Carbon Data Analysis (work in progress!!)

library(dplyr)
library(tidyverse)
library(knitr)
library(plotly)
library(kableExtra)
library(leaflet)
library(RColorBrewer)

1 Introduction

Greenhouse gases (GHGs) emissions from human activities are considered as the most significant driver of observed climate change since the mid-20th century (IPCC, 2013). In order to better mitigate climate change impacts, policy makers need relevant and accurate data on GHG emissions at various scales.

Different protocols and methodologies for quantifying GHGs emissions have been developed aiming at allowing organizations (states, municipalities, companies, etc.) to assess their carbon footprins These standards help in identifying concerned gases, sectors’ categorization as well as methodologies to be used to measure the emissions associated with organizations’ activities. Self reported inventories are collected, compiled and sorted by multiple organisms of experts in climate change and carbon analytics. In order to esnsure transparency and public knowledge on emission levels, this data is published through various open data platforms. However, GHG emissions data are published in different formats, levels of transformations, aggregates, frequencies, scales, languages… This makes their use complex and requires mapping and comparison of different sources in order to build a complete corpus that meets users’ needs.

The objective of this report is to propose a compilation of data related to GHG emissions in order to harmonize them in a single accessible database.

In the next section we address main knowledge and concepts related to greenhouse gas emissions in order to better understand the content of emissions data.

In section 3 we present a catalog of the different data sources that we have been able to identify with a description of the collection and measurement methods used and data providers. We propose at the end of the section a target data model to which we try to map raw GHG emissions data.

In sections 4, 5 and 6, we present the results of exploration of raw data collected from previously identified sources at different geographical scales (world, countries, cities) respectively. GHG emission data is mapped with the proposed data model in section 3 in order to facilitate data sources comparison and assess the accuracy of our model. We will focus in this report on France use case for national and sub-national scale.

2 Domain knowledge

2.1 Grennhouse Gases (GHG)

2.1.1 Definition

Greenhouse gases constitute a group of gases that absorb infrared radiation from the Earth’s surface and trap heat in the atmosphere contributing hence to global warming and climate change. Without greenhouse gases, the average temperature of Earth’s surface would be about -18° rather than the present average of 15°. Atmospheric concentrations are determined by the balance between sources (emissions of the gas from human activities and natural systems) and sinks (the removal of the gas from the atmosphere by conversion to a different chemical compound or absorption by bodies of water).

The main gereenhouse gases are:

• Water vapour (H2O):

Water vapor is the largest contributor to the natural greenhouse effect. Water vapor is fundamentally different from other greenhouse gases in that it can condense and rain out when it reaches high concentrations, and the total amount of water vapor in the atmosphere is in part a function of the Earth’s temperature. While some human activities such as evaporation from irrigated crops or power plant cooling release water vapor into the air, these activities have been determined to have a negligible effect on global climate (IPCC 2013).

• Carbon dioxide (CO2):

The IPCC definitively states that “the increase of CO2 … is caused by anthropogenic emissions from the use of fossil fuel as a source of energy and from land use and land use changes, in particular agriculture” (IPCC, 2013). The predominant source of anthropogenic CO2 emissions is the combustion of fossil fuels. Forest clearing, other biomass burning, and some non-energy production processes (e.g., cement production) also emit considerable quantities of CO2.

• Methane (CH4):

Methane is emitted during the production and transport of coal, natural gas, and oil. Methane emissions also result from livestock and other agricultural practices. The IPCC has estimated that slightly more than half of the current CH4 flux to the atmosphere is anthropogenic, from human activities such as agriculture, fossil fuel production and use, and waste disposal (IPCC, 2007).

• Nitrous oxide (N2O):

Nitrous oxide is emitted during agricultural and industrial activities, combustion of fossil fuels and solid waste, as well as during treatment of wastewater.

• Ozone (O3):

Ozone is present in both the upper stratosphere, where it shields the Earth from harmful levels of ultraviolet radiation, and at lower concentrations in the troposphere, where it is the main component of anthropogenic photochemical “smog.”

