Climate Data Analysis

🌍 We Can’t Afford to Get Climate Policy Wrong—A Look at the Data Behind What Really Works 🌍 In the race against time to combat climate change, bold promises are everywhere. But here’s the critical question: Are the policies being implemented actually reducing emissions at the scale we need? A groundbreaking study published in Science, cuts through the noise and delivers the insights we desperately need. Evaluating 1,500 climate policies from around the world, the research identifies the 63 most effective ones—policies that have delivered tangible, significant reductions in emissions. What’s striking is that the most successful strategies often involve combinations of policies, rather than single initiatives. Think of it as the ultimate teamwork: when policies like carbon pricing, renewable energy mandates, and efficiency standards are combined thoughtfully, the impact is far greater than any one policy could achieve on its own. It’s a powerful reminder that for climate solutions the whole is indeed greater than the sum of its parts. Moreover, the study’s use of counterfactual emissions pathways is a game changer. By showing what would have happened without these policies, it provides a clear, quantifiable measure of their effectiveness. This is exactly the kind of rigorous evaluation we need to ensure that every policy counts, especially when we’re working against the clock. If we’re serious about meeting the Paris Agreement’s targets, we need to focus on what works—and this research offers a clear roadmap. Let’s champion policies that have proven to make a difference, because we don’t have time to waste on anything less. 🔗 Full study in the comments #ClimateAction #Sustainability #PolicyEffectiveness #ParisAgreement #NetZero #ClimateScience

A 1°C rise in temperature is a poverty multiplier. New global evidence based on subnational data from 130 countries shows that each additional degree of warming: ✖️ Increases poverty by 0.63–1.18 percentage points ✖️ Raises inequality by 1.3–1.9% (Gini index) ✖️ Pushes 62–99 million more people into poverty by 2030 compared to a world without climate change The impacts are not evenly distributed. They are strongest in poorer countries, especially where agriculture dominates livelihoods, and are particularly acute across Sub-Saharan Africa. When we look only at national averages, much of the damage disappears. But subnational analysis reveals the real story: large, localized climate shocks interacting with poverty, inequality, and vulnerability. This matters for policy, finance, and development planning. If we underestimate climate risk by relying on national-level data, we: 1️⃣ Misprice climate risk 2️⃣ Misallocate adaptation finance 3️⃣ Miss the communities most exposed Climate change is no longer just about emissions trajectories. It is about distributional impacts, justice, and who pays the price first. This is why granular climate intelligence must sit at the heart of poverty reduction, adaptation, and development strategies. Because climate risk is not abstract. It is local, unequaland already reshaping development outcomes. read the article in Nature here 👇 https://lnkd.in/ehtBmjip

