World Resources Institute

Abstract. Global estimates of anthropogenic land use CO2 fluxes from bookkeeping models and dynamic global vegetation models, as well as those derived from Earth observations (EO), can differ widely from those obtained by aggregation of national GHG inventories (NGHGI). These discrepancies are mostly due to conceptual differences, reflecting varying definitions (or system boundaries) of anthropogenic CO2 fluxes, particularly on the forest carbon sink. This study presents a new online platform ‒ the "LULUCF Data Hub" ‒ which compiles various datasets of country-level CO2 emissions and removals from land use, land-use change and forestry (LULUCF). Through a multi-disciplinary collaboration, results from two key global initiatives ‒ one based on carbon cycle modelling (Global Carbon Budget, GCB 2024) and one on Earth Observations (Global Forest Watch, GFW 2024) ‒ are "translated" to conceptually align with NGHGI definitions. We compared these global approaches to the 2024 NGHGI data submitted to the UNFCCC under the Paris Agreement reporting rules (cut-off date April 2025). The aim is to enhance comparability and foster consensus on anthropogenic land-use CO2 flux estimates. Translating definitions between datasets considerably improves the match in land use CO2 flux estimates, both globally and for most countries. At the global level, NGHGI indicate that LULUCF is a net sink of -3.4 Gt CO2 yr-1 (2001–2023 average; 2023 estimate is an extrapolation for all NGHGI), whereas GCB reports a net source of +4.7 Gt CO2 yr-1. This +8.1 Gt CO2 yr-1 discrepancy drops to +1.8 Gt CO2 yr-1 when GCB fluxes are translated to NGHGI definitions. When the global net forest-related flux of -5.8 Gt CO2 yr-1 estimated by the GFW model (sum of fluxes from Forest and Deforestation including organic soils) is limited to a proxy of managed forest area, the sink declines to -3.5 Gt CO2 yr-1, closely aligning with NGHGI (-3.2 Gt CO2 yr-1 for forest-related flux only, including Forest, Deforestation, and organic soils in Indonesia). Despite improved alignment in magnitude after translation, some differences persist in global CO2 flux trends: in the last two decades, forests (including re/afforestation) have remained a roughly constant net sink in NGHGI and GCB but are a declining net sink in GFW; deforestation emissions are stable or slightly declining in NGHGI and GCB but increase in GFW. For other land uses, NGHGI globally reports a greater sink than GCB (the GFW model does not include other land uses). At the country-level, we identify both convergences and divergences in CO2 flux estimates between NGHGI and the translated datasets – in sign, magnitude and trend. Notably, after translation, estimates of both GCB and GFW still differ from individual NGHGI values in 19 countries by more than ±0.1 Gt CO2 yr-1 on average (2001–2023), highlighting areas that deserve further investigation. The LULUCF Data Hub was first presented in July 2024 during the IPCC Expert Meeting on Reconciling Anthropogenic Land Use Emissions, showing preliminary data. In November 2024 version 1.0 (with NGHGI pre-Paris reporting, GCB 2024 data and GFW 2024 data) was launched. Our study shows results from version 2.0, corresponding to 2024 data from all datasets: i) NGHGI data from the first Biennial Transparency Reports (BTRs) submitted under the Paris Agreement (cut-off date April 2025), complemented by previous reports (from version 1.0) for countries with no BTR submission, ii) data from GCB 2024 and iii) data from the GFW carbon flux model 2024. Data from all versions, including future annual updates, can be visualized and downloaded from the LULUCF Data Hub hosted by the European Union Forest Observatory (European Commission Joint Research Centre, 2025) and from the online repository (Melo et al., 2025; available at https://doi.org/10.5281/zenodo.17140775).

ABSTRACTThe Congo Basin and its contiguous forests harbor globally significant carbon stocks, estimated at 65 gigatons of C (GtC) above and belowground. Despite rising temperatures and intensifying droughts, they have remained a carbon sink, albeit weak: 0.26–0.50 GtC yr.−1 carbon uptake since 1980. However, these forests' carbon stocks and fluxes, including gross primary productivity, respiration, net primary productivity, and riverine carbon transport, remain poorly quantified. This limits understanding of the region's role in the global carbon cycle, its vulnerability to environmental change, and its potential as a long‐term carbon sink. We review and quantify Congo Basin and contiguous forest carbon stocks and fluxes and synthesize the current knowledge on how key global change drivers shape the region's carbon cycle. We find limited responses to long‐term precipitation variability, but declining stocks and fluxes in response to long‐term and increasing temperature and drought frequency. Land cover and land use changes, largely driven by small‐scale agriculture, logging, and agro‐industrial expansion, reduce carbon stocks, ecosystem structure and functioning, and animal‐mediated ecosystem services. In contrast, large‐scale savanna biomass burning delivers phosphorus and nitrogen to Congo Basin forests via cross‐equatorial winds, providing additional nutrients and supporting carbon sequestration. In situ studies suggest that CO2 fertilization has increased intrinsic water‐use efficiency, although its effects may be modulated by climate change, and its impacts on biomass accumulation remain uncertain. Legacy effects from historical land use and climate change likely shaped present‐day vegetation structure, yet their relative influence is unclear. High‐resolution carbon monitoring, improved remote sensing, and strengthened in situ measurement networks are needed to quantify the impacts of these key drivers and their interactions on the Central African carbon cycle. This is needed to inform conservation strategies and advance understanding of the region's future as a carbon sink under global change pressures.