• Fluorinated gases:

Halocarbons, Sulfur Hexafluoride, and Nitrogen Trifluoride are synthetic, powerful greenhouse gases that are emitted from a variety of industrial processes. These gases are typically emitted in small quantities, but because they are potent greenhouse gases, they are sometimes referred to as high Global Warming Potential gase.

2.1.2 Sources

Anthropogenic GHG refer to emissions caused by human activity. Water vapour (H2O) and ozone (O3) are not included in the scope of GHG accounting since their emission is not directly influenced by human activity. The vast majority of anthropogenic GHG emission come from combustion of fossil fuels, principally coal, oil and natural gas, with additional contributions coming from deforestation and other changes in land use. Carbon dioxide (CO2) is considered as the anthropogenic GHG with strongest impact on the climate.

2.1.3 Global Warming Potential

GHGs warm the earth by absorbing energy and slowing the rate at which the energy escapes to space. Different GHGs can have different effects on the earth’s warming depending on their ability to absorb energy (radiative efficiency) and how long they stay in the atmosphere (lifetime).

The Global Warming Potential (GWP) was developed to allow comparisons of the global warming impacts of different gases. It is a measure of how much energy the emission of 1 Ton of a gas will absorb over a given period of time, relative to the emissions of 1 Ton of carbon dioxide. The larger the GWP, the more that given gas warms the earth compared to CO2 over that time period (usually 100 years). GWP provides a common unit of measure which allows analysts to add up emissions estimates of different gases (e.g., to compile a national GHG inventory), and allows policymakers to compare emissions reduction opportunities across sectors.

• CO2, by definition, has a GWP of 1 regardless of the time period used since it is the gas used as the reference. CO2 emissions cause an increase in the atmospheric concentrations that will least thousands of years.

• Methane (CH4) is estimated to have a GWP of 28-36 over 100 years. CH4 emitted today lasts about a decade on average, which is much less time than CO2. But CH4 absorbs much more energy than CO2.

• Nitrous Oxide (N2O) has a GWP 265-298 times that of CO2 for a 100-year timescale. N2O emitted today remains in the atmosphere for more than 100 years, on average.

• Fluorinated gases are high-GWP gases because, for a given amount of mass, they trap substantially more than CO2. The GWPs for these gases can be in the thousands or tens of thousands.

2.1.4 Carbon cycle

The carbon cycle describes the movement of carbon as is recycled and reused throught the biosphere, as well as long-term processes of carbon sequestration and release from carbon sinks. The carbon cycle is usually divided into the five main interconnected reservoirs of carbon: the atmosphere, the terrestrial biosphere, the ocean, sediments (including fossil fuels, freshwater systems and non-living organic material) and the earth’s interior.

2.2 Methods for estimating GHG emissions

Diffrent approaches exist for estimating GHGs emissions across a city or region, that we can classify into top-down and bottom-up methods:

• Top-Down methods estimate emissions based on observations and often incorporate atmospheric transport and dispersion modeling with inverse models to estimate emissions from observed concentrations.
• Bottom-Up methods often use reported emissions, process-based models, and activity data to estimate fluxes either from anthropogenic activities or the biosphere.

Various studies have indicated important gaps between bottom-up and top-down GHGs emissions estimations. Bergamaschi et al.) identified significant differences between Euorpean CH4 and N2O emissions as reported to the UNFCCC compared to three inverse models based on observations from 10 European stations. Thompsont et al. discovered overestimation of CH5 emission in EDGAR bottom-up data comapred to their estimation based on atmospheric Bayesian inversion technique. Cheewaphongphan et al. explored gap between bottom-up and top-down emission estimates based on uncertainties in CH4 emission inventories in China.

2.3 Top-Down: GHG emission observation

Top-Down GHG emission methods are based on atmospheric measurements and atmospheric models estimations. It consists of linking emissions with atmospheric concentrations using atmospheric transport (and chemistry) models, often referred as inverse modeling. A large number of scientific studies demonstrate that inverse modeling can be used to check the consistency between bottom-up emission inventories and GHG concentrations measured in the atmosphere.

However, the accuracy of emissions’ estimations derived from inverse modeling, and the spatial scales at which the emissions can be estimated, depend on the quality and density of measurements and the quality of the atmospheric models. Furthermore, inverse modeling provides estimates of total emissions, including both anthropogenic and natural sources. The World Meteorological organization(WMO) has initiated the Integrated Global Greenhouse gas Information System (IG3IS) with aim of promoting top-down methods.

