@article{JankinDeNeveDawesetal., author = {Jankin, Slava and De Neve, Jan-Emmanuel and Dawes, Christopher T. and Christakis, Nicholas A. and Fowler, James H.}, title = {Born to Lead? A Twin Design and Genetic Association Study of Leadership Role Occupancy}, series = {The Leadership Quarterly}, volume = {24}, journal = {The Leadership Quarterly}, number = {1}, doi = {10.1016/j.leaqua.2012.08.001}, pages = {45 -- 60}, abstract = {We address leadership emergence and the possibility that there is a partially innate predisposition to occupy a leadership role. Employing twin design methods on data from the National Longitudinal Study of Adolescent Health, we estimate the heritability of leadership role occupancy at 24\%. Twin studies do not point to specific genes or neurological processes that might be involved. We therefore also conduct association analysis on the available genetic markers. The results show that leadership role occupancy is associated with rs4950, a single nucleotide polymorphism (SNP) residing on a neuronal acetylcholine receptor gene (CHRNB3). We replicate this family-based genetic association result on an independent sample in the Framingham Heart Study. This is the first study to identify a specific genotype associated with the tendency to occupy a leadership position. The results suggest that what determines whether an individual occupies a leadership position is the complex product of genetic and environmental influences, with a particular role for rs4950.}, language = {en} } @article{BaturoJankin, author = {Baturo, Alexander and Jankin, Slava}, title = {Life of Brian Revisited: Assessing Informational and Non-Informational Leadership Tools}, series = {Political Science Research and Methods}, volume = {1}, journal = {Political Science Research and Methods}, number = {1}, doi = {10.1017/psrm.2013.3}, pages = {139 -- 157}, abstract = {Recent literature models leadership as a process of communication in which leaders' rhetorical signals facilitate followers' co-ordination. While some studies have explored the effects of leadership in experimental settings, there remains a lack of empirical research on the effectiveness of informational tools in real political environments. Using quantitative text analysis of federal and sub-national legislative addresses in Russia, this article empirically demonstrates that followers react to informational signals from leaders. It further theorizes that leaders use a combination of informational and non-informational tools to solve the co-ordination problem. The findings show that a mixture of informational and non- informational tools shapes followers' strategic calculi. Ignoring non-informational tools — and particularly the interrelationship between informational and non-informational tools - can threaten the internal validity of causal inference in the analysis of leadership effects on co-ordination.}, language = {en} } @article{MarshJankin, author = {Marsh, Michael and Jankin, Slava}, title = {A Conservative Revolution: The electoral response to economic crisis in Ireland}, series = {Journal of Elections, Public Opinion and Parties}, volume = {24}, journal = {Journal of Elections, Public Opinion and Parties}, number = {2}, issn = {1745-7289}, doi = {10.1080/17457289.2014.887719}, pages = {160 -- 179}, abstract = {The 2011 election in Ireland was one of the most dramatic elections in European post-war history in terms of net electoral volatility. In some respects the election overturned the traditional party system. Yet it was a conservative revolution, one in which the main players remained the same, and the switch in the major government party was merely one in which one centre right party replaced another. Comparing voting behaviour over the last three elections we show that the 2011 election looks much like that of 2002 and 2007. The crisis did not result in the redefinition of the electoral landscape. While we find clear evidence of economic voting at the 2011 election, issue voting remained week. We believe that this is due to the fact that parties have not offered clear policy alternatives to the electorate in the recent past and did not do so in 2011.}, language = {en} } @article{JankinMarsh, author = {Jankin, Slava and Marsh, Michael}, title = {Reading The Tea Leaves: Medvedev's Presidency Through Political Rhetoric Of Federal And Sub-National Actors}, series = {Europe-Asia Studies}, volume = {66}, journal = {Europe-Asia Studies}, number = {6}, issn = {0966-8136}, doi = {10.1080/09668136.2014.926716}, pages = {969 -- 992}, abstract = {In the absence of public information on the inner workings of the Russian political regime, especially during Medvedev's presidency, outside observers often have to rely on politicians' unguarded comments or subjective analysis. Instead, we turn to quantitative text analysis of political rhetoric. Treating governors as a quasi-expert panel, we argue that policy positions revealed in regional legislative addresses explain how elites perceived the distribution of power between Putin and Medvedev. We find that governors moved from a neutral position in 2009 to a clearly pro-Putin position in 2011, and that policy initiatives advocated by Medvedev all but evaporated from the rhetoric of governors in 2012.}, language = {en} } @article{JankinBaturo, author = {Jankin, Slava and Baturo, Alexander}, title = {Blair Disease? Business Careers of the Former Democratic Heads of State and Government}, series = {Public Choice}, volume = {166}, journal = {Public Choice}, number = {3-4}, issn = {0048-5829}, doi = {10.1007/s11127-016-0325-8}, pages = {335 -- 354}, abstract = {Examining the careers of democratic heads of state and government from 1960-2010, we find that one in every seven turns to the private sector after office. Distinguishing between the factors that attract leaders to business and those that render leaders attractive, we find that the global CEO compensation rates, cultural norms, having served in office in Anglo-Saxon countries as well as their personal background, matter. We also find that certain economic outcomes and policies in office such as economic growth and reduction in state spending are often associated with post-tenure business careers. We do not find evidence, however, that leaders are able to implement policies with future careers in mind, which would in turn raise concerns over accountability.}, language = {en} } @article{JankinBenoitConwayetal., author = {Jankin, Slava and Benoit, Kenneth and Conway, Drew and Laver, Michael}, title = {Crowd-Sourced Text Analysis: Reproducible and agile production of political data}, series = {American Political Science Review}, volume = {110}, journal = {American Political Science Review}, number = {2}, issn = {0003-0554}, doi = {10.1017/S0003055416000058}, pages = {278 -- 295}, abstract = {Empirical social science often relies on data that are not observed in the field, but are transformed into quantitative variables by expert researchers who analyze and interpret qualitative raw sources. While generally considered the most valid way to produce data, this expert-driven process is inherently difficult to replicate or to assess on grounds of reliability. Using crowd-sourcing to distribute text for reading and interpretation by massive numbers of non-experts, we generate results comparable to those using experts to read and interpret the same texts, but do so far more quickly and flexibly. Crucially, the data we collect can be reproduced and extended transparently, making crowd-sourced datasets intrinsically reproducible. This focuses researchers' attention on the fundamental scientific objective of specifying reliable and replicable methods for collecting the data needed, rather than on the content of any particular dataset. We also show that our approach works straightforwardly with different types of political text, written in different languages. While findings reported here concern text analysis, they have far-reaching implications for expert-generated data in the social sciences.