dataesr/scientific-paragraphs-categorization huggingface dataset:
Conclusion Advancing theories on sustainability science requires articulating existing SES frameworks to generalisable and replicable analysis of large scale systems. Achieving the sustainable development goals depends on distinguishing where a sustainable solution is context dependent or where it could be generalised to different SES arenas. Here we have advanced methods to identify SES by updating clustering routines with a sensitivity analysis that allow us to reinterpret a binational dataset in the Volta basin. By doing so, we identified where and under which conditions an agricultural innovation such as water infrastructure development is likely to influence the food security of farmers thriving in one of the most arid areas of the world. These patterns, although descriptive, can inform policy decisions.
2% since 1990 and some 71. 2% of the 2971 cities in the WHO air pollution database exceed recommendations of annual fine particulate matter exposure (Indicator 3. 5). The strength and coverage of carbon pricing covers only 13. 1% of global anthropogenic CO2 emissions, with the weighted average carbon price of these instruments at 8. 81USD/tCO2e in 2017 (Indicator 4. 7). Furthermore, responses to climate change have yet to fully take advantage of the health co-benefits of mitigation and adaptation interventions, with action taken to-date only yielding modest improvements in human wellbeing. In part, this reflects a need for further evidence and research on these ancillary effects and the cost-savings available. However, it also reflects a need for more joined-up policymaking across health and non-health ministries of national governments.
The extent to which human populations are exposed to this change, and thus the health implications of temperature change, depend on the detailed spatial-temporal trends of population and temperature over time. Temperature anomalies were calculated relative to 1986 to 2008, from the European Research Area (ERA) produced by the European Centre for Medium-Range Weather Forecasts (ECMWF). 15 This dataset uses climate reanalysis to give a description of recent climate, produced by combining models with observations. 16 The time series shown in Figure  1. 1 are global mean temperatures calculated from the gridded data, weighted by area (to avoid bias from measurements near the poles) and by population (to show the number of people exposed); these are described as "area weighted" and "exposure weighted", respectively.
Changes in population were obtained per country and the data projected onto the gridded population. 17  The increase in exposure relative to the global average is driven partly by growing population densities in India, parts of China and Sub-Saharan Africa. Accounting for population when assessing temperature change provides a vital insight into how human wellbeing is likely to be affected by temperature change, with the analysis here showing that temperature change where people are living is much higher than average global warming. Details of the global distribution of this warming can be found in Appendix 2. The health impacts of extremes of heat range from direct heat stress and heat stroke, through to exacerbations of pre-existing heart failure, and even an increased incidence of acute kidney injury resulting from dehydration in vulnerable populations.
The elderly, children under the age of 12 months, and people with chronic cardiovascular and renal disease are particularly sensitive to these changes. 10 re, a heatwave is defined as a period of more than 3 days where the minimum temperature is greater than the 99th percentile of the historical minima (1986-2008 average). 18 This metric therefore focuses on periods of high night-time temperatures, which are critical in denying vulnerable people vital recuperation between hot days. Heatwave data were calculated against the historical period 1986-2008. The population for the exposure calculations was limited to people over the age of 65 (as this age group is most vulnerable to the health impacts of heatwaves), which was obtained on a per-country basis from the UN World Population Prospects archives for each year considered.
4  Disaster impact is a function of hazard and vulnerability, with vulnerability from a climate change perspective sometimes defined as a function of exposure, sensitivity, and adaptive capacity. 22 This indicator measures the ratio of the number of deaths, to the number of people affected by weatherrelated disasters. Weather-related disasters included are: droughts, floods, extreme temperature events, storms and wildfires. The health impacts of weather-related disasters expand beyond mortality alone, including injuries, mental health impacts, spread of disease, and food and water insecurity. Data for the calculations for this indicator come from the Emergency Events Database (EM-DAT). 23, 24 Here, in line with the EM-DAT data used for analysis, a disaster is defined as either: 1) 10 or more people reported killed, 2) 100 or more people affected, 3) a declaration of a state of emergency, or 4) a call for international assistance.
