CES is a monthly online survey of, currently, around 19,000 adult consumers (i.e. aged 18 or over) from 11 euro area countries: Belgium, Germany, Ireland, Greece, Spain, France, Italy, the Netherlands, Austria, Portugal and Finland. The main aggregate results of the CES are published on the ECB’s website every month. The results are used for policy analysis and complement other data sources used by the ECB.
• Further information about the survey and the data collected is available on the CES web page. Detailed information can also be found in the following two publications: Bańkowska, K. et al., “ECB Consumer Expectations Survey: an overview and first evaluation”, Occasional Paper Series, No 287, ECB, Frankfurt am Main, December 2021; and Georgarakos, D. and Kenny, G., “Household spending and fiscal support during the COVID-19 pandemic: Insights from a new consumer survey”, Journal of Monetary Economics, Vol. 129, Supplement, July 2022, pp. S1-S14.
• The survey results do not represent the views of the ECB’s decision-making bodies or staff.

Introduction

Acute Generalized Exanthematous Pustulosis (AGEP) is a rare but severe dermatologic condition, typically characterized by the sudden onset of widespread sterile pustules on an erythematous and edematous base. Most AGEP cases are drug-induced hypersensitivity reactions, although infections and other immune-related causes have also been reported.¹ Clinically, AGEP presents with rapidly developing pustular eruptions, often within 24–48 hours, accompanied by fever, pruritus, and systemic discomfort. In severe cases, disease progression may lead to skin desquamation, multi-organ involvement, and serious complications, with mortality reported in a minority of patients.^2,3

The risk factors for drug-induced AGEP are multifactorial, including drug class, dosage, duration of exposure, prior sensitization, and individual immunologic susceptibility. Antibiotics and nonsteroidal anti-inflammatory drugs (NSAIDs) are among the most frequently implicated agents, yet the underlying mechanisms remain incompletely understood.⁴ A systematic investigation into pharmacologic triggers and clinical risk factors is therefore warranted to support improved recognition and management.

The FDA Adverse Event Reporting System (FAERS) provides a large-scale pharmacovigilance platform with detailed reports on drug exposure, dosing, concomitant therapies, and clinical outcomes. Leveraging this resource allows the identification of safety signals and the evaluation of drug-specific risks associated with rare but serious adverse events such as AGEP.⁵

Accordingly, this study analyzed FAERS data from 2004 to 2024 to characterize drug-related risk factors for AGEP. We focused on implicated drug categories, patterns of use, and potential interactions associated with disease onset. The findings are expected to provide useful evidence for drug safety monitoring and to facilitate early identification of patients at risk.

Materials and Methods

Data Source and Collection

This real-world, retrospective pharmacovigilance study was conducted using data from the FAERS, covering the period from the first quarter of 2004 to the fourth quarter of 2024. Adverse drug reactions (ADRs) related to AGEP were identified using the Medical Dictionary for Regulatory Activities (MedDRA) terminology, specifically by selecting the preferred term “Acute Generalized Exanthematous Pustulosis.”

The data were retrieved from the FAERS Public Dashboard, a web-based interface that allows users to access adverse event reports submitted by pharmaceutical companies, healthcare professionals, and consumers. Each report contains multiple variables, including the suspected drug’s active ingredient, indication for use, severity of the adverse reaction, date of event onset, patient demographics (eg, age, sex, weight), type and source of the reporter, concomitant medications, geographic location, and any associated literature references. In each report, drugs are assigned different roles in relation to the adverse event (eg, primary suspect, secondary suspect, concomitant, or interacting drug). For this analysis, only cases where the drug was designated as the “primary suspect” for AGEP were included.

Disproportionality analysis was performed using the Reporting Odds Ratio (ROR) and its corresponding 95% confidence interval (CI) to evaluate the strength of association between suspected drugs and AGEP (Tables 1 and 2). Drugs known to be used for the treatment of AGEP were excluded. A potential safety signal was considered present if the lower bound of the 95% CI for the ROR exceeded 1 and the upper bound was greater than 3.⁵

Table 1 Four Fold Table for Measures of disproportionality5

Table 2 Formulas and Threshold Values of ROR5

P-adjust refers to the p-value after Fisher’s exact test and Bonferroni correction. A volcano plot was generated, with -log(p-adjust) on the x-axis and logROR on the y-axis.