}, language = {en} } @article{JankinBaturoDasandi, author = {Jankin, Slava and Baturo, Alexander and Dasandi, Niheer}, title = {Understanding State Preferences With Text As Data: Introducing the UN General Debate Corpus}, series = {Research \& Politics}, volume = {4}, journal = {Research \& Politics}, number = {2}, doi = {10.1177/2053168017712821}, abstract = {Every year at the United Nations (UN), member states deliver statements during the General Debate (GD) discussing major issues in world politics. These speeches provide invaluable information on governments' perspectives and preferences on a wide range of issues, but have largely been overlooked in the study of international politics. This paper introduces a new dataset consisting of over 7300 country statements from 1970-2014. We demonstrate how the UN GD corpus (UNGDC) can be used as a resource from which country positions on different policy dimensions can be derived using text analytic methods. The article provides applications of these estimates, demonstrating the contribution the UNGDC can make to the study of international politics.}, language = {en} } @techreport{HerzogJankin, type = {Working Paper}, author = {Herzog, Alexander and Jankin, Slava}, title = {Database of Parliamentary Speeches in Ireland, 1919-2013}, series = {IEEE Proceedings of the 2017 International Conference on the Frontiers and Advances in Data Science (FADS)}, journal = {IEEE Proceedings of the 2017 International Conference on the Frontiers and Advances in Data Science (FADS)}, doi = {10.1109/FADS.2017.8253189}, pages = {29 -- 34}, abstract = {We present a database of parliamentary debates that contains the complete record of parliamentary speeches from D{\´a}il {\´E}ireann, the lower house and principal chamber of the Irish parliament, from 1919 to 2013. In addition, the database contains background information on all TDs (Teachta D{\´a}la, members of parliament), such as their party affiliations, constituencies and office positions. The current version of the database includes close to 4.5 million speeches from 1,178 TDs. The speeches were downloaded from the official parliament website and further processed and parsed. Background information on TDs was collected from the member database of the parliament website. Data on cabinet positions (ministers and junior ministers) was collected from the official website of the government. A record linkage algorithm and human coders were used to match TDs and ministers.}, language = {en} } @techreport{GurciulloJankin, type = {Working Paper}, author = {Gurciullo, Stefano and Jankin, Slava}, title = {Detecting Policy Preferences and Dynamics in the UN General Debate with Neural Word Embeddings}, series = {IEEE Proceedings of the 2017 International Conference on the Frontiers and Advances in Data Science (FADS)}, journal = {IEEE Proceedings of the 2017 International Conference on the Frontiers and Advances in Data Science (FADS)}, doi = {10.1109/FADS.2017.8253197}, pages = {74 -- 79}, abstract = {Foreign policy analysis has been struggling to find ways to measure policy preferences and paradigm shifts in international political systems. This paper presents a novel, potential solution to this challenge, through the application of a neural word embedding (Word2vec) model on a dataset featuring speeches by heads of state or government in the United Nations General Debate. The paper provides three key contributions based on the output of the Word2vec model. First, it presents a set of policy attention indices, synthesizing the semantic proximity of political speeches to specific policy themes. Second, it introduces country-specific semantic centrality indices, based on topological analyses of countries' semantic positions with respect to each other. Third, it tests the hypothesis that there exists a statistical relation between the semantic content of political speeches and UN voting behavior, falsifying it and suggesting that political speeches contain information of different nature then the one behind voting outcomes. The paper concludes with a discussion of the practical use of its results and consequences for foreign policy analysis, public accountability, and transparency.}, language = {en} } @article{JankinWattsAmannetal., author = {Jankin, Slava and Watts, Nick and Amann, Markus and Ayeb-Karlsson, Sonja and Belesova, Kristine and Bouley, Timothy and Boykoff, Maxwell and Byass, Peter and Cai, Wenjia and Campbell-Lendrum, Diarmid and Chambers, Jonathan and Cox, Peter M and Daly, Meaghan and Dasandi, Niheer and Davies, Michael and Depledge, Michael and Depoux, Anneliese and Dominguez-Salas, Paula and Drummond, Paul and Ekins, Paul and Flathault, Antoine and Frumkin, Howard and Georgeson, Lucien and Ghanei, Mostafa and Grace, Delia and Graham, Hilary and Grojsman, R{\´e}becca and Haines, Andy and Hamilton, Ian and Hartinger, Stella and Johnson, Anne and Kelman, Ilan and Kiesewetter, Gregor and Kniveton, Dominic and Liang, Lu and Lott, Melissa and Lowe, Robert and Mace, Georgina and Sewe, Maquins Odhiambo and Maslin, Mark and Milner, James and Latifi, Ali Mohammad and Moradi-Lakeh, Maziar and Morrissey, Karyn and Murray, Kris and Neville, Tara and Nilsson, Maria and Oreszczyn, Tadj and Owfi, Fereidoon and Pencheon, David and Pye, Steve and Rabbaniha, Mahnaz and Robinson, Elizabeth and Rockl{\"o}v, Joacim and Sch{\"u}tte, Stefanie and Shumake-Guillemot, Joy and Steinbach, Rebecca and Tabatabaei, Meisam and Wheeler, Nicola and Wilkinson, Paul and Gong, Peng and Montgomery, Hugh and Costello, Anthony}, title = {The Lancet countdown on health and climate change: from 25 years of inaction to a global transformation for public health}, series = {The Lancet}, volume = {391}, journal = {The Lancet}, number = {10120}, doi = {10.1016/S0140-6736(17)32464-9}, pages = {581 -- 630}, abstract = {The Lancet Countdown tracks progress on health and climate change and provides an independent assessment of the health effects of climate change, the implementation of the Paris Agreement, and the health implications of these actions. It follows on from the work of the 2015 Lancet Commission on Health and Climate Change, which concluded that anthropogenic climate change threatens to undermine the past 50 years of gains in public health, and conversely, that a comprehensive response to climate change could be "the greatest global health opportunity of the 21st century".}, language = {en} } @article{JankinPenchevaEsteve, author = {Jankin, Slava and Pencheva, Irina and Esteve, Marc}, title = {Big Data \& AI - A Transformational Shift for Government: So, What Next for Research?}, series = {Public Policy and Administration}, journal = {Public Policy and Administration}, doi = {10.1177/0952076718780537}, pages = {1 -- 21}, abstract = {Big Data and Artificial Intelligence will have a profound transformational impact on governments around the world. Thus, it is important for scholars to provide a useful analysis on the topic to public managers and policymakers. This study offers an in-depth review of the Policy and Administration literature on the role of Big Data and advanced analytics in the public sector. It provides an overview of the key themes in the research field, namely the application and benefits of Big Data throughout the policy process, and challenges to its adoption and the resulting implications for the public sector. It is argued that research on the subject is still nascent and more should be done to ensure that the theory adds real value to practitioners. A critical assessment of the strengths and limitations of the existing literature is developed, and a future research agenda to address these gaps and enrich our understanding on the topic is proposed.