Between 1994 and 2013, the frequency of reported weather-related events (mainly floods and storms) increased significantly. However, this trend may be partially accounted for by information systems having improved in the last 35 years, and statistical data are now more available as a result of increased socio-cultural sensitivity to disaster consequences and occurrence. 25 From 2007 to 2016, EM-DAT recorded an average of 306 weather-related disasters per annum, up 46% from the 1990-1999 average. However, owing to impressive poverty reduction and health adaptation efforts, this has not yet been accompanied by any discernible trend in number of deaths, nor in those affected by disasters, nor in the ratio of these two (Figure  1. 6a). Indeed, separating out the disasters by the type of climate and weather hazard associated with the disaster (Figure  1.
Data was obtained from NASA's Earth Observatory Databank, and mapped across to the significant basins outlined in Appendix 2. From 1985 to 2016, a 1 o C increase in SST (from an annual average of 22. 74 o C to 23. 73 o C) was recorded in these locations. 62 This indicator requires significant further work to draw out the attribution to climate change and the health outcomes that may result. A case study on food security and fish stocks in the Persian Gulf is presented in Appendix 2.
This indicator tracks the policy commitments of national governments for health and climate change adaptation. Data are drawn from the recent WHO Climate and Health Country Survey (Panel 2. 1). Of the 40 countries responding to this baseline survey, 30 reported having a national adaptation strategy for health, approved by their Ministry of Health or relevant health authority (Figure  2. 1). This number includes countries with a health component of their National Adaptation Plan (NAPs), which was established by the UNFCCC to help nations identity medium-and long-term adaptation needs and develop and implement programmes to address those needs. 82 There is a need for caution in extrapolating the results to global level, as many of the respondent countries have received support from WHO in developing and implementing their plans.
83, 84 Nonetheless, with 75% of respondents in the survey having an approved national health adaptation plan there is evidence of the recognition of the need to adapt to climate change. Countries with national health adaptation plans are found across all regions and, perhaps most significantly, among some of the most vulnerable countries across Africa, South East Asia and South America. In future iterations of the survey, data will be gathered on the content and quality of these adaptation plans, their level of implementation, the main priorities for health adaptation, internal monitoring and review processes, and the level of funding available to support policy interventions. Globally, 54. 5% of people live in cities, where key health infrastructure is often concentrated. 85 hese urban centres are increasingly at risk from climate change, with negative impacts predicted for human health and health services.
Data is drawn from the WHO Climate and Health Country Survey (Panel 2. 1). Only 40% of countries (16 out of 40) reported having taken measures to increase the climate resilience of their health infrastructure (Figure  2. 6). These results suggest widespread vulnerability of health system infrastructure to climate change. For example, only two out of nine responding countries in the African Region report efforts to improve the climate resiliency of health infrastructure. Similar trends were found across other WHO regions. This indicator does not capture the quality or effectiveness of efforts to build climate-resilient health system infrastructure. Nonetheless, it highlights the importance of ensuring that countries work to implement climate-resilient health infrastructure, as these findings suggest this is generally lacking.
Indicators in this section are broadly considered within the framework of Driving Force-Pressure-State-Exposure-Effect-Action (DPSEEA). The DPSEEA framework is recognized as being suitable for the development of environmental health indicators, and identification of entry points for policy intervention. 105 An adaptation of the framework for examination of the health co-benefits of climate change mitigation is explained in Appendix 4. Here, health co-benefit indicators are captured for four sectors: 1) energy, 2) transport, 3) food, and 4) healthcare. Appendix 4 provides more detailed discussion of the data and methods used.