Regression Analysis

Patient-level data including sex, age, and body weight were extracted from FAERS reports. Only reports with complete information were included in the analysis. Records with implausible values—defined as age over 120 years or body weight exceeding 400 kg—were excluded.

Univariate analysis was conducted for all suspected drugs. A drug was considered significant if it met the following criteria: a lower bound of the 95% confidence interval (CI) for the reporting odds ratio (ROR) greater than 1, ROR > 100, and an adjusted p-value (p-adjust) < 0.01. Drugs meeting these thresholds were then included in a Least Absolute Shrinkage and Selection Operator (LASSO) regression model for feature selection. Variables selected by the LASSO model, along with relevant patient characteristics, were subsequently included in a multivariate logistic regression to identify potential risk factors associated with drug-induced AGEP.

Results

Baseline Characteristics of TEN

Baseline characteristics of drug-associated AGEP cases are summarized in Table 3 and Figure 1. Between the first quarter of 2004 and the fourth quarter of 2024, a total of 18,214,476 adverse events (AEs) were reported in the FAERS database, among which 6,880 cases were identified as AGEP. Of these, 3,750 cases (54.5%) involved female patients, 2,471 cases (35.9%) involved male patients, and 659 reports (9.6%) lacked gender information. The median age of AGEP patients was 59 years, and the median body weight was 83 kg. Notably, 39.2% (n = 2,699) of reports were submitted by healthcare professionals. France, the United States, and Spain contributed the highest number of AGEP case reports, with 1,977, 1,222, and 326 cases, respectively.

Table 3 Patient Demographics

Figure 1 Overview of reports related to drug-induced AGEP.YEAR: Number of AGEP reports submitted each year.AGE: Distribution of AGEP reports by patient age group.OUTCOME: Clinical outcomes reported in AGEP-related adverse events, Case outcomes were categorized as death (DE), life-threatening (LT), hospitalization (HO), disability (DS), congenital anomaly (CA), required intervention to prevent permanent impairment/damage (RI), and other outcomes (OT).

Drugs Associated with AGEP

A volcano plot was generated to visualize the association between suspected drugs and AGEP (Figure 2). In this plot, the x-axis represents the logarithmic values of the reporting odds ratio (logROR), where a positive x–value indicates that adverse events associated with a given drug are reported more frequently in AGEP cases than in other adverse events. The y-axis represents the negative logarithm of the Bonferroni-adjusted p-value obtained from Fisher’s exact test, with higher values indicating stronger statistical significance. The color of each point reflects the logarithm of the number of case reports, with redder shades indicating a larger number of reports. Therefore, drugs located in the upper right quadrant of the plot are considered to have both strong association signals and high reporting frequency.

Figure 2 Volcano Plot of Drugs Associated with AGEP; ROR, reporting odds ratio; P-adjust, p-value after Bonferroni correction.

A total of 148 drugs were found to be significantly associated with AGEP (see Supplementary Table S1). The top 10 drug classes included: antibiotics (44/148), nonsteroidal anti-inflammatory drugs (NSAIDs; 10/148), antifungals (6/148), antineoplastic agents (6/148), antiepileptics (5/148), calcium channel blockers (5/148), antituberculosis agents (4/148), contrast agents (4/148), glucocorticoids (4/148), and proton pump inhibitors (4/148).

Risk Factors for Drug-Associated AGEP

We performed univariate analysis on suspected drugs that met the following criteria: more than 100 reported AGEP cases, ROR > 1, and both the lower bound of the 95% confidence interval and the Bonferroni-adjusted p-value < 0.01. Drugs that met these thresholds were subsequently included in the Least Absolute Shrinkage and Selection Operator (LASSO) regression model. Seventeen drugs were identified by LASSO (Figure 3) and further analyzed using multivariate logistic regression, incorporating relevant patient characteristics (Table 4).The results indicated that 17 drugs—including pantoprazole, omeprazole, terbinafine, metronidazole, ceftriaxone, and vancomycin—were significantly associated with an increased risk of AGEP. The predictive performance of the final model was evaluated using a receiver operating characteristic (ROC) curve, with an area under the curve (AUC) of 0.69 (Figure 4), indicating moderate discriminatory ability.