WEF's Global Risks Report 2026 is out 👉 (https://lnkd.in/eaMrdW67).. I put the findings in a 20-year perspective. I mapped 20 years of risk rankings. Two patterns stand out. Both troubling. The headline findings in this report: 🔵 geoeconomic confrontation is now the #1 risk in the short term, 🔵 economic risks are spiking, 🔵 50% of experts expect a turbulent or stormy outlook over the next two years. But the deeper signal only appears when you track the rankings over time (what I did, see 👇 ). ⚫ 𝐏𝐚𝐭𝐭𝐞𝐫𝐧 𝟏 – 𝐋𝐨𝐧𝐠-𝐭𝐞𝐫𝐦 𝐫𝐢𝐬𝐤𝐬 𝐦𝐢𝐠𝐫𝐚𝐭𝐞 𝐢𝐧𝐭𝐨 𝐭𝐡𝐞 𝐬𝐡𝐨𝐫𝐭 𝐭𝐞𝐫𝐦 Not overnight. Not mechanically. But persistently. In 2007–2010, short-term risks were concrete and immediate: asset bubbles, oil shocks, chronic diseases. Fast forward to today. The long-term top risks for 2026 are: 🌪️ extreme weather 🌍 biodiversity loss 🧠 misinformation 🤖 adverse AI outcomes What changed is not that economic risks disappeared. It’s that structural risks began to act as crisis amplifiers. Extreme weather didn’t replace financial shocks, it reshaped them. Climate risks first entered the short-term top 5 around 2014. By 2020, climate action failure topped the list. “Tomorrow’s risks” became today’s stress multipliers, and increasingly, direct crisis drivers. The future didn’t wait. ⚫𝐏𝐚𝐭𝐭𝐞𝐫𝐧 𝟐: 𝐍𝐚𝐭𝐮𝐫𝐞 𝐢𝐬 𝐛𝐞𝐢𝐧𝐠 𝐟𝐨𝐫𝐠𝐨𝐭𝐭𝐞𝐧, 𝐚𝐠𝐚𝐢𝐧 This year, environmental risks dropped sharply in the short-term rankings. More worrying: their severity scores also declined in absolute terms. Yet over the 10-year horizon, environmental risks dominate the top 10. Twenty years of WEF risk data tell the same story: we consistently recognise long-term environmental threats, then consistently deprioritise them when short-term pressures mount. It's not that we don't know. It's that our attention economy is structurally biased toward the urgent over the important. The most interconnected risk for the second year running? Inequality (👇). It fuels everything else: polarisation, migration, political instability, resistance to climate policy. Perhaps that's where to start: 𝐢𝐟 𝐰𝐞 𝐰𝐚𝐧𝐭 𝐭𝐨 𝐚𝐝𝐝𝐫𝐞𝐬𝐬 𝐥𝐨𝐧𝐠-𝐭𝐞𝐫𝐦 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬, 𝐰𝐞 𝐧𝐞𝐞𝐝 𝐭𝐨 𝐫𝐞𝐝𝐮𝐜𝐞 𝐭𝐡𝐞 𝐬𝐡𝐨𝐫𝐭-𝐭𝐞𝐫𝐦 𝐝𝐞𝐬𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐭𝐡𝐚𝐭 𝐤𝐞𝐞𝐩𝐬 𝐮𝐬 𝐭𝐫𝐚𝐩𝐩𝐞𝐝 𝐢𝐧 𝐜𝐫𝐢𝐬𝐢𝐬 𝐦𝐨𝐝𝐞. #GlobalRisks #WEF #ClimateChange #Sustainability #SystemChange

What’s holding back natural climate solutions? Natural climate solutions (NCS)—from reforestation and agroforestry to wetland restoration—have long been championed as low-cost, high-benefit pathways for reducing greenhouse gases. In theory, they could provide over a third of the climate mitigation needed by 2030 to stay under 2°C of warming. But in practice, progress is stalling. A sweeping new PNAS Nexus study reveals why. Drawing on 352 peer-reviewed papers across 135 countries, researchers led by Hilary Brumberg cataloged 2,480 documented barriers to implementing NCS. The obstacles are not ecological. Rather, they are human: insufficient funding, patchy information, ineffective policies, and public skepticism. The result is a vast “implementation gap” between what is technically possible and what is politically, economically, or socially feasible. The analysis found that “lack of funding” was the most commonly cited constraint globally—identified in nearly half of all countries surveyed. Yet it rarely stood alone. Most regions face a tangle of interconnected hurdles. Constraints from different categories often co-occur, compounding difficulties: poor governance erodes trust; disinterest stems from unclear benefits; technical know-how is stymied by bureaucratic confusion. These patterns vary by region and type of intervention. Reforestation projects, for instance, face particularly high scrutiny over equity concerns—especially in the Global South, where land tenure insecurity and historical injustices run deep. Agroforestry and wetland restoration often struggle with the complexity of design and monitoring. Meanwhile, grassland and peatland pathways remain understudied, despite their importance. The study’s most striking insight may be spatial. Countries within the same UN subregion tend to share a similar profile of constraints—more so than across broader development regions. This geographic clustering suggests an opportunity: Supranational collaboration, if properly resourced and attuned to local context, could address shared challenges more efficiently than isolated national efforts. Crucially, the authors argue that piecemeal fixes will not suffice. Because most countries face an average of seven distinct constraints, many from different domains, effective solutions must be integrated and cross-sectoral. Adaptive management—a flexible, feedback-based approach—could help. By identifying which barriers arise at each stage of an NCS project’s lifecycle, it may be possible to design interventions that are not just technically sound, but socially and politically viable. Natural climate solutions still hold vast potential. But unlocking it will require less focus on where trees grow best—and more on where people can make them thrive. 🔬 Brumberg et al 2025. Global analysis of constraints to natural climate solution implementation. PNAS Nexus. https://lnkd.in/gDmYJEph