CO2 monitoring from space can provide important additional information and identify CO2 hotpots. For instance, Nassar et al., used Orbiting Carbon Observatory 2 (OCO-2) satellite retrievals to quantify, CO2 emissions from large point sources in close agreement with reported daily emission values. This study suggests that future CO2 imaging satellites, optimised for point sources, could monitor emissions from individual power plants, which will be important for areas that lack detailed emission information. Recent improvements in satellite retrievals are encouraging and various studies investigate the potential to use satellite data to quantify CO2 emissions from large cities and point sources.

In addition to satellite data, ground based carbon monitoring remains indispensable and will require a significant expansion of surface monitoring stations, such as the “Integrated carbon Observation System” (ICOS) network over Europe, and national and international networks, including “Total Carbon Column Observing Network” (TCCON) for validating satellite data. Furthermore, measurement programs closer to emission sources, which can quantify emissions at facility scale, should be further expanded. Such facility scale measurements can provide more representative emission factors and allow to directly improve emission inventories.

• How we measure CO2 concentrations in the atmosphere? The measured quantity of CO2 by measurement stations is described by the chemical term “mole fraction”, defined as the number of carbon dioxide molecules in a given number of molecules of aire, after removal of water vapor. For example, 413 parts per million of CO2 (abbrieved as ppm) means that in every million molecules of (dry) air there are on average 413 CO2 molecules. More details of CO2 measurement methods are available in this document provided by Global Monitoring Laboratory.

• Orbiting Carbon Observatory 2 (OCO-2): OCO-2 is a CO2 observing satellite used to study carbon dioxide concentrations and distributions in the atmosphere. The OCO-2 project objectives are to collect the space-based measurements needed to quantify variations in the column averaged atmospheric dioxide (CO2) dry air mole fraction, with the precision, resolution, and coverage needed toi improve our understanding of surface CO2 sources and sinks on regional scales (> 1000km). The entire OCO-2 data records can be obtained from the Nasa earth data portal.

• The Total Carbon Column Observing Network (TCCON): TCCON is a network of ground-based Fourier Transform Spectrometers recording direct solar spectra in the near-infrared spectral region. From these spectra, accurate and precise column-averaged abundance of CO2, CH4, N2O, HF, CO, H2O, and HDO are retrieved.

• The Global Greenhouse Gas Reference Network: The Global Greenhouse Gas Reference Network measures the atmospheric distribution and trends of the three main long-term drivers of climate change, carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O), as well as carbon monoxide (CO) which is an important indicator of air pollution. The Reference Network is a part of NOAA’s Global Monitoring Laboratory in Boulder, Colorado. The carefully calibrated and documented measurements data are provided freely through the Global Monitoring Laboratory data portal.

• Copernicus CO2 Monitoring Task Force

• Integrated carbon Observation System

Generalised schematic illustrating the combination of top-down information from atmospheric concentration measurements (including atmospherci monitoring stations and satellite) and bottom-up minformation on amissions which are used as first estimate (source)

• Greenhouse gas monitoring

Greenhouse gas monitoring is the direct measurement of greenhouse gas emissions and levels. GHGs are measured from space such as by Orbiting Carbon Observatory and by the mean of networks of ground stations such as the Integreated Carbon Observation System. GHGs monitoring refers to tracking how much GHGs is produced by particular activity at a particular point in time.

NASA CArbon Monitoring System (CMS) is a climate research program that provides grants for climate research that measure carbon dioxide and methane emissions. Using instruments in satellites and ariplanes CMS funded research projects provide data to the United States and other countries that help track progress of individual nations regarding their emissions.

• Space-based measurements of GHG emissions

Space-based measurements of carbon dioxide (CO2) are used to help understanding Earht’s carbon cycle. There are two high-precision CO2 observing satellites: GOSAT and OCO-2.