Table 4 Results of Multivariate Logistic Regression Analysis

Figure 3 Results of LASSO Regression Analysis,The plot shows the coefficient profiles of 17 drug variables across log(λ) values and the ten-fold cross-validation curve for λ selection using AUC as the metric, with dotted lines indicating the optimal λ values.

Figure 4 ROC Curve for Drug-Induced AP Risk Factors;The red line represents the ROC curve for predicting the outcome, and the gray diagonal line indicates the reference line of no discrimination. The AUC was 0.690, suggesting fair discriminative ability.

The 17 identified risk drugs could be categorized into the following classes: antibiotics (7/17), nonsteroidal anti-inflammatory drugs (NSAIDs; 2/17), antiviral agents (3/17), proton pump inhibitors (2/17), calcium channel blockers (1/17), antifungal agents (1/17), and other (1/17).

Discussion

AGEP is an acute, drug-associated cutaneous reaction characterized by the sudden onset of fever, widespread non-follicular pustular eruptions, and systemic symptoms. While its exact pathogenesis remains incompletely understood, AGEP is believed to involve drug-induced immune activation, primarily through T cell–mediated pathways that drive neutrophilic infiltration in the epidermis.⁶ Clinically, diagnosis relies on the hallmark skin manifestations, patient history, and histopathological confirmation. The cornerstone of management is immediate discontinuation of the culprit drug, along with symptomatic and supportive care. Although most patients recover, a subset may experience severe complications, including exfoliative dermatitis, sepsis, or multi-organ involvement, resulting in poor prognosis and mortality rates reported as high as 30%–40%.⁷

Previous reports have implicated various antibiotics, antiviral agents, and other commonly prescribed drugs in AGEP, but the strength of these associations has often been limited by small sample sizes or potential confounding. False-positive signals remain a concern.⁸ Therefore, large-scale pharmacovigilance studies are warranted to identify credible drug-related risk factors for AGEP.

In this study, we leveraged real-world data from the FAERS database to systematically analyze AGEP-related adverse event reports from 2004 to 2024. A total of 6,880 cases were identified. The majority of patients were female (54.5%) and the median age was 59 years, suggesting that AGEP predominantly affects middle-aged and older adults—a finding consistent with prior literature. This may be due to increased polypharmacy and age-related decline in immune tolerance.⁹ The highest number of reports originated from France, the United States, and Spain, reflecting greater pharmacovigilance activity in these regions.

Using disproportionality analysis and multivariate regression, we identified 148 drugs with significant AGEP signals, and further narrowed this list to 17 high-risk agents, spanning multiple therapeutic classes—antibiotics, NSAIDs, antivirals, proton pump inhibitors, calcium channel blockers, and antifungals.

Among the antibiotics, ceftriaxone had the highest odds ratio (OR = 49.87), highlighting its strong association with AGEP. As a third-generation cephalosporin, ceftriaxone contains a β-lactam ring that may act as a hapten, binding to host proteins and triggering IgE-mediated immediate reactions or T cell–mediated delayed hypersensitivity. Patch testing studies have shown high positivity rates for ceftriaxone among β-lactams, supporting its sensitizing potential.¹⁰ Clinical reports from Turkey and elsewhere have described rapid AGEP onset after ceftriaxone initiation, with symptoms resolving upon discontinuation.¹¹

Metronidazole, a nitroimidazole antibiotic, may induce AGEP via aberrant immune activation. It has been shown to stimulate drug-specific CD4⁺ T cells and enhance neutrophil chemotaxis through IL-8 and GM-CSF secretion, contributing to pustule formation.^12,13 Vancomycin is another well-documented trigger, and recent single-cell immunopathology studies suggest it induces a mixed immune response, including TH17-like effectors, indicating possible therapeutic targets for severe or relapsing cases.^14,15