TPES reflects the total amount of primary energy used in a specific country, accounting for the flow of energy imports and exports. 111 Commitments under the Paris Agreement should begin to lower the overall carbon intensity of TPES, with the aim of reducing to near-zero by 2050. Drawing on data from the International Energy Agency (IEA), this indicator shows that globally, since the 1990s, the carbon intensity of primary energy supply has remained between 55-56 tCO2/TJ. 112 However, a 53% growth in energy demand over the period has meant that global CO2 emissions have grown significantly. Rapidly, low and middle income countries (LMICs) have seen an increase in carbon intensity since the 1970s, driven by increased coal use (Figure  3. 1). For example, India's TPES has almost tripled since 1980, with the share of coal in the mix doubling (from 22% to 44%).
The dirtiest form of coal produces almost twice the carbon per unit of primary energy than the least carbon intensive fossil fuel -natural gas. 112 Given that a large share of coal is used for power generation, it is an important sector of focus, both to reduce CO2 emissions and mitigate a major source of air pollution. 112 is indicator of coal phase-out is the total primary coal supply (EJ) in the energy system (Figure  3. 2), which makes use of recent data from the IEA. 112 obally, coal use has increased by just under 60% since 1990. This is due to strong growth in global energy demand, and an increasing share of TPES coming from coal, rising from 26% to 29% between 1990 and 2014. 112 This growth has largely been driven by China's increasing use of coal in industry and for electricity production, particularly in the 2000s (see East Asia trend in Figure  3.
3. 5. 1: Exposure to air pollution in cities The data for this indicator makes use of the WHO's Urban Ambient Air Pollution Database, which compiles information from a range of public sources, including national and subnational reports and websites, regional networks, intergovernmental agencies, and academic publications. 125 The air pollution measurements are taken from monitoring stations located in urban background, residential, commercial, and mixed areas. The annual average density of emission sources in urban areas and the proximity of populations to those sources led the Lancet Countdown to focus on exposure in cities. For this indicator, the Lancet Countdown has combined the WHO database with the Sustainable Healthy Urban Environments (SHUE) database, presenting data on 246 randomly sampled cities across the world (stratified by national wealth, population size, and Bailey's Ecoregion) (Figure  3.
More specifically, this indicator follows the metric of fuel use for transportation on a per capita basis (TJ/person) by type of fuel. To develop this indicator, the Lancet Countdown draws on transport fuel data from the IEA and population data from the World Bank. 112 ile some transition away from carbon-intensive fuel use, towards increasing levels of fuel efficiency has occurred in select countries, transport is still heavily dominated by gasoline and diesel. Global transport fuel use has increased by almost 65% since 1970 on a per capita basis (Tj/person) (Figure  3. 9). However, non-conventional fuels (for example, electricity, biofuels, and natural gas) have been rapidly gaining traction since the 2000s, with more than two million electric vehicles having been sold around the globe since 2010, mostly in the US, China, Japan and some European countries (Figure  3.
167, 168 This indicator should be viewed in the context of the specific setting where this trend is examined (in some populations, meat consumption is a main source of food energy and provides essential micronutrients, as well as livelihoods). Data was constructed using data from the FAO food balance sheets, which comprises national supply and utilisation accounts of primary foods and processed commodities. 169 igure  3. 13. The total amount of ruminant meat available for human consumption in kg/capita/year by WHOdefined regions. The amount of ruminant meat available for consumption is high in the Americas and has remained relatively stable across 1990-2013. In Europe, the amount of ruminant meat was relatively high in 1990, declined rapidly from 1990-2000 and has remained stable from 2000-2013.
populous, with more economically valuable property and infrastructure (in absolute terms). A rather different picture emerges in Figure  4. 4, which presents the data in terms of 'intensity' -insured and uninsured economic losses per $1000 GDP (in US$2016). Between 2010 and 2016, high and upper-middle income countries experienced the least average annual economic loss as a proportion of GDP ($1. 45/$1000 GDP and $1. 95/$1000 GDP, respectively), with low and lower-middle income countries subject to somewhat higher values ($2. 65/$1000 GDP and $2. 3/$1000 GDP, respectively). Economic losses in low-income countries were more than three times as high in 2016 than in 2010. However, for 1990-2016, average annual values vary significantly (see Appendix 5 for the full dataset). Whilst high and upper-middle income countries maintain relatively similar values ($1.
