Science-Based Policy Implementation

🌍 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

This is a brilliant paper - hot off the press - which makes the point that for too long, behavioural insights have been seen as a tool for tweaking individual behaviour—nudges, default settings, and small interventions. But this report argues that behavioural insights has the power to shape entire systems, not just individuals. "Systemic change is fundamentally rooted in human behaviour: while structural, political, economic, or technological challenges may set the stage, it is the decisions and actions of individuals that ultimately drive change. Behind every challenge lies the potential for human behaviour to alter the course, provided the right behavioural pathways are identified and leveraged." Created by Marion Dupoux and colleagues at the European Commission Joint Research Centre, it outlines how to harness the full potential of behavioural insights, by: 1️⃣ Moving beyond Nudges: By doing more than influencing individual choices—they can inform policy mixes that integrate regulations, incentives, and behavioural interventions 2️⃣ Creating policy coherence: By helping to identify where different policies complement or contradict each other, leading to more effective, aligned strategies 3️⃣ Leading to systemic Impact: By embedding behavioural insights early in the policymaking process, we can design policies that work with human behaviour rather than against it The authors call for us to go further with behavioural insights: "We argue that a more proactive and systematic approach is needed for BI to contribute to systemic change. This involves ensuring behavioural interventions are crafted with scalability in mind, informing the design of traditional policy instruments from the outset and, last but not least, understanding and working with complex systems." One of my favourite parts of this resource is the section focussed on achieving a systemic impact with behavioural insights. In this section, the authors highlight several key principles: 1️⃣ Embracing interdisciplinary collaborations and research  2️⃣ Embedding behavioural insights across the policy cycle, and crucially, starting early  3️⃣ Fostering knowledge of behavioural insights  4️⃣ Making tools from behavioural insights more readily available "BI should be integrated into all phases of the policy process, with particular emphasis on the earliest stages, to ground policy design in human behaviour, enhance policy coherence, and ensure a better functioning system." Source: Dupoux, M. (2025). Unlocking the full potential of behavioural insights for policy. From influencing the individual to shaping the system. European Commission Joint Research Centre.

Community pharmacies are already delivering primary care — the question is whether health systems are ready to recognize and govern that reality. In many countries, especially LMICs, community pharmacies are the first point of contact for people seeking care. They are accessible, trusted, and embedded in communities. Yet too often, they sit outside formal primary care models — under-recognized, under-regulated, and under-utilized. A new WHO Clearing House brief, Inclusion of community pharmacies in private practice within primary care, explores what the evidence tells us about this gap — and what governments can do about it. The brief examines: • Why community pharmacies matter for primary health care and UHC • The governance risks of ignoring their role (from antimicrobial resistance to high out-of-pocket spending) • Policy strategies to recognize, regulate, and integrate pharmacies within models of care • Practical tools — regulation, accreditation, remuneration — that enable safer, more effective integration When community pharmacies are brought into primary care through deliberate policy design, aligned incentives, and appropriate oversight, they can support prevention, continuity of care, rational medicine use, and health system resilience — all critical to achieving universal health coverage. This brief is part of WHO’s broader effort to strengthen stewardship of the private sector in health systems and to move beyond “public vs private” debates toward designed integration for public value #PrimaryHealthCare #UniversalHealthCoverage #HealthGovernance #CommunityPharmacy #PrivateSectorInHealth #SystemsThinking #GovernanceByDesign

This image is from an Amazon Braket slide deck that just did the rounds of all the Deep Tech conferences I've been at recently (this one from Eric Kessler). It's more profound than it might seem. As technical leaders, we're constantly evaluating how emerging technologies will reshape our computational strategies. Quantum computing is prominent in these discussions, but clarity on its practical integration is... emerging. It's becoming clear however that the path forward isn't about quantum versus classical, but how quantum and classical work together. This will be a core theme for the year ahead. As someone now on the implementation partner side of this work, and getting the chance to work on specific implementations of quantum-classical hybrid workloads, I think of it this way: Quantum Processing Units (QPUs) are specialised engines capable of tackling calculations that are currently intractable for even the largest supercomputers. That's the "quantum 101" explanation you've heard over and over. However, missing from that usual story, is that they require significant classical infrastructure for: - Control and calibration - Data preparation and readout - Error mitigation and correction frameworks - Executing the parts of algorithms not suited for quantum speedup Therefore, the near-to-medium term future involves integrating QPUs as accelerators within a broader classical computing environment. Much like GPUs accelerate specific AI/graphics tasks alongside CPUs, QPUs are a promising resource to accelerate specific quantum-suited operations within larger applications. What does this mean for technical decision-makers? Focus on Integration: Strategic planning should center on identifying how and where quantum capabilities can be integrated into existing or future HPC workflows, not on replacing them entirely. Identify Target Problems: The key is pinpointing high-value business or research problems where the unique capabilities of quantum computation could provide a substantial advantage. Prepare for Hybrid Architectures: Consider architectures and software platforms designed explicitly to manage these complex hybrid workflows efficiently. PS: Some companies like Quantum Brilliance are focused on this space from the hardware side from the outset, working with Pawsey Supercomputing Research Centre and Oak Ridge National Laboratory. On the software side there's the likes of Q-CTRL, Classiq Technologies, Haiqu and Strangeworks all tackling the challenge of managing actual workloads (with different levels of abstraction). Speaking to these teams will give you a good feel for topic and approaches. Get to it. #QuantumComputing #HybridComputing #HPC