Amoxicillin and amoxicillin-clavulanate have also been frequently reported. Delayed-type T cell responses to the shared β-lactam ring structure are considered the likely mechanism, and cross-reactivity with other penicillin-class antibiotics has been demonstrated via patch testing.^16,17 Clindamycin is another well-established culprit, with a median symptom resolution time of 9 days after drug withdrawal and supportive care.^18,19 Although ciprofloxacin–related AGEP is rare, its potential mechanism may involve phototoxic metabolites generating free radicals under UV exposure, leading to keratinocyte damage and inflammation.^20,21

Hydroxychloroquine, commonly prescribed for autoimmune diseases, has been implicated in the development of AGEP through mechanisms that may involve enhanced antigen presentation and subsequent T cell sensitization.^22–24 Of particular note, a case reported by Xi’an Jiaotong University Hospital described the successful management of hydroxychloroquine-induced AGEP using secukinumab, an IL-17 pathway inhibitor, highlighting the potential role of IL-17 signaling in the pathogenesis and treatment of this condition.²⁵

Among antiviral agents, levetiracetam, valaciclovir, and aciclovir were identified as risk drugs. Though rare, levetiracetam–related AGEP has been reported, especially when combined with other antiepileptics like valproate.²⁶ Aciclovir and valaciclovir, used for herpesvirus infections, have been implicated in multiple AGEP cases, with rapid resolution post-withdrawal.^27,28

Despite the perceived safety of proton pump inhibitors (PPIs), both pantoprazole and omeprazole were strongly associated with AGEP in this study. Case reports document typical features including pustular eruptions, fever, and leukocytosis, resolving upon discontinuation.^29,30

Among NSAIDs, both ibuprofen and paracetamol were identified. While generally well-tolerated, ibuprofen has been shown to cause AGEP in rare cases.³¹ Though paracetamol is often considered to have low allergenic potential, there are reports of AGEP even in children, with eosinophilia and neutrophilic pustules.³²

In addition, terbinafine, a systemic antifungal, was associated with AGEP, possibly due to its intracellular accumulation in keratinocytes and subsequent T cell–mediated cytotoxicity.^33,34 One case demonstrated progression despite corticosteroids and eventual improvement with adalimumab therapy.³⁵ Lastly, diltiazem, a calcium channel blocker used for hypertension and arrhythmia, has also been reported to cause AGEP with rapid onset and resolution following corticosteroid treatment.³⁶

Study Strengths and Limitations

This study leveraged a large-scale real-world dataset from the FAERS spanning two decades (2004–2024), providing a comprehensive and representative overview of drug-induced AGEP in clinical practice. The robustness and external validity of the findings are supported by the breadth of data and diversity of reporting sources across countries. By integrating multiple statistical methods—including reporting odds ratios, univariate analysis, LASSO regression, and multivariate logistic regression—the study effectively combined signal detection, variable selection, and risk quantification. These complementary approaches enabled us to systematically identify and validate key drug-related risk factors for AGEP. A total of 148 drugs with significant associations were screened, with 17 identified as risk factors, providing clinicians with practical insights for risk-aware prescribing and targeted monitoring.

Despite these strengths, several limitations should be acknowledged. First, the FAERS database is based on spontaneous and voluntary reporting, which may introduce underreporting or selective reporting biases. Second, the completeness and granularity of clinical data in FAERS are often limited; missing values for variables such as age, body weight, and comorbidities could affect the precision and interpretability of the analysis. Third, although multivariate logistic regression was used to control for confounding, residual confounding from unmeasured variables cannot be fully excluded. It is also important to note that disproportionality analysis and FAERS data, by nature, do not allow for the establishment of causality or the estimation of incidence rates.