}, language = {en} } @article{JankinEsteveCampion, author = {Jankin, Slava and Esteve, Marc and Campion, Averill}, title = {Artificial intelligence for the public sector: opportunities and challenges of cross-sector collaboration}, series = {Philosophical Transactions of the Royal Society A}, volume = {376}, journal = {Philosophical Transactions of the Royal Society A}, number = {2128}, doi = {10.1098/rsta.2017.0357}, abstract = {Public sector organizations are increasingly interested in using data science and artificial intelligence capabilities to deliver policy and generate efficiencies in high uncertainty environments. The long-term success of data science and artificial intelligence (AI) in the public sector relies on effectively embedding it into delivery solutions for policy implementation. However, governments cannot do this integration of AI into public service delivery on their own. The UK Government Industrial Strategy is clear that delivering on the AI grand challenge requires collaboration between universities and public and private sectors. This cross-sectoral collaborative approach is the norm in applied AI centres of excellence around the world. Despite their popularity, cross-sector collaborations entail serious management challenges that hinder their success. In this article we discuss the opportunities and challenges from AI for public sector. Finally, we propose a series of strategies to successfully manage these cross-sectoral collaborations.}, language = {en} } @article{JankinWattsAmannetal., author = {Jankin, Slava and Watts, Nick and Amann, Markus and et al.,}, title = {The 2018 report of the Lancet Countdown on health and climate change: shaping the health of nations for centuries to come}, series = {The Lancet}, volume = {392}, journal = {The Lancet}, number = {10163}, issn = {0140-6736}, doi = {10.1016/S0140-6736(18)32594-7}, pages = {2479 -- 2514}, abstract = {The Lancet Countdown: tracking progress on health and climate change was established to provide an independent, global monitoring system dedicated to tracking the health dimensions of the impacts of, and the response to, climate change. The Lancet Countdown tracks indicators across five domains: climate change impacts, exposures, and vulnerability; adaptation, planning, and resilience for health; mitigation actions and health co-benefits; finance and economics; and public and political engagement. This report is the product of a collaboration of 27 leading academic institutions, the UN, and intergovernmental agencies from every continent. The report draws on world-class expertise from climate scientists, ecologists, mathematicians, geographers, engineers, energy, food, livestock, and transport experts, economists, social and political scientists, public health professionals, and doctors. The Lancet Countdown's work builds on decades of research in this field, and was first proposed in the 2015 Lancet Commission on health and climate change,1 which documented the human impacts of climate change and provided ten global recommendations to respond to this public health emergency and secure the public health benefits available.}, language = {en} } @article{CarmodyDasandiJankin, author = {Carmody, P{\´a}draig and Dasandi, Niheer and Jankin, Slava}, title = {Power Plays and Balancing Acts: The Paradoxical Effects of Chinese Trade on African Foreign Policy Positions}, series = {Political Studies}, journal = {Political Studies}, doi = {10.1177/0032321719840962}, abstract = {There has been substantial focus on China's influence in Africa in recent years. Some argue that China's growing economic ties with African states have increased its political influence across the continent. This article examines whether trade with China leads African states to adopt more similar foreign policy preferences to China in the United Nations. We examine foreign policy similarity using voting patterns in the United Nations General Assembly and country statements in the United Nations General Debate. The analysis demonstrates that more trade with China has paradoxical effects on foreign policy positions of African states—it leads them to align more closely with US foreign policy positions in the United Nations, except on human rights votes. Our findings suggest that African states are engaged in balancing behavior with external powers whereby African elites seek to play off rival powers against one another in order to strengthen their own autonomy and maximize trade.}, language = {en} } @article{MunzertJankinal, author = {Munzert, Simon and Jankin, Slava and al., et}, title = {The 2019 report of The Lancet Countdown on health and climate change: ensuring that the health of a child born today is not defined by a changing climate}, series = {The Lancet}, volume = {394}, journal = {The Lancet}, number = {10211}, doi = {10.1016/S0140-6736(19)32596-6}, pages = {1836 -- 1878}, abstract = {The Lancet Countdown is an international, multidisciplinary collaboration, dedicated to monitoring the evolving health profile of climate change, and providing an independent assessment of the delivery of commitments made by governments worldwide under the Paris Agreement.}, language = {en} } @article{PomeroyDasandiJankin, author = {Pomeroy, Caleb and Dasandi, Niheer and Jankin, Slava}, title = {Multiplex communities and the emergence of international conflict}, series = {PLoS One}, volume = {14}, journal = {PLoS One}, number = {10}, doi = {10.1371/journal.pone.0223040}, abstract = {Advances in community detection reveal new insights into multiplex and multilayer networks. Less work, however, investigates the relationship between these communities and outcomes in social systems. We leverage these advances to shed light on the relationship between the cooperative mesostructure of the international system and the onset of interstate conflict. We detect communities based upon weaker signals of affinity expressed in United Nations votes and speeches, as well as stronger signals observed across multiple layers of bilateral cooperation. Communities of diplomatic affinity display an expected negative relationship with conflict onset. Ties in communities based upon observed cooperation, however, display no effect under a standard model specification and a positive relationship with conflict under an alternative specification. These results align with some extant hypotheses but also point to a paucity in our understanding of the relationship between community structure and behavioral outcomes in networks.}, language = {en} } @techreport{DasandiJankin, type = {Working Paper}, author = {Dasandi, Niheer and Jankin, Slava}, title = {AI for SDG-16 on Peace, Justice, and Strong Institutions: Tracking Progress and Assessing Impact}, pages = {3}, abstract = {The transition from the Millennium Development Goals (MDGs) to the Sustainable Development Goals (SDGs) brought with it significant changes in the process of creating the goals and with the actual content of the SDGs. One of the most important developments was the inclusion of SDG 16, which recognises the central role of effective, accountable and inclusive political institutions in promoting sustainable development. Yet, a significant shortcoming is the difficulty in measuring progress on this SDG 16. In addition to general issues linked with data availability across the various indicators, a key challenge is aggregating trends across these wide-ranging indicators to track overall progress on SDG 16. A second issue that follows, is that despite claims regarding the centrality of SDG 16 for achieving the other SDGs, little is known about the causal pathways from the different indicators in SDG 16 to the other SDGs and associated indicators. In other words, questions remain over how changes in SDG 16 indicators impact a country's progress towards indicators linked to health, gender equality, water and sanitation, and climate change.}, language = {de} } @article{Jankin, author = {Jankin, Slava}, title = {Application of Natural Language Processing to Determine User Satisfaction in Public Services}, series = {EasyChair Preprint}, volume = {1103}, journal = {EasyChair Preprint}, abstract = {Research on user satisfaction has increased substantially in recent years. Studies to date tend to test for significance of pre-defined factors thought to have an influence with no scalable means to verify the validity of the assumptions made. Digital technology has enabled new methods to collect user feedback, for example through online forums where service users post comments. Topic models can help analyze large volumes of such feedback and are proposed as a feasible solution to aggregate user opinions for use in the public sector. Insights can contribute to a more inclusive decision-making process in public services. This novel approach is applied to process reviews of publicly-funded primary care practices in England. Findings from the analysis of over 200,000 reviews indicate that the quality of interactions with staff and bureaucratic exigencies are the key drivers of user satisfaction. Moreover, patient satisfaction is strongly influenced by factors not considered in state-of-the-art patient surveys. These results highlight the potential benefits that text mining and machine learning for the public administration field.