A. The number of temperature-related deaths averted if Australia’s health system and the whole economy decarbonises by 2040 and 2050. B. The monetary equivalent welfare gain under a range of discount rates and emission trajectories.

The global food system contributes approximately one-quarter of greenhouse gas (GHG) emissions, with these dominated by the livestock sector. The projected increase in livestock demand is likely to undermine efforts to keep global average warming below a 2°C target. A carbon tax is often proposed as the preferred demand-side mechanism for reduced meat consumption. Previous studies, however, suggest that while this could prove successful in reducing net global emissions, it may worsen nutritional standards in lowest-income nations. An alternative market mechanism which may simultaneously reduce GHG emissions and improve health at all income levels is a reduction in the price of meat substitute products (meat-free proteins with particular nutritional and aesthetic similarities to meat). Using a combined ecological and health modeling approach, we project the associated GHG savings and health benefits associated with a stepwise reduction in the price of meat substitute products. Utilizing food demand elasticities, we quantify the substitution of meat commodities across a range of social acceptability scenarios. Our results show that meat substitute products—integrated within a “flexitarian” approach (primarily vegetarian but occasionally eating meat and fish)—have a large potential for reducing GHG emissions (up to 583 MtCO2e per year) and improving nutritional outcomes (up to 52,700 premature deaths avoided per year). However, this capacity is strongly dependent on a combination of price reductions and improved social acceptability of this product group; therefore both will be essential.

BackgroundClimate change is likely to further worsen ozone pollution in already heavily polluted areas, leading to increased ozone-related health burdens. However, little evidence exists in China, the world’s largest greenhouse gas emitter and most populated country. As China is embracing an aging population with changing population size and falling age-standardized mortality rates, the potential impact of population change on ozone-related health burdens is unclear. Moreover, little is known about the seasonal variation of ozone-related health burdens under climate change. We aimed to assess near-term (mid-21st century) future annual and seasonal excess mortality from short-term exposure to ambient ozone in 104 Chinese cities under 2 climate and emission change scenarios and 6 population change scenarios.Methods and findingsWe collected historical ambient ozone observations, population change projections, and baseline mortality rates in 104 cities across China during April 27, 2013, to October 31, 2015 (2013–2015), which included approximately 13% of the total population of mainland China. Using historical ozone monitoring data, we performed bias correction and spatially downscaled future ozone projections at a coarse spatial resolution (2.0° × 2.5°) for the period April 27, 2053, to October 31, 2055 (2053–2055), from a global chemistry–climate model to a fine spatial resolution (0.25° × 0.25°) under 2 Intergovernmental Panel on Climate Change Representative Concentration Pathways (RCPs): RCP4.5, a moderate global warming and emission scenario where global warming is between 1.5°C and 2.0°C, and RCP8.5, a high global warming and emission scenario where global warming exceeds 2.0°C. We then estimated the future annual and seasonal ozone-related acute excess mortality attributable to both climate and population changes using cause-specific, age-group-specific, and season-specific concentration–response functions (CRFs). We used Monte Carlo simulations to obtain empirical confidence intervals (eCIs), quantifying the uncertainty in CRFs and the variability across ensemble members (i.e., 3 predictions of future climate and air quality from slightly different starting conditions) of the global model. Estimates of future changes in annual ozone-related mortality are sensitive to the choice of global warming and emission scenario, decreasing under RCP4.5 (−24.0%) due to declining ozone precursor emissions but increasing under RCP8.5 (10.7%) due to warming climate in 2053–2055 relative to 2013–2015. Higher ambient ozone occurs under the high global warming and emission scenario (RCP8.5), leading to an excess 1,476 (95% eCI: 898 to 2,977) non-accidental deaths per year in 2053–2055 relative to 2013–2015. Future ozone-related acute excess mortality from cardiovascular diseases was 5–8 times greater than that from respiratory diseases. Ozone concentrations increase by 15.1 parts per billion (10−9) in colder months (November to April), contributing to a net yearly increase of 22.3% (95% eCI: 7.7% to 35.4%) in ozone-related mortality under RCP8.5. An aging population, with the proportion of the population aged 65 years and above increased from 8% in 2010 to 24%–33% in 2050, will substantially amplify future ozone-related mortality, leading to a net increase of 23,838 to 78,560 deaths (110% to 363%). Our analysis was mainly limited by using a single global chemistry–climate model and the statistical downscaling approach to project ozone changes under climate change.ConclusionsOur analysis shows increased future ozone-related acute excess mortality under the high global warming and emission scenario RCP8.5 for an aging population in China. Comparison with the lower global warming and emission scenario RCP4.5 suggests that climate change mitigation measures are needed to prevent a rising health burden from exposure to ambient ozone pollution in China.