✨ AI at a crossroads: Can we steer it responsibly? The Association for the Advancement of Artificial Intelligence (AAAI) 2025 Presidential Panel on the Future of AI Research lays out a stark reality—AI is advancing at an unprecedented pace, but governance, safety, and evaluation mechanisms are struggling to keep up. 🌏 Having worked at the intersection of AI governance, responsible deployment, and multi-agent AI, I see a recurring challenge: we are building AI that is more powerful than our ability to govern it responsibly. 🔬 Key takeaways from the report & my perspective:- ✅ AI Reasoning & Trustworthiness:- While LLMs and Agentic AI are demonstrating emergent reasoning, we lack verifiable correctness. Can we afford AI-driven decision-making without reliability guarantees? ✅ Agentic AI & Multi-Agent Systems:- The integration of LLMs into autonomous, multi-agent AI systems is a double-edged sword. On one hand, these systems offer adaptive, cooperative intelligence—but on the other, they introduce complexity, opacity, and safety risks. We need governance models that balance autonomy and oversight. ✅ Responsible AI Development & Deployment:- Many organizations still focus on post-deployment fixes rather than AI safety by design. Alignment techniques today (RAG, constitutional AI, human feedback) remain fragile. We must shift toward "failsafe AI"—AI that degrades gracefully rather than unpredictably. ✅ AI Ethics & Governance:- AI risks—whether misinformation, deepfakes, or algorithmic bias—are no longer just theoretical. Geopolitical competition for AI dominance could further sideline ethical considerations. It is time for a convergence of policy, technical safety, and corporate governance models to ensure AI serves societal progress, not just market incentives. 👩💻 The Path Forward: A Call for Multidisciplinary Collaboration:- AI governance cannot be an afterthought. It must be woven into the DNA of AI systems—across research, regulation, and deployment. As someone deeply involved in AI governance and policy, I believe the future lies in co-regulation—where industry, academia, and policymakers collaborate proactively rather than reactively. ✨ How do we get there? 1️⃣ Bridging the gap between AI development and policy-making. 2️⃣ Building safety-aligned benchmarks for Agentic AI. 3️⃣ Embedding ethical constraints within AI architectures, not just in guidelines. 💡 AI is no longer just a tool—it is a co-pilot in decision-making, shaping economies, politics, and societies. The question is: can we govern it before it governs us? 🔎 Would love to hear your thoughts! What challenges do you see in ensuring AI remains safe, aligned, and trustworthy? #AIResearch #ResponsibleAI #AITrust #AgenticAI #Governance #AAAI2025 #AISafety #AIRegulation #EthicalAI

Cross-disciplinary science teams are being asked to solve increasingly complex problems—but many of our leadership habits are still built for a simpler world. I’ve been re-reading Dr. Gemma Jiang’s 2023 paper on collaborative leadership in team science, which frames these teams as complex adaptive systems and then asks a practical question: how do we actually lead when outcomes are emergent, not predictable? The article highlights three recurring pitfalls: 1. Perpetual sensemaking with no real decisions or actions 2. Decisions made by a small inner circle without inclusive sensemaking, undermining both quality and buy‑in. 3. Rigid adherence to initial plans even as context shifts, treating the project plan as “the bible.” To move beyond these traps, Gemma brings together three conceptual frameworks that, in effect, act as lightweight operating systems for collaborative leadership: >> Theory U – Encourages teams to go “down the left side of the U” into deep, inclusive sensemaking before committing to action, linking the depth of inquiry to the quality of outcomes. >> Divergence–Convergence Double Diamond – Makes visible the oscillation between divergence and convergence in both sensemaking and action, including the inevitable “groan zone” where integrating diverse perspectives feels hard but is essential for innovation. >> Strategic Doing – Replaces long, hierarchical planning cycles with fast iterations and “pathfinder projects,” integrating thinkers and doers in short loops of sensemaking, deciding, and acting. What I find especially useful is how these frameworks shift leadership from a person to a process: distributed leadership becomes the disciplined practice of structuring conversations so that coherence, decision making, and actions continually inform one another. For those working in large, multi-institutional projects—or building innovation platforms and ecosystems—this paper offers a practical way to design the rules of engagement so that adaptive behavior can emerge without generating chaos or reverting to the rigidities of command‑and‑control practices.