Abstract Zomato announced the acquisition of Blinkit on June 24, 2022, for a total price of $568 million (Rs. 4447 crore) in equity. The company's valuation was reportedly around $800 million before the acquisition. Shareholders of Blinkit will receive one Zomato share for every ten Blinkit shares. Zomato made a $100 million investment in Blinkit (formerly known as Grofers) earlier in March 2022 in exchange for a stake of over 10%.According to the deal terms, Blinkit's largest shareholder Soft Bank will receive 28.71 crore shares of Zomato, Tiger Global will take home 12.34 crore shares, BCCL will get 1.5 crore shares, and South Korean investor DAOL will get 3.66 crore shares of the food delivery major.The success in quick commerce is heavily dependent on an efficient hyperlocal delivery network. Zomato has a natural advantage here given the millions of food orders that it deliver. The acquisition marks a signiant milestone in the quick delivery start-up industry.This acquisition paves a milestone in quick delivery start-up system.The case raises an important strategic question: should bigger companies buy lossmaking company for long term financial gains? Will you do the same for your business too?

India’s power system is facing growing challenges of ramping due to the increasing share of variable renewable energy (VRE) sources such as solar and wind. As net load profiles become steeper and more volatile, particularly during solar generation drops due to cloud cover and evening peaks, the ability to manage ramping, defined as the rate of change in generation output, has become critical in maintaining grid stability. A comprehensive analysis of state-wise ramping behaviour using hourly data for the year 2023 reveals that opposing ramp directions between states occurred in 30%-50% of time blocks, challenging the net national ramp and complicating system-level balancing. Despite these challenges, India possesses sufficient ramping capability, particularly from thermal generators, but lacks a mechanism to harmonize ramping efforts of generators across state as well as regions. The paper proposes a layered Security-Constrained Economic Dispatch (SCED) framework, which combines intrastate optimization with a national-level coupling layer to align state-level ramp vectors without disrupting existing contractual structures. The paper also includes the recommendations e.g. regulatory recognition of ramping, improved data transparency, and incentivization of flexible generation to support integration of renewables reliably in a cost-effective manner.

The self-reference effect (SRE) describes enhanced memory and perception for self-related information. Using a perceptual matching paradigm across three experiments, this study examined the SRE and two associated phenomena in a Chinese sample: self-retention (difficulty updating self-associations) and self-obstruction (interference from self-cues). Study 1 established the basic SRE, revealing comparable advantages for self and friend-related information. Study 2 demonstrated self-retention through performance costs during rule-switching, while Study 3 identified self-obstruction via distraction from task-irrelevant self-cues. A 15-minute mindfulness intervention attenuated all three effects by enhancing processing efficiency for stranger-related stimuli rather than suppressing self-prioritization. These findings suggest mindfulness mitigates SRE through attentional reorientation from self to others, offering new insights into modifiable mechanisms of self-referential processing.

"The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest".In the published article, there was a slight mistake. The title was displayed as: [Tropical ecosystem multifunctionality assessment and insights for sustainable land management: a systematic literature review using the driver-pressure-state-impact-responses framework]. The correct title of the article is [Tropical ecosystem multifunctionality assessment and insights for sustainable land management: a systematic literature review using the Drivers-Pressures-State-Impacts-Responses framework].The original version of this article has been updated.

This best practice brief presents Component 2 of the Data-to-Deal (D2D) framework which addresses Capacity. This component focuses on strengthening national capacity to conduct robust and inclusive planning processes for energy and transport that unlock climate-aligned investment in low-and middle-income countries (LMICs). Many countries face persistent gaps in technical expertise, institutional coordination, and long-term continuity for planning. Component 2 comprises three sub-components: (i) Assessing needs for capacity strengthening, (ii) Implementing targeted capacity development programmes, and (iii) Sustaining the gains in institutional capacity. The brief recommends demand-driven, iterative approaches that embed capacity within national institutions, prioritise trust-building and knowledge exchange across national institutions, and align closely with country-specific needs. Drawing on available diagnostic tools and recent country experiences, the brief outlines how ministries and planning agencies can select appropriate modelling toolkits, adopt standardised data practices, and integrate training into institutional structures. By operationalising this approach, countries can create a strong institutional foundation from which to advance the technical tasks associated with investment planning and finance.