}, language = {en} } @article{HerzogJankin, author = {Herzog, Alexander and Jankin, Slava}, title = {Intra-Cabinet Politics and Fiscal Governance in Times of Austerity}, series = {Political Science Research and Methods}, journal = {Political Science Research and Methods}, doi = {10.1017/psrm.2019.40}, pages = {1 -- 16}, abstract = {In the context of recent economic and financial crisis in Europe, questions about the power of the core executive to control fiscal outcomes are more important than ever. Why are some governments more effective in controlling spending while others fall prey to excessive overspending by individual cabinet ministers? We approach this question by lifting the veil of collective cabinet responsibility and focusing on intra-cabinet decision-making around budgetary allocation. Using the contributions of individual cabinet members during budget debates in Ireland, we estimate their positions on a latent dimension that represents their relative levels of support or opposition to the cabinet leadership. We find some evidence that ministers who are close to the finance minister receive a larger budget share, but under worsening macro-economic conditions closeness to the prime minister is a better predictor for budget allocations. Our results highlight potential fragility of the fiscal authority delegation mechanism in adverse economic environment.}, language = {en} } @article{KowalskiEsteveJankin, author = {Kowalski, Radoslaw and Esteve, Marc and Jankin, Slava}, title = {Improving Public Services by Mining Citizen Feedback: An Application of Natural Language Processing}, series = {Public Administration}, journal = {Public Administration}, doi = {10.1111/padm.12656}, abstract = {Research on user satisfaction has increased substantially in recent years. To date, most studies have tested the significance of pre-defined factors thought to influence user satisfaction, with no scalable means of verifying the validity of their assumptions. Digital technology has created new methods of collecting user feedback where service users post comments. As topic models can analyze large volumes of feedback, they have been proposed as a feasible approach to aggregating user opinions. This novel approach has been applied to process reviews of primary-care practices in England. Findings from an analysis of more than 200,000 reviews show that the quality of interactions with staff and bureaucratic exigencies are the key drivers of user satisfaction. In addition, patient satisfaction is strongly influenced by factors that are not measured by state-of-the-art patient surveys. These results highlight the potential benefits of text mining and machine learning for public administration.}, language = {en} } @article{BecharaHerzogJankin, author = {B{\´e}chara, Hannah and Herzog, Alexander and Jankin, Slava}, title = {Transfer learning for topic labeling: Analysis of the UK House of Commons speeches 1935-2014}, series = {Research and Politics}, volume = {April-June 2021}, journal = {Research and Politics}, doi = {10.1177/20531680211022206}, abstract = {Topic models are widely used in natural language processing, allowing researchers to estimate the underlying themes in a collection of documents. Most topic models require the additional step of attaching meaningful labels to estimated topics, a process that is not scalable, suffers from human bias, and is difficult to replicate. We present a transfer topic labeling method that seeks to remedy these problems, using domain-specific codebooks as the knowledge base to automatically label estimated topics. We demonstrate our approach with a large-scale topic model analysis of the complete corpus of UK House of Commons speeches from 1935 to 2014, using the coding instructions of the Comparative Agendas Project to label topics. We evaluated our results using human expert coding and compared our approach with more current state-of-the-art neural methods. Our approach was simple to implement, compared favorably to expert judgments, and outperformed the neural networks model for a majority of the topics we estimated.}, language = {en} } @misc{MunzertJankinetal, author = {Munzert, Simon and Jankin, Slava and et al.,}, title = {The 2021 report of the Lancet Countdown on health and climate change: code red for a healthy future}, series = {The Lancet}, journal = {The Lancet}, edition = {10311}, doi = {10.1016/S0140-6736(21)01787-6}, pages = {1619 -- 1662}, abstract = {The Lancet Countdown is an international collaboration that independently monitors the health consequences of a changing climate. Publishing updated, new, and improved indicators each year, the Lancet Countdown represents the consensus of leading researchers from 43 academic institutions and UN agencies. The 44 indicators of this report expose an unabated rise in the health impacts of climate change and the current health consequences of the delayed and inconsistent response of countries around the globe—providing a clear imperative for accelerated action that puts the health of people and planet above all else. The 2021 report coincides with the UN Framework Convention on Climate Change 26th Conference of the Parties (COP26), at which countries are facing pressure to realise the ambition of the Paris Agreement to keep the global average temperature rise to 1·5°C and to mobilise the financial resources required for all countries to have an effective climate response. These negotiations unfold in the context of the COVID-19 pandemic—a global health crisis that has claimed millions of lives, affected livelihoods and communities around the globe, and exposed deep fissures and inequities in the world's capacity to cope with, and respond to, health emergencies. Yet, in its response to both crises, the world is faced with an unprecedented opportunity to ensure a healthy future for all.}, language = {en} } @article{JankinUshakova, author = {Jankin, Slava and Ushakova, Anastasia}, title = {Big data to the rescue? Challenges in analysing granular household electricity consumption in the United Kingdom}, series = {Energy Research \& Social Science}, volume = {64}, journal = {Energy Research \& Social Science}, doi = {10.1016/j.erss.2020.101428}, abstract = {Rapid growth in smart meter installations has given rise to vast collections of data at a high time-resolution and down to an individual level. However, to enable efficient policy interventions, we need to be able to appropriately segment the population of users. The aim of this paper is to consider challenges and opportunities associated with large highly-granular temporal datasets that describe residential electricity consumption. In particular, the focus is on experiments relating to aggregation of smart meter time-series data in the context of clustering and prediction tasks that are often used for customer targeting and to gain insight on energy-use about sub populations. To cluster energy use profiles, we propose a novel framework based on a set of Gaussian based models which we use to encode individuals' energy consumption over time. The dataset consists of half hourly electricity consumption records from smart meters of households in the UK (2014-2015). The contribution of this paper comes from its investigation of how consumers or groups may be clustered according to model parameters in scenarios where additional data on consumers is not available to the researcher, or where anonymity preservation of the smart meter user is prioritised. A secondary aim is to invite greater awareness when data reduction is required to reduce the size of a large dataset for computational purposes. This may have implications for policy interventions acting at the individual or small group level, for instance, when designing incentives to encourage energy efficient behaviour or when identifying fuel poor customers.