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Labour requirements for healthy and sustainable diets at global, regional, and national levels: a modelling study
No full textBackground
Major changes in diets and food systems will be required to limit climate change and meet the Sustainable Development Goals, while providing healthy diets to a growing population. Among others, these changes include what foods are being produced and where, which has implications for the quarter of labourers currently employed in agriculture globally. We estimated the labour requirements for agricultural (primary) production associated with healthy and sustainable diets at global, regional, and national levels.
Methods
We constructed an inventory of agricultural labour requirements per food and region based on farm-level estimates and paired it with a set of diet and food-system scenarios. The scenarios included changes to a set of healthy and sustainable dietary patterns, including flexitarian, pescatarian, vegetarian, and vegan dietary patterns. We combined the inventory of labour requirements with a biophysical input–output model of the global food system to trace how changes in food consumption would affect changes in food production and the associated labour requirements for 20 food groups in 179 countries.
Findings
We found that transitions towards healthy and sustainable food systems could lead to substantial changes in the amount and distribution of agricultural labour. Compared with estimates of food demand in 2030 under a business-as-usual scenario, adopting more plant-based dietary patterns was associated with global reductions in labour requirements ranging from 5% for flexitarian and pescatarian diets to 22–28% for vegetarian and vegan diets. Reductions were strongest in countries currently dominated by livestock production, but a quarter to half of countries showed increased labour requirements to meet increased horticultural demand for fruits and vegetables. The changes in labour requirements were associated with global reductions in labour costs of 0·2–0·6% of gross domestic product annually.
Interpretation
Consistent strategies and political support will be needed to enable just transitions both into and out of agricultural labour
Forecasting Natural Gas Prices in Real Time
No full textThis paper provides a comprehensive analysis of the forecastability of the real price of natural gas in the United States at the monthly frequency considering a universe of models that differ in complexity and economic content. We find that considerable reductions in mean‐squared prediction error relative to a no‐change benchmark can be achieved in real time for horizons of up to 2 years. A particularly promising model is a vector autoregressive (VAR) model that includes the fundamental determinants of supply and demand for natural gas. To capture real‐time data constraints of these and other predictors, we assemble a rich database of historical vintages from multiple sources. We also compare our model‐based forecasts to model‐free forecasts provided by experts and futures markets. Given that no single forecasting method dominates, we show that combining forecasts from individual models selected in real time using the model confidence set as a novel criterion for dynamic model selection delivers the most accurate forecasts
Устойчивая водохозяйственная инфраструктура для водной безопасности населенных пунктов, их населения и объектов экономики (Sustainable and resilient water infrastructure for water security of communities, their population, and economic assets)
No full textMany water infrastructure systems are underdeveloped and are not always capable of ensuring water, food, or energy security, and may not be resilient to natural and man-made risks. This can be attributed to both managerial and financial reasons.
While the distribution of responsibilities for municipal water supply and wastewater disposal has been well studied, these issues remain insufficiently explored for other types of water systems critical to communities. These include multi-purpose water infrastructure (MPWI), rural water supply, and systems for protecting settlements and economic assets from the negative impacts of water (such as avalanches, mudflows, landslides, collector-drainage systems, and stormwater sewers).The methodological basis for addressing these questions is drawn from research on national water security indicators based on the “nexus” concept; the approaches and recommendations of authoritative international organizations, including the Global Water Partnership, UNECE, and the OECD (e.g., the “3Ts” concept and recommendations on private sector participation); as well as the authors’ analysis of the specific benefits provided by water infrastructure and the experiences of several countries. The article presents the author's vision of options for sustainable, systemic solutions to these issues. It proposes an appropriate distribution of responsibility among different levels of public authorities and other economic agents for financing the capital and operating costs of various types of water infrastructure
Morbidity changes induced by future air quality and demographic structure changes
No full textClimate change mitigation policies can enhance health by improving air quality. Previous studies have evaluated historical years lived with disability (YLDs) for cardiovascular and respiratory diseases attributable to particulate matter (PM2.5). However, the impact of dementia, which can significantly affect YLDs, has not been thoroughly examined in the context of climate change mitigation. In this study, we estimated global YLDs attributable to PM2.5 using health impact assessment models and PM2.5 concentrations simulated by a global chemical transport model under two scenarios: with and without climate change mitigation. To address appropriately the issue of dementia, we explicitly considered future demographic patterns, particularly the aging population. YLDs are projected to increase globally by 2100 in both scenarios due to global aging, increasing from 7.1 million years in 2015 to 18 million years without mitigation and to 12.5 million years with it. Mitigation measures could reduce global YLDs by 5.33 million years, by 2100, limiting the increase from 2.5 times to 1.8 times. Although mitigation measures can reduce the health impacts attributable to PM2.5, the role of population aging remains critical for the future
Skills-in-Literacy Adjusted Human Capital Dataset (SLAMYS)
No full textThe dataset of global Skills-in-Literacy Adjusted Mean Years of Schooling (SLAMYS) provides the indicator for 185 countries, by gender and three broad age groups (20-64; 20-39; 40-64) presented in five-year steps from 1970 to 2025. This dataset is an extention and update of Lutz et al. (2021) which included estimates until 2020 and for working age population (age 20-64) only. This new dataset allows for more nuanced analyses of gender-specific trends and generational shifts in skill formation, with particular attention to younger adult populations. The dataset is based on more up to date survey data, including the most recent OECD’s Programme for the International Assessment of Adult Competencies Cycle 2 data (PIAAC, 2023), most recent the Demographic and Health Survey (DHS), and Multiple Indicator Cluster Surveys (MICS). It also uses more recent mean years of schooling (MYS) which are sourced from the most recent Wittgenstein Centre Human Capital Data Explorer, version 3 (K. C. et al., 2024; , https://dataexplorer.wittgensteincentre.org/wcde-v3). Estimates for 2020 and 2025 correspond to the medium scenario (SSP2) of the 2023 update (v15) of the Wittgenstein Centre’s Human Capital Projections (K.C. et al 2024). MYS values for the period 1970–2015 are based on a historical reconstruction (KC et al. 2025) that is fully consistent with the SSP2 scenario. Additionally, estimates of educational attainment distributions by sex and age for all 185 countries—used as covariates in the prediction models—are drawn from the same sources and are fully aligned with the MYS values. See the attached technical documentation for more details.