<strong class="journal-contentHeaderColor">Abstract.</strong> High-resolution crop maps over large spatial extents are fundamental to many agricultural applications; however, generating high-quality crop maps consistently across space and time remains a challenge. In this study, we improved a workflow for crop mapping and developed the first openly available, annual, 10-m spatial resolution maize and soybean maps over the Contiguous United States (CONUS) from 2019 to 2022, available at the Global Land Analysis and Discovery at the University of Maryland (<a href="https://glad.umd.edu/projects/mapping-crops-10-m-resolution-united-states" target="_blank" rel="noopener">https://glad.umd.edu/projects/mapping-crops-10-m-resolution-united-states</a>). We obtained all available Sentinel-2 surface reflectance data between May and October for every year, applied quality assurance, corrected the bidirectional reflectance distribution function (BRDF) effects, and generated 10-day analysis ready data (ARD) composites. We then derived multi-temporal metrics from the 10-day ARD as training features for the national-scale wall-to-wall mapping. We implemented a stratified, two-stage cluster sampling, and then conducted annual field surveys and collected ground data. Utilizing the training data with Sentinel-2 multi-temporal metrics and topographic factors, we trained random forest models generalized for annual maize and soybean classification separately. Validated using field data from the two-stage cluster sample, our annual maps achieved consistent overall accuracies (OA) greater than 95 % with standard errors of less than 1 %. User&rsquo;s accuracies (UAs) and producer&rsquo;s accuracies (PAs) for maize were higher than 91 % and 84 % across the years, and UAs and PAs for soybean were greater than 88 % and 82 %, respectively. To illustrate the substantial improvement of the 10-m map over existing datasets, e.g., the 30-m Cropland Data Layer (CDL), we aggregated the 10-m maps to 30-m spatial resolution and quantified the amount of 30-m mixed pixels that can be reduced at field, regional, and national levels. The counties with the most maize and soybean production in Iowa, Illinois and Nebraska had the lowest reduction in mixed pixels, ranging from 1 % to 10 %, whereas southern counties had a higher reduction in mixed pixels. Overall, the median percentages of mixed maize and soybean pixels reduction across all counties were 14 % and 16 %, respectively. With more Sentinel-2-like data available from continuous observations and incoming satellite missions, we anticipate that 10-m crop maps will greatly benefit long-term monitoring for agricultural practices from the field to global scales. The dataset is also available at <a href="https://doi.org/10.6084/m9.figshare.28934993.v1" target="_blank" rel="noopener">https://doi.org/10.6084/m9.figshare.28934993.v1</a> (Li et al., 2025).

BackgroundHearing loss is comparatively frequent in the Latin America and Caribbean (LAC) region, where the prevalence of disabling hearing loss is higher than in many areas of the world. The current societal infrastructure in the Dominican Republic (DR) for Deaf people is insufficient: services such as sign language interpreters are lacking, as are accessible and widespread educational and health care services or access to emergency services. This project was formed through four partnerships, including United States- and Dominican Republic-based academic institutions and community organizations serving Deaf communities, to identify lived experience of Deaf Dominicans in accessing health care.MethodsWe recruited 102 people in the DR self-identifying as Deaf to participate in 51 qualitative interviews. All interviews were conducted in Dominican Sign language (“LSRD”) by Deaf interviewers and were visually recorded. Interview transcripts were analyzed using thematic analysis informed by grounded theory to identify categories explaining participants’ experiences.ResultsThree themes emerged in our grounded theory showing that Deaf Dominicans (1) rely heavily on hearing people as facilitators and navigators for the Deaf person to access health care; (2) often as a result of this dependence, Deaf Dominicans frequently experience lack of health care information in transactional health-related encounters and, more broadly, in general health knowledge; and (3) communicate frequently with the hearing world (including health care providers and workers) through gestures other than an established sign language. Further, Deaf Dominicans routinely experience structural audism, defined as systemic discrimination and prejudice against Deaf indivdiuals, when they are unable to fully access health care.ConclusionsUrgent attention is needed to address these systemic barriers. Deaf Dominicans are an underserved linguistic and cultural minority population who face barriers and challenges in accessing the health care system. Full communication and comprehension for the Deaf community is vital in ensuring equitable access to health care.Supplementary InformationThe online version contains supplementary material available at 10.1186/s44263-025-00207-0.