}, language = {en} } @article{ChatsiouJankin, author = {Chatsiou, Kakia and Jankin, Slava}, title = {Deep Learning for Political Science}, series = {The SAGE Handbook of Research Methods in Political Science and International Relations}, journal = {The SAGE Handbook of Research Methods in Political Science and International Relations}, abstract = {Political science, and social science in general, have traditionally been using computational methods to study areas such as voting behavior, policy making, international conflict, and international development. More recently, increasingly available quantities of data are being combined with improved algorithms and affordable computational resources to predict, learn, and discover new insights from data that is large in volume and variety. New developments in the areas of machine learning, deep learning, natural language processing (NLP), and, more generally, artificial intelligence (AI) are opening up new opportunities for testing theories and evaluating the impact of interventions and programs in a more dynamic and effective way. Applications using large volumes of structured and unstructured data are becoming common in government and industry, and increasingly also in social science research. This chapter offers an introduction to such methods drawing examples from political science. Focusing on the areas where the strengths of the methods coincide with challenges in these fields, the chapter first presents an introduction to AI and its core technology - machine learning, with its rapidly developing subfield of deep learning. The discussion of deep neural networks is illustrated with the NLP tasks that are relevant to political science. The latest advances in deep learning methods for NLP are also reviewed, together with their potential for improving information extraction and pattern recognition from political science texts.}, language = {en} } @article{CampionGascoJankinetal., author = {Campion, Averill and Gasco, Mila and Jankin, Slava and Esteve, Marc}, title = {Managing Artificial Intelligence Deployment in the Public Sector}, series = {Computer/IEEE, 2020}, volume = {53}, journal = {Computer/IEEE, 2020}, number = {10}, doi = {10.1109/MC.2020.2995644}, pages = {28 -- 37}, abstract = {The scarcity of empirical evidence surrounding the organizational challenges and successful approaches to artificial intelligence (AI) deployment has resulted in mostly theoretical conceptualizations. By analyzing policy labs and offices of data analytics across the US to understand organizational challenges of AI adoption and implementation in the public sector as well as to identify successful management strategies to address such challenges, our study moves from speculation to gathering evidence. Our findings show that most challenges are found during the implementation stage and include challenges related to skills, culture, and resistance to share the data driven by data challenges. Further, our results indicate that long term strategies and short term actions need to be put in place to address these challenges. Among the first ones, leadership and executive support and stakeholder management seem to play an important role. Data standardization, training, and data-sharing agreements also seem to be successful specific short-term actions.}, language = {en} } @article{DasandiGrahamLampardetal., author = {Dasandi, Niheer and Graham, Hilary and Lampard, Pete and Jankin, Slava}, title = {Engagement with health in national climate change commitments under the Paris Agreement: a global mixed-methods analysis of the nationally determined contributions}, series = {Lancet Planetary Health}, volume = {5}, journal = {Lancet Planetary Health}, number = {2}, doi = {10.1016/S2542-5196(20)30302-8}, pages = {93 -- 101}, abstract = {Background: Instituted under the Paris Agreement, nationally determined contributions (NDCs) outline countries' plans for mitigating and adapting to climate change. They are the primary policy instrument for protecting people's health in the face of rising global temperatures. However, evidence on engagement with health in the NDCs is scarce. In this study, we aimed to examine how public health is incorporated in the NDCs, and how different patterns of engagement might be related to broader inequalities and tensions in global climate politics. Methods: We analysed the NDCs in the UN Framework Convention on Climate Change registry submitted by 185 countries. Using content analysis and natural language processing (NLP) methods, we developed measures of health engagement. Multivariate regression analyses examined whether country-level factors (eg, population size, gross domestic product [GDP], and climate-related exposures) were associated with greater health engagement. Using NLP methods, we compared health engagement with other climate-related challenges (ie, economy, energy, and agriculture) and examined broader differences in the keyword terms used in countries with high and low health engagement in their NDCs. Findings: Countries that did not mention health in their NDCs were clustered in high-income countries, whereas greater health engagement was concentrated in low-income and middle-income countries. Having a low GDP per capita and being a small island developing state were associated with higher levels of health engagement. In addition, higher levels of population exposure to temperature change and ambient air pollution were associated with more health coverage included in a country's NDC. Variation in health engagement was greater than for other climate-related issues and reflected wider differences in countries' approaches to the NDCs. Interpretation: A focus on health in the NDCs follows broader patterns of global inequalities. Poorer and climate-vulnerable countries that contribute least to climate change are more likely to engage with health in their NDCs, while richer countries focus on non-health sectors in their NDCs, such as energy and the economy.}, language = {en} } @article{DasandiGrahamLampardetal., author = {Dasandi, Niheer and Graham, Hilary and Lampard, Pete and Jankin, Slava}, title = {Intergovernmental engagement on health impacts of climate change}, series = {Bulletin of the World Health Organization}, volume = {102}, journal = {Bulletin of the World Health Organization}, number = {111}, doi = {10.2471/BLT.20.270033}, abstract = {Objective: To examine country engagement with the health impacts of climate change in (1) annual statements in the UN General Debate (UNGD); and (2) the Nationally Determined Contributions (NDCs) of the Paris Agreement; and to identify what factors drive country engagement. Methods: First, we measure engagement by the total references to the health and climate change relationship in each text, using a keyword-in-context search with relevant search terms. Second, we use machine learning models, specifically random forest models, to identify the most important country-level predictors of engagement. Our predictors are political and economic factors, health outcomes, climate change-related variables, and membership of political negotiating groups in the UN. Findings: For both UNGD statements and NDCs, we find that that low- and middle-income countries discuss the health impacts of climate change much more than high-income countries. We find that the most important predictors of country engagement are related health outcomes (infant mortality rates, maternal death risk, life expectancy), countries' income levels (GDP per capita), and fossil fuel consumption. Membership of political negotiating groups (e.g. G77 and SIDS) is less important predictors. Conclusion: Our analysis indicates a North-South division in engagement, and hence there is little to suggest that a health framing of climate change overcomes existing geopolitical divisions in climate change negotiations. Countries who carry the heaviest health burdens but lack necessary resources to address the impacts of climate change are shouldering responsibility for reminding the global community of the implications of climate change for people's health.