2.4 Bottom-Up: GHG inventory and assessment

A GHG assessment is an evaluation of the quantity of emitted (or captured and stored) GHGs in the atmosphere over one year resulting from the activities of an organization or a territory. This assessment is based on methodologies that quantify the flows of greenhouse gases generated by an entity, and characterize their impact by means of an indicator, usually the global warming potential coefficient (GWP). Bottom-up emission inventories, however, can have significant uncertainties, especially for non CO2 GHGs due to large uncertainties in the emissions factors for many source sectors, as well as biased due to unaccounted sources. Furthermore, statistical activity data can have considerable uncertainties (and might be incomplete), in particular for countries with less developed statistical infrastructure, which will also have to submit regular reports under the Paris Agreement

2.4.1 Methodology

The methodological principles vary considerably depending on the type of assessment that is carried out.

• GHG organizational reporting (companies, collectivities, public establishments)
• GHG reporting for territories

The Chapter 8 of the IPCC report provides framework and guidance for reporting complete, consistent and transparent national greeenhouse gas inventories, regardless the method used to produce the data.

• Link to the IPCC reporting guideline
• 2006 IPCC guidelines for National Greenhouse Gas INventories

2.4.2 Geographical Coverage

• The 2006 Guidelines are designed to estimate and report on national inventories of anthropogenic greenhouse gas emissions and removels. Anthropogenic emissions and removeals means that greenhouse gas emissions and removals in national inventories are a result of human activities.

• National inventories should include ghg emissions and removals taking place withing national territory and offshore areas over whoch the country has jurisdication.

2.4.3 gases included

The 2006 Guidelines can be applied for two groups of greenhouse gases:

• GHG covered by the Montreal Protocol: carbon dioxide, methane, nitrous oxide, hydroflurocarbons, perfluorocarbons…
• Other halogenated greenhouse gases not covered by the Montreal Protocol

2.4.4 Sectors and categories

The 2006 Guidelines group emissions and removals categories into five main sectors:

• Energy
• Industrial Processes and Product Use (IPPU)
• Agriculture, Forestry and Other Land Use (AFOLU)
• Waste
• Other

These sectors are divided into several sub-categories with specific codes that can be explored in [the chapter 8.5 of the guideline](https://www.ipcc-nggip.iges.or.jp/public/2006gl/pdf/1_Volume1/V1_8_Ch8_Reporting_Guidance.pdf].

2.4.5 Scopes

According to GHG emissions protocols and standards (e.g., GHG protocol, Bilan Carbone… ), emissions might be divided into 3 scopes:

• Scope 1: Direct GHG emissions

Scope 1 covers all direct GHG emissions physically produced by an activity. It includes fuel combustion, process emissions and fugitive emissions. These direct emissions are used to compute national inventories like those defined under the United Nations Framework Convention on Climate Change (UNFCCC).

• Scope 2: Indirect GHG emissions

Scope 2 covers indirect GHG emissions from consumption of purchased electricity, heat or steam. It refers to consumption of a final energy for which emissions occur at the energy production site and not at the place of consumption.

• Scope 3: Other indirect GHG emissions

Scope 3 covers other indirect emissions produced from sources outside of the organizational boundaries of the reporting entity but are necessary for its activity. For example: extraction and production of purchased materials and fuels, transport-related activities in vehicles not owned or controlled by the reporting entity, electricity-related activities (e.g. transmission and distribution (T&D) losses) not covered in Scope 2, outsourced activities, waste disposal, etc. Scope 3 emissions (also known as value chain emissions) often represent the largest source of greenhouse gas emissions and in some cases can account for up to 90% of the total carbon impact.

Bilan Carbone: emissions’ categories (source: GHG Protocol)

2.4.6 Frameworks

2.4.6.1 Bilan carbone

In France, ADEME published in 2004 a methodology for quantifying greenhouse gas emissions for organizations, called the Bilan Carbone®. This method is today coordinated and dissiminated by the Association Bilan carbone.

The Bilan Carbone® method takes account of all greenhouse gases defined by the IPCC for all physical flows without which the organization could not function. This method therefore allows companies and territorial collectivities to carry out a global assessment of GHG emissions, whether direct or indirect. A method has been specifically developed on the territorial scale.

2.4.6.2 GHG Protocol

In 1998, the World Business Council for Sustainable Development (WBCSD) and the World Resources Institutes (WRI) developed, in partnership with companies, NGOs and State representatives, the GHG Protocol: “A Corporate Accounting and Reporting Standard”.