Conclusion

This study utilized real-world data from the FAERS database between 2004 and 2024 to systematically evaluate potential risk factors for drug-induced AGEP. A range of commonly prescribed medications—including antibiotics, nonsteroidal anti-inflammatory drugs (NSAIDs), antiviral agents, proton pump inhibitors, calcium channel blockers, and antifungal drugs—were found to be significantly associated with AGEP. Notably, ceftriaxone, pantoprazole, hydroxychloroquine, and vancomycin exhibited particularly high odds ratios, underscoring the need for heightened pharmacovigilance and risk assessment during their clinical use. Furthermore, the observation that AGEP was more frequent in younger and middle-aged adults suggests a possible age-related component in disease susceptibility.

Although this study employed large-scale signal detection and multivariate modeling to enhance the robustness of its findings, several limitations remain. These include the passive nature of FAERS reporting, missing clinical data, and the inability to infer causality from spontaneous reports. Therefore, future research should integrate prospective clinical studies and mechanistic investigations to elucidate the immunological basis and individual susceptibility to AGEP. Such efforts will be essential for advancing early recognition, targeted prevention, and safe prescribing practices in clinical pharmacology and dermatovigilance.

Data Sharing Statement

The dataset generated and analyzed during the current study is available from the corresponding authors upon reasonable request: Wanchun Wang or Xiaojian Li.

Ethical Approval

In accordance with Article 32 of the Measures for Ethical Review of Life Science and Medical Research Involving Human Beings issued by the National Science and Technology Ethics Committee of the People’s Republic of China, this study was exempted from ethical review because the data analyzed pose no harm to human participants, do not involve sensitive personal information or commercial interests, and were obtained from open and legally accessible databases.

Acknowledgment

The authors affirm that they have no commercial or financial affiliations that could be perceived as potential conflicts of interest. This includes any relationships or financial interests that could influence or bias the study’s results and conclusions.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Funding

This research was funded by the Technology Innovation Team of Jiangxi University of Traditional Chinese Medicine (CXTD220090), National Natural Science Foundation of China Regional Projects (No. 81960874, 82260935, 82260937), Jiangxi Provincial Natural Science Foundation Key Projects (NO. 20202ACB206010).

Disclosure

The authors declare no conflicts of interest.

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Factors weighing on the food supply chain include not producing enough calories to feed people, not enough land available to cultivate crops, greenhouse gas emissions from food production, lost biodiversity integral to agriculture, and a water shortage for agricultural use, according to Sunny Verghese, CEO of food and ag company Olam Group.

“We need about $4.5 trillion of investment in finding the next breakthroughs to find a sustainable food future,” Verghese said at the Fortune Global Forum in Saudi Arabia on Sunday. “We are not, in this point in time, making that investment.”

Ertharin Cousin, a former U.S. food and agriculture ambassador to the United Nations, thinks enough calories are being produced but said the problem is not enough nutritious calories are out there at the right price.

“There are 2.4 billion people today who can’t afford a diverse and nutritious diet because we don’t grow what is required to support the diet diversity to meet human health as well as to meet the environmental challenges of the food system of today,” she told Fortune’s Matt Heimer.

Despite their warnings, data from the U.S. Department of Agriculture shows that food insecurity will improve this year. 

Its annual Global Food Assessment says per-capita income in 83 low- and middle-income countries will grow by 3.7% this year, while food price inflation in most of the monitored countries is expected to ease.

This means the number of food-insecure people this year is projected to drop by about 221 million people to 604 million people, or 13.5% of the world’s population.

But experts are still concerned about building a sustainable food supply chain for the future.

Verghese estimated the world requires 593 million hectares of land—which is equivalent to twice the size of India—every year for crop cultivation at current productivity growth rates to meet that challenge. 

Cousin, who is CEO of FSF Ventures, a nonprofit focused on sustainable food business models, believes investments like AI will help spur productivity in land already used for cultivation. 

The new tech, when paired with other innovations like biological tools and energy advancements to support production growth, will help create a “diversity of solutions” required to combat the world’s food shortage, she added. These are the investment opportunities Cousin’s organization is seeking.

“We have the responsibility of identifying the investment capital that is necessary to support the multi-sectoral investments that are required from farm to consumer that will change the food system in a way to make it more productive and make that food more affordable and available,” Cousin said.

