#naturalresources — Public Fediverse posts on home.social

Bytes Europe @[email protected] · 2026-09-10 · 08:34 UTC

Super El Niño: The northern Amazon faces unprecedented climate stress https://www.byteseu.com/2353045/ #Climate #ClimateChange #COPClimateChange #GlobalWarming #NaturalResources #WeatherConditions

#weatherconditions #naturalresources #globalwarming #copclimatechange #climatechange #climate

FRNstudio @[email protected] · 2026-06-18 · 11:34 UTC

Menjaga Bumi untuk Generasi Mendatang: Tanggung Jawab Bersama dalam Konservasi Lingkungan

Di era modern yang ditandai dengan perkembangan teknologi dan pertumbuhan ekonomi yang pesat, isu lingkungan menjadi salah satu tantangan terbesar yang dihadapi umat manusia. Perubahan iklim, berkurangnya kawasan hutan, pencemaran udara dan air, serta menurunnya keanekaragaman hayati merupakan dampak nyata dari aktivitas manusia yang kurang memperhatikan keseimbangan alam. Di tengah kondisi tersebut, kesadaran akan pentingnya konservasi lingkungan menjadi semakin relevan. Foto ini […]

https://frnstudio.wordpress.com/2026/06/18/menjaga-bumi-untuk-generasi-mendatang-tanggung-jawab-bersama-dalam-konservasi-lingkungan/

#biodiversity #butterfly #carbonfootprint #carbonreduction #cleanenergy #climateaction

DoomsdaysCW @[email protected] · 2026-06-08 · 22:46 UTC

Press Release: Rising #Emissions, Depleting #Water and Vanishing #Land—UN Scientists: #AI Is Threatening #NaturalResources for Billions

By 2030, AI's water use will match the needs of 1.3 billion people while its power use triples that of 650 million, UN University investigation warns

Date Published
3 Jun 2026

Excerpt: "Inference, efficiency, and the rebound effect

"Public discussion has largely focused on the energy required to train massive models. Training GPT-3 was estimated to require 1.3 gigawatt-hours (GWh) of electricity, while estimates suggest GPT-4 consumed between 50 and 70 GWh. However, the report reveals this framing is outdated. Once a model is deployed, inference—the continuous running of models to answer everyday user prompts—becomes the dominant cost, accounting for 80 to 90 per cent of total #AI energy use. ChatGPT alone is estimated to process around 2.5 billion prompts per day, translating to roughly 383 GWh of electricity per year for a single product. Offsetting associated carbon emissions would require 2.6 million tree seedlings grown for 10 years, enough trees to cover a land area the size of Manhattan. The water footprint is equivalent to the minimum annual domestic water needs of roughly 500,000 people in Sub-Saharan Africa, and the land footprint is equal to over 800 football fields."

#AIBoom #Electricity #Hyperscale #BigTech #BigData #CarbonFootprint #EnvironmentalRacism #EnvironmentalDegradation #NoisePollution #LightPollution #WaterIsLife #AIAgents #BotTraffic #GreenSpaces #Farmland #Prairies #Woodland #TechGiants #ProtectNature #NoDatacenters #EnergyConsumption #USPol #WorldPol #Datacentres
#DatacenterMoratoriums #ArtificialIntelligence