This protocol, extensively disseminated internationally, served as the basis for drafting ISO 14064-1:2006.

In October 2011, the GHG Protocol was supplemented by the “Corporate Value Chain (Scope 3) Accounting and Reporting Standard”, which details in particular the potential indirect GHG emission categories for an organization.

Since July 2014, a method dedicated to Territories has been made available: the Global Protocol for Community-scale GHG emissions.

2.5 Downscaling GHGs emissions

2.6 Glossary

• Kyoto protocol Kyoto protocol:
  • https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Glossary:Kyoto_Protocol
• Paris Agreement

The Paris Agreement, adopted in the 21 Conference of the Parties of the United Nations Framework Convention on Climate Change (UNFCCC) in Paris on 12 December 2015, brought, for the first time, all nations into a common cause to undertake ambitious efforts to combat climate change. The central aim of the Paris Agreement is to hold “the increase in the global average temperature to well below 2°C above pre-industrial levels and pursuing efforts to limit the temperature increase to 1.5° above pre-industrial level”.

• Carbon Pricing

• Carbon footprint

• Carbon Budget: A carbon budget is an upper limit of total CO2 emissions associated with remaining below a specific global average - temperature. Global emissions budgets are calculated according to historical cumulative emissions from fossil, industrial processes, and land use change, but vary according to the global temperature target that is chosen, the probability of staying below that target, and the emissions of other non CO2 greenhouse gases. Emissions budgets are relevant to climate change mitigation because they indicate a finite amount of carbon dioxide that can be emitted over time, before resulting in dangerous levels of global warming.

2.7 References

• High-resolution spatial distribution and associated uncertainties of greenhouse gas emissions from the agricultural sector
• Quantifying greenhouse-gas emissions from atmospheric measurements: a critical reality check for climate legislation
• Satellite observations to support monitoring of greenhouse gas emissions

3 Data sources cartography

3.1 Data sources

Several organizations provide and share data related to GHG emissions on various scales and based on different frameworks. This section aims at describing main GHG data providers in international and European scale. Concerning the national and sub-national scale, we will focus essentiallt on French GHG emissions data.

3.1.1 International scale

Data provider	Description	Geoscale	Data sources	Access
The World Bank	The World Bank Group publishes various indicators on world development through its open data platform. Provided aggregated GHG emissions data is based on the Emission Database for Global Atmospheric Research (EDGAR)	World scale Country scale	EDGAR	Get data
The World Resources Institute (WRI)	The World Resources Institute compiles various sources of GHG emissions and provide access to this data through a specific tool: CLIMAT WATCH. Provided GHG emissions data is based on various data sources: CAIT database, UNFCCC, PIK.	World scale Country scale	CAIT UNFCCC PIK	Get data
The United Nations Framework Convention on Climate Change (UNFCCC)	The UNFCCC compiles and shares national annual greenhouse gases inventories submitted in accordance with with the reporting requirements adopted under the Climate Change Convention	World scale Country scale	UNFCCC	Get data
Potsdam Institute for Climate Impact Research (PIK)	The Postdam Institute provides the PRIMAP-hist dataset, which combines several published datasets to create a comprehensive set of greenhouse gas emission pathways for every country and Kyoto gas covering the years 1850 to 2017, and all UNFCCC (United Nations Framework Convention on Climate Change) member states, as well as most non-UNFCCC territories.	World scale Country scale	UNFCCC EDGAR	Get data
The Joint Research Center of Euorpean Commission	The Joint Research center produces the Emission Database for Global Atmospheric Research (EDGAR). EDGAR provides independent estimates of the global anthropogenic emissions and emission trends, based on publicly available statistics, for the atmospheric modeling community as well as for policy makers. This scientific independent emission inventory is characterized by a coherent world historical trend from 1970 to year x-3, including emissions of all greenhouse gases, air pollutants and aerosols. Data are presented for all countries, with emissions provided per main source category, and spatially allocated on a 0.1x0.1 grid over the globe	World scale Country scale Grid scale	EDGAR	Get data
The Organization for Economic Co-Operation and Development (OECD)	The OECD publishes datasets presenting trends in man-made emissions of major greenhouse gases and emissions by gas.	OECD scale Country scale	UNFCCC	Get data
European Environmental Agency (EEA)	The EEA compiles and provides data on greenhouse gas emissions and removals, sent by countries to UNFCCC and the EU Greenhouse Gas Monitoring Mechanism (EU Member States).	Europe scale Country scale	UNFCCC	Get data
Eurostat	Eurostat (European Statistical Office) is a Directorate-General of the European Commission. It provides statistical information to the institutions of the European Union (EU) such as a comprehensive set of climate change-related data including GHG emissions statistics. Eurostat maintains a data portal for exploring emissions data.	Europe scale Country scale	EEA UNFCCC	Get data
Our World In Data	Our WOrld In Data compile, maintain and shares CO2 and GHG emissions data. It is updated regularly and includes data on CO2 emissions (annual, per capita, cumulative and consumption-based), other greenhouse gases, energy mix, and other relevant metrics.	World scale Country scale	GCP CAIT	Get data
Global Carbon Project (GCP)	GCP is a global research project that seeks to quantify global greenhouse gas emissions and their causes. It provides data on carbon fluxes resulting from human activities and natural processes and a platform to explore and visualize the most up-to-date data (Gloabl carbon Atlas)	World scale Country scale City scale	GCP	Get data
Carbon Disclosure Project (CDP)	The CDP is an international non-profit organisation that helps companies and cities disclose their environmental impact and GHG emissions. CDP provides an open data protal for exploring companies and city-wide collected data.	City scale	CDP	Get data