The dataset contains output data files including technical variables (MYS, SAFs) and a technical documentation. The documentation describes calculation steps, data structures, and includes illustrative examples to guide the interpretation of the main output variables.
This dataset consists of the following files:
Dataset: SLAMYS_2025_v1.csv
The csv file includes the following variables:
country_code (3-numeric ISO code, UN standard)
country_name
year (year in five year steps, 1970-2025)
age_group (20-64, 20-39, 40-64
Assessing the success of a horizon scanning approach in predicting invasive non‐native species arrival
No full textDespite increasing awareness of invasive non‐native species (INNS) and enhanced biosecurity controls in many countries, INNS are still arriving and establishing in new destinations, remaining a globally acknowledged threat to native biodiversity. Preventing the introduction of INNS, as opposed to controlling them once they have arrived, is recognised as the most effective approach to their management. Horizon scanning represents one of the key tools to identify high‐risk INNS that have yet to arrive within a region and has been applied in many contexts around the world, but to date there have been no studies that systematically assess the effectiveness of this approach.
Here, we revisit the horizon scan for Great Britain conducted in 2013 that assessed the likelihood of high‐risk INNS arriving within the next 10 years, establishing and having an impact on biodiversity and ecosystems. We evaluated the success of this exercise in predicting arrival of these species within the subsequent 10 years.
Ninety‐two species were shortlisted in the 2013 horizon scan. In total, 31 of the 92 species identified in the 2013 horizon scan had arrived by 2023. We found that 12 of the top 20 species had arrived within 10 years. In predicting arrival, there was a significant effect of species having arrived previously to Great Britain, and the number of countries in Western Europe and Baltic countries in which an INNS was found prior to 2013.
Policy implications : We conclude that horizon scanning provides a rapid, affordable and successful mechanism to predict the arrival of high‐risk INNS. We highlight the importance of citizen science, including biological recording, and of local expertise for detecting and documenting arrival of INNS. We discuss knowledge gaps that could help inform and improve future horizon scanning. In addition, we recommend regularly repeating horizon scanning exercises to support biosecurity and awareness raising for INNS
Disaster impact forecasting framework for multi hazard disaster risk assessment
No full textDisaster risk management relies largely on the estimated disaster impact under different scenarios, as well as the estimation of the disaster probability itself. The results of such disaster risk estimation models are inputs to disaster risk management strategy building and decision-making. Decision-makers need an estimation of the risk metrics under each scenario to determine the best combination of strategies for managing disaster risk. Various studies provided models for estimating the probabilistic behavior of the disastrous event. However, the vast diversity of risk metrics and risk drivers poses a challenge in forecasting the disaster impact and consequently, the disaster risk. Furthermore, disaster risk drivers (e.g., public trust, social vulnerability, socio-economic variables, climate change) have a dynamic nature, and it is crucial to consider their dynamic structure when estimating the disaster impact. In other words, the impact should be estimated considering the interactions among the risk metrics as well as adapting to the changes in risk drivers. Furthermore, the comprehensive risk assessment relies on the distribution estimation of the disaster impacts. The purpose is to formulate the problem of the disaster impact estimation in relation to the disaster risk assessment and decision-making, and propose a framework for forecasting the disaster impact. The proposed solution extends the commonly used value at risk (VaR) concept for disaster impact forecasting
The global macroeconomic burden of diabetes mellitus
No full textDiabetes mellitus poses a substantial and rising global health and economic burden, affecting more than one in ten adults worldwide. Using a health-augmented macroeconomic model across 204 countries and territories, we estimated the economic impact of diabetes from 2020 to 2050, incorporating losses in effective labor supply due to mortality and morbidity, treatment-related resource diversion and informal caregiving costs. Without informal care, the global burden amounts to )), equivalent to 0.22% of annual global gross domestic product. Including informal care, the burden increases dramatically to INT5.5 trillion to INT$152.1 trillion, depending on the assumptions for care. The absolute costs are highest in the United States, China and India, while relative and per capita burdens are greatest in countries such as American Samoa and Australia. These findings highlight the uneven distribution of diabetes’ economic impact and underscore the urgent need for effective global interventions
Addressing the Exception Prioritization Problem in Continuous Auditing Systems With Thresholding
No full textContinuous auditing research has grappled with the challenge of managing the abundance of detected exceptions in internal audit applications for the past 30 years. A key issue in continuous auditing involves the uncontrolled proliferation of exceptions, where the sheer volume makes manual follow‐up impractical, undermining the viability of the technology. The root cause of this problem is the combination of strong class imbalance and the predominant rule‐based systems design. Prior investigations have attempted ad hoc remedies like introducing additional layers to prioritize the most suspicious exceptions or aggregating data. Currently, there is no universal method to address this prioritization challenge, leaving internal auditors without a means to focus specifically on exceptions most likely to represent genuine faults. Our research explores the origin of this prioritization dilemma and proposes a systems design that can deal appropriately with class imbalance. This solution allows full control of the exception volume by a simple approach in machine learning called thresholding and combined with methods to interpret the output of a continuous auditing system our design effectively focuses the internal auditors' attention on the most significant exceptions. We discuss the implications of thresholding for practice and the literature