}, language = {en} } @article{WattsJankinMunzertetal., author = {Watts, Nick and Jankin, Slava and Munzert, Simon and Amann, Markus and Arnell, Nigel and Ayeb-Karlsson, Sonja and Beagley, Jessica and Belesova, Kristine and Boykoff, Maxwell and Byass, Peter and Cai, Wenjia and Campbell-Lendrum, Diarmid and Capstick, Stuart and Chambers, Jonathan and Coleman, Samantha and Dalin, Carole and Daly, Meaghan and Dasandi, Niheer}, title = {The 2020 report of The Lancet Countdown on health and climate change: responding to converging crises}, series = {The Lancet}, volume = {397}, journal = {The Lancet}, number = {10269}, doi = {10.1016/S0140-6736(20)32290-X}, pages = {129 -- 170}, abstract = {The world has already warmed by more than 1.2C compared with preindustrial levels, resulting in profound, immediate, and rapidly worsening health effects, and moving dangerously close to the agreed limit of maintaining temperatures "well below 2C". These health impacts are seen on every continent, with the ongoing spread of dengue virus across South America, the cardiovascular and respiratory effects of record heatwaves and wildfires in Australia, western North America, and western Europe, and the undernutrition and mental health effects of floods and droughts in China, Bangladesh, Ethiopia, and South Africa. In the long term, climate change threatens the very foundations of human health and wellbeing, with the Global Risks Report registering climate change as one of the five most damaging or probable global risks every year for the past decade.}, language = {en} } @article{UshakovaJankin, author = {Ushakova, Anastasia and Jankin, Slava}, title = {Big data to the rescue? Challenges in analysing granular household electricity consumption in the United Kingdom}, series = {Energy Research \& Social Science}, volume = {64}, journal = {Energy Research \& Social Science}, issn = {2214-6296}, doi = {10.1016/j.erss.2020.101428}, abstract = {Rapid growth in smart meter installations has given rise to vast collections of data at a high time-resolution and down to an individual level. However, to enable efficient policy interventions, we need to be able to appropriately segment the population of users. The aim of this paper is to consider challenges and opportunities associated with large highly-granular temporal datasets that describe residential electricity consumption. In particular, the focus is on experiments relating to aggregation of smart meter time-series data in the context of clustering and prediction tasks that are often used for customer targeting and to gain insight on energy-use about sub populations. To cluster energy use profiles, we propose a novel framework based on a set of Gaussian based models which we use to encode individuals' energy consumption over time. The dataset consists of half hourly electricity consumption records from smart meters of households in the UK (2014-2015). The contribution of this paper comes from its investigation of how consumers or groups may be clustered according to model parameters in scenarios where additional data on consumers is not available to the researcher, or where anonymity preservation of the smart meter user is prioritised. A secondary aim is to invite greater awareness when data reduction is required to reduce the size of a large dataset for computational purposes. This may have implications for policy interventions acting at the individual or small group level, for instance, when designing incentives to encourage energy efficient behaviour or when identifying fuel poor customers.}, language = {en} } @article{JankinRomanellovanDaalenetal., author = {Jankin, Slava and Romanello, Marina and van Daalen, Kim and Anto, Joesp M. and Dasandi, Niheer and Drummond, Paul and Hamilton, Ian G. and Kendrovski, Vladimir and Lowe, Rachel and Rockl{\"o}v, Joacim and Schmoll, Oliver and Semenza, Jan C. and Tonne, Cathryn and Nilsson, Maria}, title = {Tracking progress on health and climate change in Europe}, series = {The Lancet Public Health}, journal = {The Lancet Public Health}, doi = {10.1016/S2468-2667(21)00207-3}, abstract = {Left unabated, climate change will have catastrophic effects on the health of present and future generations. Such effects are already seen in Europe, through more frequent and severe extreme weather events, alterations to water and food systems, and changes in the environmental suitability for infectious diseases. As one of the largest current and historical contributors to greenhouse gases and the largest provider of financing for climate change mitigation and adaptation, Europe's response is crucial, for both human health and the planet. To ensure that health and wellbeing are protected in this response it is essential to build the capacity to understand, monitor, and quantify health impacts of climate change and the health co-benefits of accelerated action. Responding to this need, the Lancet Countdown in Europe is established as a transdisciplinary research collaboration for monitoring progress on health and climate change in Europe. With the wealth of data and academic expertise available in Europe, the collaboration will develop region-specific indicators to address the main challenges and opportunities of Europe's response to climate change for health. The indicators produced by the collaboration will provide information to health and climate policy decision making, and will also contribute to the European Observatory on Climate and Health.}, language = {en} } @article{RomanelloDiNapoliDrummondetal., author = {Romanello, Marina and Di Napoli, Claudia and Drummond, Paul and Green, Carole and Kennard, Harry and Lampard, Pete and Scamman, Daniel and Arnell, Nigel and Ayeb-Karlsson, Sonja and Berrang Ford, Lea and Belesova, Kristine and Bowen, Kathryn and Cai, Wenjia and Callaghan, Max and Campbell-Lendrum, Diarmid and Chambers, Jonathan and Daalen, Kim R van and Dalin, Carole and Dasandi, Niheer and Dasgupta, Shouro and Davies, Michael and Dominguez-Salas, Paula and Dubrow, Robert and Ebi, Kristie L and Eckelman, Matthew and Ekins, Paul and Escobar, Luis E and Georgeson, Lucien and Graham, Hilary and Gunther, Samuel H and Hamilton, Ian and Hang, Yun and H{\"a}nninen, Risto and Hartinger, Stella and He, Kehan and Hess, Jeremy J and Hsu, Shih-Che and Jankin, Slava and Jamart, Louis and Jay, Ollie and Kelman, Ilan and Kiesewetter, Gregor and Kinney, Patrick and Kjellstrom, Tord and Kniveton, Dominic and Lee, Jason K W and Lemke, Bruno and Liu, Zhao and Lott, Melissa and Lotto Batista, Martin and Lowe, Rachel and MacGuire, Frances and Sewe, Maquins Odhiambo and Martinez-Urtaza, Jaime and Maslin, Mark and McAllister, Lucy and McGushin, Alice and McMichael, Celia and Mi, Zhifu and Milner, James and Minor, Kelton and Minx, Jan C and Mohajeri, Nahid and Moradi-Lakeh, Maziar and Morrissey, Karyn and Munzert, Simon and Murray, Kris A and Neville, Tara and Nilsson, Maria and Obradovich, Nick and O'Hare, Megan B and Oreszczyn, Tadj and Otto, Matthias and Owfi, Fereidoon and Pearman, Olivia and Rabbaniha, Mahnaz and Robinson, Elizabeth J Z and Rockl{\"o}v, Joacim and Salas, Renee N and Semenza, Jan C and Sherman, Jodi D and Shi, Liuhua and Shumake-Guillemot, Joy and Silbert, Grant and Sofiev, Mikhail and Springmann, Marco and Stowell, Jennifer and Tabatabaei, Meisam and Taylor, Jonathon and Tri{\~n}anes, Joaquin and Wagner, Fabian and Wilkinson, Paul and Winning, Matthew and Yglesias-Gonz{\´a}lez, Marisol and Zhang, Shihui and Gong, Peng and Montgomery, Hugh and Costello, Anthony}, title = {The 2022 report of the Lancet Countdown on health and climate change: health at the mercy of fossil fuels}, series = {The Lancet}, volume = {400}, journal = {The Lancet}, number = {10363}, doi = {10.1016/S0140-6736(22)01540-9}, pages = {1619 -- 1654}, language = {en} } @article{RomanelloWalawenderHsuetal., author = {Romanello, Marina and Walawender, Maria and Hsu, Shih-Che and Moskeland, Annalyse and Palmeiro-Silva, Yasna and Scamman, Daniel and Ali, Zakari and Ameli, Nadia and Angelova, Denitsa and Ayeb-Karlsson, Sonja and Basart, Sara and Beagley, Jessica and Beggs, Paul J and Blanco-Villafuerte, Luciana and Cai, Wenjia and Callaghan, Max and Campbell-Lendrum, Diarmid and Chambers, Jonathan D and Chicmana-Zapata, Victoria