Finally, friends! Some data to help us tackle the big question we all want answered - are Africa's grids actually ready for a shift to electric vehicles???? My latest research with colleagues from University of Massachusetts Amherst | Research was just published in Nature Portfolio Scientific Reports. It is the first comprehensive analysis of the impact of electric vehicle fleet expansion on electricity grids in African cities. We build granular models that simulate traffic patterns, EV charging, and transformer utilization, to analyze the effects of progressively higher rates of EV adoption on bulk electricity supply and transformer overloading, key indicators of grid stability. We sourced vehicle ownership data from USAID, power consumption & transformer data from KPLC, and hourly traffic data from Uber. Taking Nairobi, Kenya as a prime case, we find that adoption of electric vehicles across the public transportation and commercial fleet sectors generally improves grid conditions in the city by adding consumption during periods when the grid is otherwise significantly below peak capacity. However, widespread conversion of private vehicles – the largest vehicle class in Nairobi – can substantially exacerbate peak electric demand, leading to the accelerated overload of transformers, forcing both an increase in electricity outages as well as very expensive early equipment replacement costs. Introducing coordination logic into the charging model reveals that even at moderate of fleet conversion, coordinated charging could reverse the situation, instead representing an avoided cost saving from early equipment replacements. These findings demonstrate the critical nature of a managed and coordinated transition to electric mobility in Africa. Improved planning, and engagement across key stakeholders including the electric utility, the municipal transport authority, regulators, and national policy makers is key! The first EV study of such granularity for Africa, our model can be replicated to explore grid dynamics in other African cities. Please check out our paper if you are following trends in electric mobility and watch out for more! What other questions about EVs and the grid would you like us to research? Share your ideas below! https://lnkd.in/dURjyv5E

The Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) has released a comprehensive report highlighting the intricate connections among #biodiversity, #water, #food, #health, and #climatechange. This "Nexus Report" emphasizes that addressing these global challenges in isolation is ineffective and may exacerbate existing issues. Here are five key takeaways from the report: Interconnected Crises Amplify Economic Losses: 👉 The report estimates that neglecting the interlinked crises of biodiversity loss, climate change, water scarcity, food insecurity, and health risks results in unaccounted costs ranging from $10 trillion to $25 trillion annually, equivalent to about a quarter of global GDP.Sectors such as agriculture, energy, and fishing contribute significantly to these losses by failing to consider the broader environmental impacts of their activities. 👉 Delayed Action Increases Future Costs: Procrastination in addressing these interconnected issues leads to escalating expenses.For instance, inaction on climate change could add $500 billion annually to future costs, while postponing measures to combat biodiversity loss may double the associated costs if delayed by a decade. 👉 Misaligned Financial Incentives Perpetuate Environmental Harm: Current government subsidies, totaling approximately $1.7 trillion annually, often support environmentally detrimental practices, including fossil fuel production, overfishing, and unsustainable agriculture. These subsidies, along with $5.3 trillion in private financial flows that damage biodiversity, exacerbate the crises they aim to mitigate. 👉 Holistic Approaches Yield Significant Benefits: The report underscores the necessity of integrated policy efforts that consider the complex interconnections among biodiversity, water, food, health, and climate change. Implementing holistic solutions can unlock $10 trillion in business opportunities and support 395 million jobs globally by 2030. Proven strategies, such as agroforestry and nature-friendly renewable energy schemes, demonstrate that it's possible to address multiple environmental challenges simultaneously. 👉 Addressing Inequalities is Crucial for Transformative Change: Persistent economic and political inequalities, often rooted in historical contexts like colonialism, hinder efforts to halt biodiversity loss. The report calls for transformative change that includes recognizing and addressing these inequalities to create a more equitable and sustainable future. https://lnkd.in/dyz2_-HM

Great seeing our paper out in Science! Stefano Carattini, John List and I argue that policy evaluation should be combined with a causal analysis of public support. Starting point of our argument is that policies that are generally considered socially desirable by the scientific community are not always popular among voters, because of a lack of understanding or biased beliefs. Congestion charges and carbon taxes are a case in point. However, recent empirical studies have shown that, in cases like these, experiencing the policy may lead voters to correct their beliefs and increase their support. A credible policy evaluation may further help voters to learn about the policy's effects. Our article describes how credible policy evaluation can be fruitfully combined with a causal analysis of public support. If it becomes more widely documented that opposition to sound policies dissipates when voters experience a policy, then policy-makers may be more inclined to experiment with such policies. Learning when and why public support does not increase after policy implementation would be very important as well. Indeed, this may even lead to a change in the consensus about the policy's desirability, for instance when scientists learn that they overlooked some negative aspects of the policy that voters strongly care about. Read the full article here: https://lnkd.in/ed2EAj9G Science Magazine