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

Country: Name of the country.

Density (P/Km2): Population density measured in persons per square kilometer.

Abbreviation: Abbreviation or code representing the country.

Agricultural Land (%): Percentage of land area used for agricultural purposes.

Land Area (Km2): Total land area of the country in square kilometers.

Armed Forces Size: Size of the armed forces in the country.

Birth Rate: Number of births per 1,000 population per year.

Calling Code: International calling code for the country.

Capital/Major City: Name of the capital or major city.

CO2 Emissions: Carbon dioxide emissions in tons.

CPI: Consumer Price Index, a measure of inflation and purchasing power.

CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.

Currency_Code: Currency code used in the country.

Fertility Rate: Average number of children born to a woman during her lifetime.

Forested Area (%): Percentage of land area covered by forests.

Gasoline_Price: Price of gasoline per liter in local currency.

GDP: Gross Domestic Product, the total value of goods and services produced in the country.

Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.

Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.

Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.

Largest City: Name of the country's largest city.

Life Expectancy: Average number of years a newborn is expected to live.

Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.

Minimum Wage: Minimum wage level in local currency.

Official Language: Official language(s) spoken in the country.

Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.

Physicians per Thousand: Number of physicians per thousand people.

Population: Total population of the country.

Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.

Tax Revenue (%): Tax revenue as a percentage of GDP.

Total Tax Rate: Overall tax burden as a percentage of commercial profits.

Unemployment Rate: Percentage of the labor force that is unemployed.

Urban Population: Percentage of the population living in urban areas.

Latitude: Latitude coordinate of the country's location.

Longitude: Longitude coordinate of the country's location.

Potential Use Cases

Analyze population density and land area to study spatial distribution patterns.

Investigate the relationship between agricultural land and food security.

Examine carbon dioxide emissions and their impact on climate change.

Explore correlations between economic indicators such as GDP and various socio-economic factors.

Investigate educational enrollment rates and their implications for human capital development.

Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.

Study labor market dynamics through indicators such as labor force participation and unemployment rates.

Investigate the role of taxation and its impact on economic development.

Explore urbanization trends and their social and environmental consequences.

IntroductionAnimal agriculture has exponentially grown in recent decades in response to the rise in global demand for meat, even in countries like Italy that traditionally eat a Mediterranean, plant-based diet. Globalization related dietary changes are contributing to the epidemic of non-communicable diseases and to the global climate crisis, and are associated with huge carbon and water footprints.The objective of the study is to assess inequalities in health impacts and in attributable greenhouse gases-GHG emissions in Italy by hypothesizing different scenarios of reduction in red and processed meat consumption towards healthier consumption patterns more compliant with the recommendations of the Mediterranean food pyramid.MethodsWe used demographic and food consumption patterns from national surveys and risk relationships between meat intake and cardiovascular and colorectal cancer mortality from IARC and other meta-analyses.From the baseline data (year 2005–2006, average 406 gr/week beef and 245 gr/week processed meat), we considered hypothetical meat reduction scenarios according to international dietary guidelines such as the Mediterranean pyramid targets. For each geographical area (Northwest, Northeast, Centre, and South) and gender, we calculated the number of avoidable deaths from colorectal cancer, and cardiovascular disease among the adult population. Moreover, years of life gained by the adult population from 2012 to 2030 and changes in life expectancy of the 2012 birth cohort were quantified using gender-specific life tables.GHG emission reductions under Mediterranean scenario were estimated only for beef by applying the Global Warming Potential (GWP) coefficient to total consumption and to a low carbon food substitution in adult diet.ResultsThe deaths avoidable (as percentage change compared to baseline) according to the three reduction scenarios for beef consumption were between 2.3% and 4.5% for colorectal cancer, and between 2.1% and 4.0% for cardiovascular disease; higher benefits would be observed in Northwestern areas and among males. In parallel, 5% and 6.4% of colorectal cancer and CVD deaths would be avoided if the Italian population ate the advised quantity of processed meat. Life table analysis suggests that the scenario that is fully compliant with the Mediterranean diet model would save 5 million years of life lost prematurely among men and women over the next 18 years and would increase average life expectancy of future generations by over 7 months.Considering the environmental impact, emissions associated with the actual total intake of beef range from 12,900 to 21,800 Gg CO2 eq; emissions saved according to the Mediterranean scenario are in the range 8000–14000 Gg CO2 eq per year. The per capita reduction is 263 KgCO2eq/year/person with higher reductions in Northwestern and Central areas.ConclusionsIn Italy, scenarios for reducing beef consumption are consistent with significant health and environmental co-benefits on current and future generations. Results support introducing policies to promote healthier behavior towards red and processed meat in the adult population within an overall balanced and healthy dietary pattern. Interventions should address gender, vulnerable population groups, and geographical differences in order to be more effective.