October 28, 2025

NTT DATA Group Corporation

• White paper shows how AI can help solve the environmental challenges it creates
• Resource efficiency should be designed into systems from the start
• Sustainable AI requires lifecycle thinking and circular-economy principles

TOKYO and LONDON – October 28, 2025 – A new white paper from NTT DATA, a global leader in AI, digital business and technology services, highlights the urgent need to embed sustainability into every layer of AI development and deployment to counteract the technology’s environmental impact. Deploying innovative solutions for sustainable AI is a corporate responsibility and a strategic opportunity to create lasting value, build organizational strength and consume fewer essential resources.

The new paper, Sustainable AI for a Greener Tomorrow, illustrates the growing environmental impact of AI and outlines a path to sustainable innovation. The technology requires enormous volumes of electricity to support surging computational demands to train large language models, run inference pipelines and maintain always-on services. Researchers predict AI workloads will drive more than 50% of data center power consumption by 2028. Other primary environmental impacts include water consumption for data center cooling systems, e-waste and rare earth mineral extraction for hardware production.

“The resource consequences of AI’s rapid growth and adoption are daunting, but the technology also can empower innovative solutions to the environmental problems it creates,” said David Costa, Head of Sustainability Innovation Headquarters, NTT DATA. “AI’s amazing capabilities can help manage energy grids more efficiently, reduce overall emissions, model environmental risks and improve water conservation. It’s vital for organizations to recognize the challenge and build sustainability into AI systems from the start.”

Key Insights

• Expand From Performance to Green Priorities: NTT DATA’s AI experts and sustainability consultants urge the use of holistic sustainability goals, not just conventional AI performance metrics such as accuracy and speed. Efficiency must be prioritized, not as a trade-off, but as a core design principle.
• Quantify Environmental Impact: AI’s energy consumption, carbon emissions and water footprint need standard and verifiable metrics. Industry benchmarks such as the “AI Energy Score” and “Software Carbon Intensity (SCI) for AI” offer ways to embed sustainability into governance, procurement and compliance protocols.
• Lifecycle-Centric Approach: Sustainable AI requires lifecycle thinking, from raw material extraction and hardware production to system deployment and ultimate disposal. Important steps include lengthening hardware lifespans, optimizing cooling systems and applying circular-economy principles.
• Shared Accountability Across the Ecosystem: Responsibility is widely distributed, encompassing hardware manufacturers, data center operators, software developers, cloud providers, policymakers, investors and consumers. Cross-sector cooperation is essential for systemic change.

Barriers and Best Practices

Today, fragmented assessments and inconsistent metrics frequently prevent meaningful benchmarking. Many organizations focus narrowly on energy or emissions without considering water usage, rare material depletion and e-waste. These and other factors must be addressed comprehensively. Even when environmental goals are set, organizations often lack actionable methods to apply sustainability at every stage of the AI lifecycle.

To address these and other concerns, the report outlines numerous best practices, including:

• Applying green software engineering patterns to reduce resource consumption
• Running AI workloads in locations and at times that align with renewable energy availability
• Leveraging remote GPU Services and on-premises AI
• Reducing e-waste by prioritizing modular and upgradable components, and extending hardware lifespans through refurbishment, reuse and responsible recycling

While the road to sustainable AI is complex, an intentional, end-to-end redesign of the AI lifecycle can help fulfill this technology’s positive potential while protecting the environmental systems on which all living things depend.

To help accelerate the transition towards a sustainable future, please visit our website to download the whitepaper and learn more about NTT DATA’s sustainability services.

About NTT DATA

NTT DATA is a $30+ billion business and technology services leader, serving 75% of the Fortune Global 100. We are committed to accelerating client success and positively impacting society through responsible innovation. We are one of the world’s leading AI and digital infrastructure providers, with unmatched capabilities in enterprise-scale AI, cloud, security, connectivity, data centers and application services. Our consulting and industry solutions help organizations and society move confidently and sustainably into the digital future. As a Global Top Employer, we have experts in more than 70 countries. We also offer clients access to a robust ecosystem of innovation centers as well as established and start-up partners. NTT DATA is part of NTT Group, which invests over $3 billion each year in R&D.
Visit us at nttdata.com