Revenue must be explicitly allocated to climate change mitigation or adaptation, or for ETR, to be considered in these categories. If such explicit earmarking is not present, or no data is available, then revenue is assumed to be allocated to general funds. Indicator 4. 9: Spending on adaptation for health and health-related activities This indicator reports estimates of spending on health and health-related climate change adaptation and resilience. Many adaptation activities within and beyond the formal health sector yield health co-benefits, which are important to understand and capture. Here, estimates of the total health and health-related adaptation spending were derived from the Adaptation & Resilience to Climate Change (A&RCC) dataset produced by kMatrix. This global dataset, covering financial transactions relevant to climate change adaptation, was compiled from a relevant subset of over 27,000 independent databases and sources (such as public disclosures and reports from insurance companies, the financial sector, and governments).
'Health-related adaptation spending' was defined as that which additionally included adaptation spending from the agricultural sector (due to the centrality of food and nutrition to health) and disaster preparedness sector (due to the direct public health benefits that often result from these efforts). This data from the A&RCC dataset is reported here, showing health and health-related adaptation spending for Health-related adaptation and resilience spending, both national totals and per capita levels, is extremely low in low-income countries, and increase across the continuum towards high-income countries. Interestingly, health and health-related adaptation spending as a proportion of total adaptation spending is relatively constant across income groups.
The first major spike globally was in 2009, coinciding with COP15 (Conference of the Parties) in Copenhagen, for which there was high expectation. Newspaper reporting then dropped around 2010, but since 2011 has been rising overall globally. Data was assembled by accessing archives through the Lexis Nexis, Proquest and Factiva databases. These sources were selected through the weighting of four main factors: geographical diversity (favouring a greater geographical range), circulation (favouring higher circulating publications), national sources (rather than local/regional), and reliable access to archives over time (favouring those accessible consistently for longer periods). Search terms were aligned to those used for the indicators of scientific and political engagement and searches, with Boolean searches done in English and Spanish.
213 Two databases were used, PubMed and Web of Science, to identify papers through a bibliometric analysis using keyword searches (see Appendix 6 for details). 214 Inclusion and exclusion criteria were applied to capture the most relevant literature on the human health impacts of climate change within the chosen timeframe and papers were independently reviewed and screened three times to identify relevant publications. The General Debate (GD) takes place every September at the start of each new session of the United Nations General Assembly (UNGA). Governments use their annual statements to present their perspective on events and issues they consider the most important in global politics, and to call for greater action from the international community. All UN Member States can address the UNGA, free from external constraints.
Acknowledgements The Lancet Countdown would like to thank the  Wellcome Trust , in particular  Saskia Heijnen ,  Sarah Molten  and  Sophie Tunstall-Behrens , for its financial and strategic support-without which, this research collaboration would not be possible. While carrying out its work, the Lancet Countdown received invaluable technical advice and input from a number of individuals, including  Neil Adger  ( University of Exeter ),  Kevin Andrews  ( University of Colorado Boulder ),  Nigel Arnell  ( University of Reading ),  Rob Bailey  ( Chatham House ),  John Balbus  ( National Institute of Environmental Health Sciences ),  Simon Bennet  ( International Energy Agency) ,  Helen Berry  ( Australiana National University ),  Kathryn Brown  ( Climate Change Committee ),  Yossi Cadan  ( 350.