and Chu, Lingzhi and Cross, Troy J and van Daalen, Kim R and Dalin, Carole and Dasandi, Niheer and Dasgupta, Shouro and Davies, Michael and Dubrow, Robert and Eckelman, Matthew J and Ford, James D and Freyberg, Chris and Gasparyan, Olga and Gordon-Strachan, Georgiana and Grubb, Michael and Gunther, Samuel H and Hamilton, Ian and Hang, Yun and H{\"a}nninen, Risto and Hartinger, Stella and He, Kehan and Heidecke, Julian and Hess, Jeremy J and Jamart, Louis and Jankin, Slava and Jatkar, Harshavardhan and Jay, Ollie and Kelman, Ilan and Kennard, Harry and Kiesewetter, Gregor and Kinney, Patrick and Kniveton, Dominic and Kouznetsov, Rostislav and Lampard, Pete and Lee, Jason K W and Lemke, Bruno and Li, Bo and Liu, Yang and Liu, Zhao and Llabr{\´e}s-Brustenga, Alba and Lott, Melissa and Lowe, Rachel and Martinez-Urtaza, Jaime and Maslin, Mark and McAllister, Lucy and McMichael, Celia and Mi, Zhifu and Milner, James and Minor, Kelton and Minx, Jan and Mohajeri, Nahid and Momen, Natalie C and Moradi-Lakeh, Maziar and Morrisey, Karyn and Munzert, Simon and Murray, Kris A and Obradovich, Nick and O'Hare, Megan B and Oliveira, Camile and Oreszczyn, Tadj and Otto, Matthias and Owfi, Fereidoon and Pearman, Olivia L and Pega, Frank and Perishing, Andrew J and Pinho-Gomes, Ana-Catarina and Ponmattam, Jamie and Rabbaniha, Mahnaz and Rickman, Jamie and Robinson, Elizabeth and Rockl{\"o}v, Joacim and Rojas-Rueda, David and Salas, Renee N and Semenza, Jan C and Sherman, Jodi D and Shumake-Guillemot, Joy and Singh, Pratik and Sj{\"o}din, Henrik and Slater, Jessica and Sofiev, Mikhail and Sorensen, Cecilia and Springmann, Marco and Stalhandske, Z{\´e}lie and Stowell, Jennifer D and Tabatabaei, Meisam and Taylor, Jonathon and Tong, Daniel and Tonne, Cathryn and Treskova, Marina and Trinanes, Joaquin A and Uppstu, Andreas and Wagner, Fabian and Warnecke, Laura and Whitcombe, Hannah and Xian, Peng and Zavaleta-Cortijo, Carol and Zhang, Chi and Zhang, Ran and Zhang, Shihui and Zhang, Ying and Zhu, Qiao and Gong, Peng and Montgomery, Hugh and Costello, Anthony}, title = {The 2024 report of the Lancet Countdown on health and climate change: facing record-breaking threats from delayed action}, series = {The Lancet}, volume = {404}, journal = {The Lancet}, number = {10465}, publisher = {Elsevier BV}, issn = {0140-6736}, doi = {10.1016/S0140-6736(24)01822-1}, pages = {1847 -- 1896}, language = {en} } @article{ChelottiDasandiJankinMikhaylov, author = {Chelotti, Nicola and Dasandi, Niheer and Jankin Mikhaylov, Slava}, title = {Do Intergovernmental Organizations Have a Socialization Effect on Member State Preferences? Evidence from the UN General Debate}, series = {International Studies Quarterly}, volume = {66}, journal = {International Studies Quarterly}, number = {1}, publisher = {Oxford University Press (OUP)}, issn = {0020-8833}, doi = {10.1093/isq/sqab069}, abstract = {The question of whether intergovernmental organizations (IGOs) have a socialization effect on member state preferences is central to international relations. However, empirical studies have struggled to separate the socializing effects of IGOs on preferences from the coercion and incentives associated with IGOs that may lead to foreign policy alignment without altering preferences. This article addresses this issue. We adopt a novel approach to measuring state preferences by applying text analytic methods to country statements in the annual United Nations General Debate (UNGD). The absence of interstate coordination with UNGD statements makes them particularly well suited for testing socialization effects on state preferences. We focus on the European Union (EU), enabling us to incorporate the pre-accession period—when states have the strongest incentives for foreign policy alignment—into our analysis. The results of our analysis show that EU membership has a socialization effect that produces preference convergence, controlling for coercion and incentive effects.}, language = {en} } @article{DasandiCaiFribergetal., author = {Dasandi, Niheer and Cai, Wenjia and Friberg, Peter and Jankin, Slava and Kuylenstierna, Johan and Nilsson, Maria}, title = {The inclusion of health in major global reports on climate change and biodiversity}, series = {BMJ Global Health}, volume = {7}, journal = {BMJ Global Health}, number = {6}, publisher = {BMJ}, issn = {2059-7908}, doi = {10.1136/bmjgh-2022-008731}, abstract = {This article argues that human health has become a key consideration in recent global reports on climate change and biodiversity produced by various international organisations; however, greater attention must be given to the unequal health impacts of climate change and biodiversity loss around the world and the different health adaptation measures that are urgently required.}, language = {en} } @article{WeeksJankinMikhaylovHerzogetal., author = {Weeks, Liam and Jankin Mikhaylov, Slava and Herzog, Alex and {\´O} Fathartaigh, M{\´i}che{\´a}l and Bechara, Hannah}, title = {It's Only Words? Analysing the Roots of the Irish Party System Using Historical Parliamentary Debates}, series = {Parliamentary Affairs}, journal = {Parliamentary Affairs}, issn = {0031-2290}, doi = {10.1093/pa/gsac020}, abstract = {While the public image of legislative debates is often less than favourable, parliamentary deliberations can be an important indicator of policy preferences, issue saliency and cohesion within political parties. We consider the case of a parliamentary debate that had a considerable long-term political legacy, forging a party system that endured for almost a century. The debates in the Irish parliament over the 1921 Anglo-Irish Treaty were a critical juncture that split a dominant party, resulting in, first, a civil war and, later, a new mode of party competition. We analyse the text of the debates from this period to see if they contribute to a greater understanding of the ensuing split. Few differences between the two sides in parliament are found, which might explain why few were the differences between the key actors in the party system that evolved.}, language = {en} } @article{MuellerHansenLeeCallaghanetal., author = {M{\"u}ller-Hansen, Finn and Lee, Yuan Ting and Callaghan, Max and Jankin, Slava and Minx, Jan C.}, title = {The German coal debate on Twitter: Reactions to a corporate policy process}, series = {Energy Policy}, volume = {169}, journal = {Energy Policy}, publisher = {Elsevier BV}, issn = {0301-4215}, doi = {10.1016/j.enpol.2022.113178}, abstract = {Phasing out coal is a prerequisite to achieving the Paris climate mitigation targets. In 2018, the German government established a multi-stakeholder commission with the mandate to negotiate a plan for the national coal phase-out, fueling a continued public debate over the future of coal. This study analyzes the German coal debate on Twitter before, during, and after the session of the so-called Coal Commission, over a period of three years. In particular, we investigate whether and how the work of the commission translated into shared perceptions and sentiments in the public debate on Twitter. We find that the sentiment of the German coal debate on Twitter becomes increasingly negative over time. In addition, the sentiment becomes more polarized over time due to an increase in the use of more negative and positive language. The analysis of retweet networks shows no increase in interactions between communities over time. These findings suggest that the Coal Commission did not further consensus in the coal debate on Twitter. While the debate on social media only represents a section of the national debate, it provides insights for policy-makers to evaluate the interaction of multi-stakeholder commissions and public debates.