The World Meteorological Organization has just released its flagship #StateofClimate 2024 report. It shows that the clear signs of human-induced climate change reached new heights in 2024. Some of the consequences may be irreversible over hundreds if not thousands of years. In the short-term - and on an almost daily basis - extreme weather is causing major economic and social disruption. Some key takeaways: 🔹 Atmospheric concentration of carbon dioxide are at the highest levels in the last 800,000 years. 🔹 Globally each of the past ten years were individually the ten warmest years on record. 🔹 Each of the past eight years has set a new record for ocean heat content. 🔹 The 18 lowest Arctic sea-ice extents on record were all in the past 18 years. 🔹 The three lowest Antarctic ice extents were in the past three years. 🔹 The largest three-year loss of glacier mass on record occurred in the past three years. 🔹 The rate of sea level rise has doubled since satellite measurements began. Read the report: https://lnkd.in/dbs8QRtN

A study published yesterday should be on every European policymaker's desk this morning. New research in Science Advances narrows the uncertainty on how fast the Atlantic Meridional Overturning Circulation is weakening. The answer: a 42% to 58% slowdown by 2100, a level the authors describe as "almost certain to end in collapse." Professor Stefan Rahmstorf, who has studied the AMOC for 35 years, put it in terms any risk manager would understand. A decade ago, the perceived probability of AMOC shutdown was around 5%, and he was already arguing collapse had to be avoided at all costs. Today, he puts it at more than 50%. He now worries the tipping point may be passed in the middle of this century. The AMOC is the current system that gives Northern Europe a climate wildly warmer than its latitude would suggest. A collapse means extreme winters and summer droughts across western Europe, shifted tropical rainfall belts that millions depend on for food, and 50 to 100cm of additional sea level rise around the Atlantic. Europe's strategic autonomy conversation has been dominated by energy, defence, and semiconductors. This study is a reminder that climate stability is the foundational strategic asset, and it is the one we are actively dismantling. The science keeps sharpening. The policy needs to catch up. Guardian link in the comments.

The destabilisation of the #AMOC is no longer a distant scenario - it’s a national and global security risk, that governments must plan for now. In a new article in Dagens Nyheter, Iceland’s Climate Minister Jóhann Páll Jóhannsson and I outline actions to tackle this threat: ● Strengthen Nordic climate leadership through a joint action plan for the rapid regional decarbonisation. A coordinated strategy for policy engagement is also needed to influence countries that are moving more slowly on the international stage. ● Establish integrated long-term systems for observation, modelling, and preparedness — directly linked to civil defence and national security planning, recognising that we have entered a phase of unacceptable risk of destabilisation or collapse of the AMOC. The Nordic–Arctic region must be treated as a critical hotspot for climate tipping points, as the Arctic is warming two to three times faster than the global average and hosts at least six integrated tipping point systems. ● Place climate risk analysis at the centre of security and stability and go beyond sector-specific impacts to analyse potential domino effects when climate change affects people and communities locally and regionally. https://lnkd.in/dAc4R2sR (in Swedish)