Nearly three billion people in low- and middle-income countries (LMICs) rely on polluting fuels, resulting in millions of avoidable deaths annually. Polluting fuels also emit short-lived climate forcers and greenhouse gases (GHGs). Liquefied petroleum gas (LPG) and grid-based electricity are scalable alternatives to polluting fuels but have raised climate and health concerns. Here, we compare emissions and climate impacts of a business-as-usual household cooking fuel trajectory to four large-scale transitions to gas and/or grid electricity in 77 LMICs. We account for upstream and end-use emissions from gas and electric cooking, assuming electrical grids evolve according to the 2022 World Energy Outlook’s “Stated Policies” Scenario. We input the emissions into a reduced-complexity climate model to estimate radiative forcing and temperature changes associated with each scenario. We find full transitions to LPG and/or electricity decrease emissions from both well-mixed GHG and short-lived climate forcers, resulting in a roughly 5 millikelvin global temperature reduction by 2040. Transitions to LPG and/or electricity also reduce annual emissions of PM2.5 by over 6 Mt (99%) by 2040, which would substantially lower health risks from Household Air Pollution. Primary input data was collected from the following sources: Baseline household fuel choices - WHO household energy database (https://www.nature.com/articles/s41467-021-26036-x) End-use emissions - US EPA lifecycle assessment of household fuels (https://cfpub.epa.gov/si/si_public_record_report.cfm?dirEntryId=339679&Lab=NRMRL&simplesearch=0&showcriteria=2&sortby=pubDate&timstype=Published+Report&datebeginpublishedpresented) Upstream emissions - Argonne National Labs GREET Model (https://greet.es.anl.gov/index.php) Current and future population estimates - UNECA (http://data.un.org/Explorer.aspx?d=EDATA) Input data was processed by defining household fuel choice scenarios, estimating national household fuel consumption based on these scenarios, and applying fuel-specific emission factors to create country-specific emission pathways. These emission pathways were input into the FaIR model (https://zenodo.org/record/5513022#.Yt_jfHbMLb0) which generated additional data for each scenario including time series of pollution concentrations, radiative forcing, and temperature changes. All data is provided in CSV format. Nothing proprietary is required.

South Asian air is among the most polluted in the world, causing premature death of millions and asserting a strong perturbation of the regional climate. A central component is carbon monoxide (CO), which is a key modulator of the oxidizing capacity of the atmosphere and a potent indirect greenhouse gas. While CO concentrations are declining elsewhere, South Asia exhibits an increasing trend for unresolved reasons. In this paper, we use dual-isotope (δ13C and δ18O) fingerprinting of CO intercepted in the South Asian outflow to constrain the relative contributions from primary and secondary CO sources. Results show that combustion-derived primary sources dominate the wintertime continental CO fingerprint (fprimary ∼ 79 ± 4%), significantly higher than the global estimate (fprimary ∼ 55 ± 5%). Satellite-based inventory estimates match isotope-constrained fprimary-CO, suggesting observational convergence in source characterization and a prospect for model–observation reconciliation. This “ground-truthing” emphasizes the pressing need to mitigate incomplete combustion activities for climate/air quality benefits in South Asia.