}, language = {en} } @article{DasandiGrahamHudsonetal., author = {Dasandi, Niheer and Graham, Hilary and Hudson, David and Jankin, Slava and vanHeerde-Hudson, Jennifer and Watts, Nick}, title = {Positive, global, and health or environment framing bolsters public support for climate policies}, series = {Communications Earth \& Environment}, volume = {3}, journal = {Communications Earth \& Environment}, number = {1}, publisher = {Springer Science and Business Media LLC}, issn = {2662-4435}, doi = {10.1038/s43247-022-00571-x}, abstract = {Public support for climate policies is important for their efficacy, yet little is known about how different framings of climate change affect public support for climate policies around the world. Here we report findings from a conjoint experiment of 7,500 adults in five countries - China, Germany, India, UK, and USA - to identify climate messages that elicit greater support for policies to tackle climate change. Messages were randomly varied on four attributes: positive (opportunity) or negative (threat) framings, theme (health, environment, economy, migration), scale (individual, community, national, global), and time (current, 2030, 2050). We find that a positive frame, health and environmental frames, and global and immediate frames bolster public support. We examine differences between countries, and across groups within countries - particularly focusing on the effect of these frames among individuals that are unconcerned about climate change. Among this group, positive and health frames increase the likelihood of support for climate policies, indicating the relevance of these frames for shifting policy preferences for different audience groups.}, language = {en} } @article{vanDaalenRomanelloRockloevetal., author = {van Daalen, Kim R and Romanello, Marina and Rockl{\"o}v, Joacim and Semenza, Jan C and Tonne, Cathryn and Markandya, Anil and Dasandi, Niheer and Jankin, Slava and Achebak, Hicham and Ballester, Joan and Bechara, Hannah and Callaghan, Max W and Chambers, Jonathan and Dasgupta, Shouro and Drummond, Paul and Farooq, Zia and Gasparyan, Olga and Gonzalez-Reviriego, Nube and Hamilton, Ian and H{\"a}nninen, Risto and Kazmierczak, Aleksandra and Kendrovski, Vladimir and Kennard, Harry and Kiesewetter, Gregor and Lloyd, Simon J and Lotto Batista, Martin and Martinez-Urtaza, Jaime and Mil{\`a}, Carles and Minx, Jan C and Nieuwenhuijsen, Mark and Palamarchuk, Julia and Quijal-Zamorano, Marcos and Robinson, Elizabeth J Z and Scamman, Daniel and Schmoll, Oliver and Sewe, Maquins Odhiambo and Sj{\"o}din, Henrik and Sofiev, Mikhail and Solaraju-Murali, Balakrishnan and Springmann, Marco and Tri{\~n}anes, Joaquin and Anto, Josep M and Nilsson, Maria and Lowe, Rachel}, title = {The 2022 Europe report of the Lancet Countdown on health and climate change: towards a climate resilient future}, series = {The Lancet Public Health}, volume = {7}, journal = {The Lancet Public Health}, number = {11}, publisher = {Elsevier BV}, issn = {2468-2667}, doi = {10.1016/s2468-2667(22)00197-9}, pages = {E942 -- E965}, language = {en} } @article{RomanelloNapoliGreenetal., author = {Romanello, Marina and Napoli, Claudia di and Green, Carole and Kennard, Harry and Lampard, Pete and Scamman, Daniel and Walawender, Maria and Ali, Zakari and Ameli, Nadia and Ayeb-Karlsson, Sonja and Beggs, Paul J and Belesova, Kristine and Berrang Ford, Lea and Bowen, Kathryn and Cai, Wenjia and Callaghan, Max and Campbell-Lendrum, Diarmid and Chambers, Jonathan and Cross, Troy J and van Daalen, Kim R and Dalin, Carole and Dasandi, Niheer and Dasgupta, Shouro and Davies, Michael and Dominguez-Salas, Paula and Dubrow, Robert and Ebi, Kristie L and Eckelman, Matthew and Ekins, Paul and Freyberg, Chris and Gasparyan, Olga and Gordon-Strachan, Georgiana and Graham, Hilary and Gunther, Samuel H and Hamilton, Ian and Hang, Yun and H{\"a}nninen, Risto and Hartinger, Stella and He, Kehan and Heidecke, Julian and Hess, Jeremy J and Hsu, Shih-Che and Jamart, Louis and Jankin, Slava and Jay, Ollie and Kelman, Ilan and Kiesewetter, Gregor and Kinney, Patrick and Kniveton, Dominic and Kouznetsov, Rostislav and Larosa, Francesca and Lee, Jason K W and Lemke, Bruno and Liu, Yang and Liu, Zhao and Lott, Melissa and Lotto Batista, Mart{\´i}n and Lowe, Rachel and Odhiambo Sewe, Maquins and Martinez-Urtaza, Jaime and Maslin, Mark and McAllister, Lucy and McMichael, Celia and Mi, Zhifu and Milner, James and Minor, Kelton and Minx, Jan C and Mohajeri, Nahid and Momen, Natalie C and Moradi-Lakeh, Maziar and Morrissey, Karyn and Munzert, Simon and Murray, Kris A and Neville, Tara and Nilsson, Maria and Obradovich, Nick and O'Hare, Megan B and Oliveira, Camile and Oreszczyn, Tadj and Otto, Matthias and Owfi, Fereidoon and Pearman, Olivia and Pega, Frank and Pershing, Andrew and Rabbaniha, Mahnaz and Rickman, Jamie and Robinson, Elizabeth J Z and Rockl{\"o}v, Joacim and Salas, Renee N and Semenza, Jan C and Sherman, Jodi D and Shumake-Guillemot, Joy and Silbert, Grant and Sofiev, Mikhail and Springmann, Marco and Stowell, Jennifer D and Tabatabaei, Meisam and Taylor, Jonathon and Thompson, Ross and Tonne, Cathryn and Treskova, Marina and Trinanes, Joaquin A and Wagner, Fabian and Warnecke, Laura and Whitcombe, Hannah and Winning, Matthew and Wyns, Arthur and Yglesias-Gonz{\´a}lez, Marisol and Zhang, Shihui and Zhang, Ying and Zhu, Qiao and Gong, Peng and Montgomery, Hugh and Costello, Anthony}, title = {The 2023 report of the Lancet Countdown on health and climate change: the imperative for a health-centred response in a world facing irreversible harms}, series = {The Lancet}, volume = {402}, journal = {The Lancet}, number = {10419}, doi = {10.1016/S0140-6736(23)01859-7}, pages = {2346 -- 2394}, language = {en} } @article{RomanelloWalawenderHsuetal., author = {Romanello, Marina and Walawender, Maria and Hsu, Shih-Che and Moskeland, Annalyse and Palmeiro-Silva, Yasna and Scamman, Daniel and Smallcombe, James W and Abdullah, Sabah and Ades, Melanie and Al-Maruf, Abdullah and Ameli, Nadia and Angelova, Denitsa and Ayeb-Karlsson, Sonja and Ballester, Joan and Basaga{\~n}a, Xavier and Bechara, Hannah and Beggs, Paul J and Cai, Wenjia and Campbell-Lendrum, Diarmid and Charnley, Gina E C and Courtenay, Orin and Cross, Troy J and Dalin, Carole and Dasandi, Niheer and Dasgupta, Shouro and Davies, Michael and Eckelman, Matthew and Freyberg, Chris and Garcia Corral, Paulina and Gasparyan, Olga and Giguere, Joseph and Gordon-Strachan, Georgiana and Gumy, Sophie and Gunther, Samuel H and Hamilton, Ian and Hang, Yun and H{\"a}nninen, Risto and Hartinger, Stella and He, Kehan and Heidecke, Julian and Hess, Jeremy J and Jankin, Slava and Jay, Ollie and Pantera, Dafni Kalatzi and Kelman, Ilan and Kennard, Harry and Kiesewetter, Gregor and Kinney, Patrick and Kniveton, Dominic and Koubi, Vally and Kouznetsov, Rostislav and Lampard, Pete and Lee, Jason K W and Lemke, Bruno and Li, Bo and Linke, Andrew and Liu, Yang and Liu, Zhao and Lowe, Rachel and Ma, Siqi and Mabhaudhi, Tafadzwanashe and Maia, Carla and Markandya, Anil and Martin, Greta and Martinez-Urtaza, Jaime and Maslin, Mark and McAllister, Lucy and McMichael, Celia and Mi, Zhifu and Milner, James and Minor, Kelton and Minx, Jan and Mohajeri, Nahid and Momen, Natalie C and Moradi-Lakeh, Maziar and Morrisey, Karyn and Munzert, Simon and Murray, Kris A and Obradovich, Nick and Orgen, Papa and Otto, Matthias and Owfi, Fereidoon and Pearman, Olivia L and Pega, Frank and Pershing, Andrew J and Pinho-Gomes, Ana-Catarina and Ponmattam, Jamie and Rabbaniha, Mahnaz and Repke, Tim and Roa, Jorge and Robinson, Elizabeth and Rockl{\"o}v, Joacim and Rojas-Rueda, David and Ruiz-Cabrejos, Jorge and Rusticucci, Matilde and Salas, Renee N and San Jos{\´e} Plana, Adri{\`a} and Semenza, Jan C and Sherman, Jodi D and Shumake-Guillemot, Joy and Singh, Pratik and Sj{\"o}din, Henrik and Smith, Matthew R and Sofiev, Mikhail and Sorensen, Cecilia and Springmann, Marco and Stowell, Jennifer D and Tabatabaei, Meisam and Tartarini, Federico and Taylor, Jonathon and Tonne, Cathryn and Treskova, Marina and Trinanes, Joaquin A and Uppstu, Andreas and Valdes-Ortega, Nicolas and Wagner, Fabian and Watts, Nick and Whitcombe, Hannah and Wood, Richard and Yang, Pu and Zhang, Ying and Zhang, Shaohui and Zhang, Chi and Zhang, Shihui and Zhu, Qiao and Gong, Peng and Montgomery, Hugh and Costello, Anthony}, title = {The 2025 report of the Lancet Countdown on health and climate change: climate change action offers a lifeline}, series = {The Lancet}, volume = {406}, journal = {The Lancet}, number = {10521}, publisher = {Elsevier BV}, doi = {10.1016/S0140-6736(25)01919-1}, pages = {2804 -- 2857}, language = {en} }