3.1.2 French scale

Data provider	Description	Geoscale	Data sources	Access
Agence De l’Environnement Et de la Maitrise de l’Energie (ADEME)	In France, ADEME provides GHG accounting framework, called Bilan Carbone, for collecting and compiling emissions inventories from organizations and territories.	City scale	ADEME	Get data
Technical Reference for Air Pollution and Climate Change (CITEPA)	Citepa officially estimates greenhouse gas and air polluant emissions each year on behalf of the French Ministry of the Environment.	Country scale City scale	Citepa	Get data
Atmo France: Fédération des Associations Agréées de Surveillance de la Qualité de l’Air (AASQA)	Atmo France is a federation of regional approved supervisory assoiciations of air quality in France. It provides an Air QUality Open Data Portal including: air polluants stations measurements, emissions, air quality indicators… Data is provided in multiple temporal scales, ranging from hourly to annual data and with historical depth of 5 years.	Region scale City scale	AASQA	Get data

3.2 Data providers

3.2.1 The World Resources Institute

The World Resources Institute (WRI) is a global research non-profit organization promoting environmental sustainability, economic opportunity, and human health and well-being. WRI’s activities are focused on seven areas: food, forests, water, energy, cities, climate and ocean. It participates to the definition of Greenhouse Gas Protocol by providing standards, guidance, tools, and trainings for business and government to quantify and manage GHG emissions.

WRI provides also access to historic GHG data allowing for easy access, analysis and visualization of the latest available international greenhouse gas emissions data. It includes information for 191 countries and the European Union, 50 U.S. states, 6 gases, multiple economic sectors, and 160 years - carbon dioxide emissions for 1850-2014 and multi-sector greenhouse gas emission for 1990-2014.

More description is available here

3.2.2 The United Nations Framework Convention on Climate Change (UNFCCC)

• History:

The UNFCCC is an international environmental treaty addressing climate change, negotiated by 154 states at the United Nations Conference on Environment and Development, informally known as the Earth Summit, held in Rio de Janiero in 1992. Adopted in 1992 at the Rio Earth Summit, most members of the Organization for Economic Cooperation and Development (OECD) plus the states of Central and Eastern Europe - known collectively as Annex I countries - are committed to adopting policies and measures aimed at returning their greenhouse-gas emissions to 1990 levels by the year 2000. All Parties develop and submit “national communications” containing inventories of greenhouse gas emissions by source and greenhouse gas removals by sinks.

• Objectives:

The UNFCCC seeks for the stabilization of greenhouse gas concentrations in the atmosphere at a level that would prevent dangerous anthropogenic human-induced interferences with the earth’s climate system. It participates in the definition of guidelines for states to submit annual national greenhouse gas emissions inventories and displays collected data through an open data portal.