Goal 13Take urgent action to combat climate change and its impactsTarget 13.1: Strengthen resilience and adaptive capacity to climate-related hazards and natural disasters in all countriesIndicator 13.1.1: Number of deaths, missing persons and directly affected persons attributed to disasters per 100,000 populationVC_DSR_MISS: Number of missing persons due to disaster (number)VC_DSR_AFFCT: Number of people affected by disaster (number)VC_DSR_MORT: Number of deaths due to disaster (number)VC_DSR_MTMP: Number of deaths and missing persons attributed to disasters per 100,000 population (number)VC_DSR_MMHN: Number of deaths and missing persons attributed to disasters (number)VC_DSR_DAFF: Number of directly affected persons attributed to disasters per 100,000 population (number)VC_DSR_IJILN: Number of injured or ill people attributed to disasters (number)VC_DSR_PDAN: Number of people whose damaged dwellings were attributed to disasters (number)VC_DSR_PDYN: Number of people whose destroyed dwellings were attributed to disasters (number)VC_DSR_PDLN: Number of people whose livelihoods were disrupted or destroyed, attributed to disasters (number)Indicator 13.1.2: Number of countries that adopt and implement national disaster risk reduction strategies in line with the Sendai Framework for Disaster Risk Reduction 2015–2030SG_DSR_LGRGSR: Score of adoption and implementation of national DRR strategies in line with the Sendai FrameworkSG_DSR_SFDRR: Number of countries that reported having a National DRR Strategy which is aligned to the Sendai FrameworkIndicator 13.1.3: Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategiesSG_DSR_SILS: Proportion of local governments that adopt and implement local disaster risk reduction strategies in line with national disaster risk reduction strategies (%)SG_DSR_SILN: Number of local governments that adopt and implement local DRR strategies in line with national strategies (number)SG_GOV_LOGV: Number of local governments (number)Target 13.2: Integrate climate change measures into national policies, strategies and planningIndicator 13.2.1: Number of countries with nationally determined contributions, long-term strategies, national adaptation plans, strategies as reported in adaptation communications and national communicationsEN_NACOM_NAIP: Number of countries with national communications, non-Annex I Parties (Number)EN_BIUREP_NAIP: Number of countries with biennial update reports, non-Annex I Parties (Number)EN_NACOM_AIP: Number of countries with national communications, Annex I Parties (Number)EN_BIUREP_AIP: Number of countries with biennial reports, Annex I Parties (Number)EN_ADAP_COM: Number of countries with adaptation communications (Number)EN_NAD_CONTR: Number of countries with nationally determined contributions (Number)EN_NAA_PLAN: Number of countries with national adaptation plans (Number)Indicator 13.2.2: Total greenhouse gas emissions per yearEN_ATM_GHGT_AIP: Total greenhouse gas emissions without LULUCF for Annex I Parties (Mt CO₂ equivalent)EN_ATM_GHGT_NAIP: Total greenhouse gas emissions without LULUCF for non-Annex I Parties (Mt CO₂ equivalent)Target 13.3: Improve education, awareness-raising and human and institutional capacity on climate change mitigation, adaptation, impact reduction and early warningIndicator 13.3.1: Extent to which (i) global citizenship education and (ii) education for sustainable development are mainstreamed in (a) national education policies; (b) curricula; (c) teacher education; and (d) student assessmentTarget 13.a: Implement the commitment undertaken by developed-country parties to the United Nations Framework Convention on Climate Change to a goal of mobilizing jointly $100 billion annually by 2020 from all sources to address the needs of developing countries in the context of meaningful mitigation actions and transparency on implementation and fully operationalize the Green Climate Fund through its capitalization as soon as possibleIndicator 13.a.1: Amounts provided and mobilized in United States dollars per year in relation to the continued existing collective mobilization goal of the $100 billion commitment through to 2025DC_FIN_CLIMB: Climate-specific financial support provided via bilateral, regional and other channels, by type of support (Billions of current United States dollars)DC_FIN_CLIMM: Climate-specific financial support provided via multilateral channels, by type of support (Billions of current United States dollars)DC_FIN_CLIMT: Total climate-specific financial support provided (Billions of current United States dollars)DC_FIN_GEN: Core/general contributions provided to multilateral institutions (Billions of current United States dollars)DC_FIN_TOT: Total financial support provided (Billions of current United States dollars)Target 13.b: Promote mechanisms for raising capacity for effective climate change-related planning and management in least developed countries and small island developing States, including focusing on women, youth and local and marginalized communitiesIndicator 13.b.1: Number of least developed countries and small island developing States with nationally determined contributions, long-term strategies, national adaptation plans, strategies as reported in adaptation communications and national communicationsEN_NACOM_NAIP: Number of countries with national communications, non-Annex I Parties (Number)EN_BIUREP_NAIP: Number of countries with biennial update reports, non-Annex I Parties (Number)EN_ADAP_COM: Number of countries with adaptation communications (Number)EN_NAD_CONTR: Number of countries with nationally determined contributions (Number)EN_NAA_PLAN: Number of countries with national adaptation plans (Number)

Namibia NA: Mortality Rate Attributed to Unintentional Poisoning: per 100,000 Population data was reported at 1.500 Ratio in 2016. This records a decrease from the previous number of 1.600 Ratio for 2015. Namibia NA: Mortality Rate Attributed to Unintentional Poisoning: per 100,000 Population data is updated yearly, averaging 2.000 Ratio from Dec 2000 (Median) to 2016, with 5 observations. The data reached an all-time high of 2.500 Ratio in 2000 and a record low of 1.500 Ratio in 2016. Namibia NA: Mortality Rate Attributed to Unintentional Poisoning: per 100,000 Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Namibia – Table NA.World Bank: Health Statistics. Mortality rate attributed to unintentional poisonings is the number of deaths from unintentional poisonings in a year per 100,000 population. Unintentional poisoning can be caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure.; ; World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).; Weighted average;