Thrilled to unveil our latest work: multi-modal machine learning to forecast localized weather! We construct a graph neural network to learn dynamics at point locations, where typical gridded forecasts miss significant variation. Paper: https://lnkd.in/eBmfsJin Weather dataset: https://lnkd.in/ejCG8bKs Code: https://lnkd.in/eQg-JzQJ AI weather models have made huge strides, but most still emulate products like ERA5, which struggle to capture near-surface wind dynamics. The correlation between ERA5 and ground weather station data is low due to topography, buildings, vegetation, and other local factors. In this work, we forecast near-surface wind at localized off-grid locations using a message-passing graph neural network ("MPNN"). The graph is heterogeneous, integrating both global forecasts (ERA5) and historical local weather station data as different nodes. What do we find? First off, ERA5 interpolation performs poorly, failing to capture local wind variations, especially in coastal and inland regions with complex conditions. An MLP trained on historical data at a location performs better than ERA5 interpolation, as it learns from the station's past observations. However, it struggles with longer lead times and lacks the spatial context necessary to capture weather patterns. Meanwhile, our MPNN dramatically improves performance, reducing the error by over 50% compared to the MLP. This is because the MPNN incorporates spatial information through message passing, allowing it to learn local weather dynamics from both station data and global forecasts. Interestingly, adding ERA5 data to the MLP does not improve its performance significantly. The MLP struggles to integrate spatial information from global forecasts, while the MPNN excels, highlighting the importance of combining global and local data. Large improvements in forecast accuracy occur at both coastal and inland locations. Our model shows a 92% reduction in MSE relative to ERA5 interpolation overall. This work showcases the strength of machine learning in combining multi-modal data. By using a graph to integrate global and local weather data, we were able to generate much more accurate localized weather forecasts! Congrats to Qidong Yang and Jonathan Giezendanner for the great work, and thanks to Campbell Watson, Daniel Salles Chevitarese, Johannes Jakubik, Eric Schmitt, Anirban C., Jeremy Vila, Detlef Hohl, and Chris Hill for a wonderful collaboration. Thanks also to our partners at Amazon Web Services (AWS) for providing cloud computing and technical support!

❓❓❓WHICH CLIMATE POLICIES WORK   💡New insights on this crucial question in our new paper 'Climate policies that achieved major emission reductions: Global evidence from two decades', published last week in Science.   This is probably the most important study that I have contributed to. Screening more than 1500 climate policies implemented between 1998 and 2022 across 41 countries from 6 continents, we identify those policy combinations that were successful in achieving substantial emission reductions (see picture below). This offers crucial guidance for countries to design climate policies and fight climate change.   Key messages: ✅Mixes>standalone: Climate policies tend to work best if they're part of a policy mix rather than implemented alone. ✅There is no one-size fits all approach: Successful policy mixes vary across sectors and between developed and developing countries.   This study was possible thanks to our #OECD Climate Actions and Policies Measurement Framework (CAPMF). This is exactly the kind of empirical research, for which we have developed the CAPMF. I'm very hopeful that this study will pave the way for further research that supports countries to develop best practices.   Links Study: https://lnkd.in/emNSyUmw Climate policy explorer: https://lnkd.in/e4YyxwKa CAPMF dataset: https://oe.cd/dx/capmf   Many thanks to my co-authors Annika Stechemesser, Nicolas Koch, Ebba Mark, Elina Dilger, Patrick N. Klösel, Laura Menicacci, Felix Pretis, Nolan Ritter, Moritz Schwarz, Helena Vossen & Anna Wenzel from PIK - Potsdam Institute for Climate Impact Research, Mercator Research Institute on Global Commons and Climate Change (MCC) gGmbH and others.   #climateaction #climatepolicy #policy

Only a fraction of climate policy interventions produces significant results, according to a big study published in 'Science' yesterday. The study assessed 1500 policies (e.g., changes in subsidies and taxes) implemented between 1998 and 2022 across 41 countries. Only 63 policies showed large effects on reduced emissions (so-called 'breaks'). ❗We have a 'climate ambition gap' (policies do not aim high enough), but we also have a significant 'climate outcome gap' (those policies that are implemented often do not produce significant enough results). ❗ Key take aways: 1️⃣ Taxation and price incentives are by far the most effective policy instruments to achieve emission breaks. "It [taxation] stands out as the only policy instrument that achieves near equal or larger effect size as a stand-alone policy across all sectors." 2️⃣ Successful emissions reductions usually rely on mixes of different interventions (with tax and price incentives being part of the mix). Market-based instruments and regulations (e.g., product bans) need to be aligned and work together (e.g., banning fossil cars, increasing the price of gasoline, and subsidising e-mobility). 3️⃣ Most successful policy interventions occur in the building sector, followed by transport , industry, and electricity. Success rates vary strongly by sector and policymakers should therefore contextualise interventions. Successful climate policies need the right mix of instruments and have to include taxation and pricing measures to show significant outcomes! Full study (open access): https://lnkd.in/d7GdU6v3 #climatechange, #sustainability, #esg