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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.
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.
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.
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.
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.
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.
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.
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.
The 2021 report of the Lancet Countdown on health and climate change: code red for a healthy future
(2021)
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.