AAP -Ambient Air Pollution AUM -Assets Under Management BEV -Battery Electric Vehicle CDP -Carbon Disclosure Project CFU -Climate Funds Update CO2 -Carbon Dioxide COP -Conference of the Parties COPD -Chronic Obstructive Pulmonary Disease CPI -Consumer Price Indices DALYs -Disability Adjusted Life Years DPSEEA -Driving Force-Pressure-State-Exposure-Effect-Action ECMWF -European Centre for Medium-Range Weather Forecasts EJ -Exajoule EM-DAT -Emergency Events Database ERA -European Research Area ETR -Environmental Tax Reform ETS -Emissions Trading System EU -European Union EU28 -28 European Union Member States FAO -Food and Agriculture Organization of the United Nations FAZ -Frankfurter Allgemeine Zeitung FISE -Social Inclusion Energy Fund GBD -Global Burden of Disease GDP -Gross Domestic Product GHG -Greenhouse Gas GtCO2 -Gigatons of Carbon Dioxide GW -Gigawatt GWP -Gross World Product HAB -Harmful Algal Blooms HIC -High Income Countries ICS -Improved Cook Stove IEA -International Energy Agency IHR -International Health Regulations IPC -Infection Prevention and Control IPCC -Intergovernmental Panel on Climate Change IRENA -International Renewable Energy Agency LMICs -Low and Middle Income Countries LPG -Liquefied Petroleum Gas Mt -Megaton MtCO2e -Metric Tons of Carbon Dioxide Equivalent NAP -National Adaptation Plan NDCs = Nationally Determined Contributions NHMSs -National Meteorological  and Hydrological Services  NHS-National Health Service NOx -Nitrogen Oxide OECD -Organization for Economic Cooperation and Development PHEV -Plug-in Hybrid Electric Vehicle PM2.
We interpreted ostracode assemblage data, using paleoenvironmental indicators (see section 2. 1), community-scale statistics, and three multivariate analyses (see section 2. 3). The community-scale statistics include number of ostracode specimens, relative abundance (%) of each species, average frequency of each species, and Simpson index. The average frequency equals to ΣP i /n, where P i is the relative percentage of the species in each sample, and n is the total sample number. Simpson index, which is relatively insensitive to small sample size  (Magurran, 2003; Morris et al. , 2014) , is selected to represent the taxonomic diversity of each sample. The calculation formula follows  Simpson (1949) : 1ÀΣP i 2 , where P i is relative percentage of a species. The data analyses were done in R version 3.
The spatial and temporal coverage of the Landsat satellite imagery make it an ideal resource for the monitoring of water temperature over large territories at a moderate spatial and temporal scale at a low cost. We used Landsat 5 and Landsat 7 archive images to create the Lake Skin Surface Temperature (LakeSST) data set, which contains skin water surface temperature data for 442 French water bodies (natural lakes, reservoirs, ponds, gravel pit lakes and quarry lakes) for the period 1999-2016. We assessed the quality of the satellite temperature measurements by comparing them to in situ measurements and taking into account the cool skin and warm layer effects. To estimate these effects and to investigate the theoretical differences between the freshwater and seawater cases, we adapted the COARE 3.
0 algorithm to the freshwater environment. We also estimated the warm layer effect using in situ data. At the reservoir of Bimont, the estimated cool skin effect was about -0. 3 and -0. 6 • C most of time, while the warm layer effect at 0. 55 m was negligible on average, but could occasionally attain several degrees, and a cool layer was often observed in the night. The overall RMSE of the satellite-derived temperature measurements was about 1. 2 • C, similar to other applications of satellite images to estimate freshwater surface temperatures. The LakeSST data can be used for studies on the temporal evolution of lake water temperature and for geographical studies of temperature patterns.
, 2012; Prats and Danis, 2017) , and (iii) improve the estimation of surface heat and gas fluxes  (Lofgren and Zhu, 2000)  that is important to understand the thermal behaviour of lakes  (Henderson-Sellers, 1986). This data paper presents the data set LakeSST of skin surface water temperature for 442 French lake water bod-ies derived from Landsat thermal infrared imagery. LakeSST was produced as part of a project funded by the French National Office for Water and Aquatic Environments (ONEMA, now part of the French Agency for Biodiversity, AFB) to assess the advantages and limitations of satellite data to monitor the water temperature of inland water bodies. After recalling what is meant by surface temperature in this paper and the methodology used to derive skin temperatures from Landsat thermal infrared bands fully described by  Simon et al.