• Parties:

The Convention divides countries into three main groups according to differing commitments. Here is the description of these categories (source).

-Annex I Parties include the Industrialized  countries that were members of the OECD (Organization for Economic Co-operation and Development) in 1992, plus countries with economies in transition (the EIT parties), including the Russian Federation, the Baltic States, and several Central and Eastern European States.

-Annex II Parties consist of the OECD members of Annex I, but not the EIT Parties. They are required to provide financial resources to enable developing countries to undertake emissions reduction activities under the Convention and to help them adapt to adverse effects of climate change.

- Non-Annex I Parties are mostly developing countries. Certain groups of developing countries are recognized by the Convention as being especially vulnerable to the adverse impacts of climate change, including countries with low-lying coastal areas and those prone to desertification and drought. Others (such as countries that rely heavily on income from fossil fuel production and commerce) feel more vulnerable to the potential economic impacts of climate change response measures.

3.2.3 European Environmental Agency

The European Environment Agency (EEA) is an agency of the European Union, whose task is to provide sound, independent information on the environment. The EEA aims to support sustainable development by helping to achieve significant and measurable improvement in Europe’s environment, through the provision of timely, targeted, relevant and reliable information to policymaking agents and the public.

The European environment information and observation network (Eionet) is a partnership network of the EEA and its member and cooperating countries. Through Eionet, the EEA brings together environmental information from individual countries concentrating on the delivery of timely, nationally validated, high-quality data.

3.2.4 ADEME

ADEME is a public Environment and Energy Management Agency in France. ADEME is active in the implementation of public policy in the areas of the environment, energy and sustainable development. ADEME makes its expertise and consultancy capacities available to companies, local authorities, public authorities and the general public and helps finance projects in five areas (waste management, soil conservation, efficiency energy and renewable energies, air quality and noise reduction) and to progress in their sustainable development initiatives.

To meet regulatory obligations and support the energy and ecological transition, ADEME has decided to make its data available to businesses, local authorities, public authorities and the general public in order to enable them to progress in their environmental approach by the mean of an open data portal.

• Bilan Carbone

Bilan Carbone® refers to the methods developed by ADEME and Association Bilan Carbone (ABC) to enable organizations and local government authorities (respectively Bilan Carbone® and Bilan Carbone® Territoire) to address, measure and reduce their greenhouse gas emissions. These inventories are published on the ADEME GES platform. ADEME publishes the GHG reports entered by organizations via a search engine on its site, but does not publish the consolidated underlying database of all the reports. It is therefore possible to view each report one by one, but not to perform automated processing on this data.

Bilan Carbone: emissions’ categories (source: https://www.bilans-ges.ademe.fr/fr/accueil/contenu/index/page/categorie/siGras/0)

• Base Carbone

The Base Carbone® is a public database of emission factors, necessary for carrying out a greenhouse gas (GHG) emissions assessment and more generally any carbon accounting exercise. A complete documentation of the Base Carbone is provided here. The data in the Base Carbone ® can be viewed free of charge by everyone and can be downloaded through this page.

3.2.5 Citepa

Created in 1961, CITEPA (Technical Reference for Air Pollution and Climate Change) quantifies, identifies, expertises and reports atmospheric emissions data, explanatory variables and efficiency indicators, as well as methods for monitoring, quantifying, projecting and modeling emissions, policies and measures of mitigation and adaptation.

As a non-profit organisation and State operator for the French Environment Ministry, the Citepa meets reporting requirements for air pollutants and greenhouse gas emissions from France in different inventory formats, such as UNFCCC, EMEP, and Kyoto Protocol.

National inventory data

Citepa officially estimates greenhouse gas and air polluant emissions each year on behalf of the Ministry of the Environment. his inventory is carried out as part of France’s international commitments, mainly under the United Nations Framework Convention on Climate Change (as well as the Kyoto Protocol and the Paris Agreement) for greenhouse gases, and the United Nations Economic Commission for Europe for pollutants (LRTAP Convention).

All produced data and official reports can be download from this page.

IGT (Inventaire GES territorialisé)

The Ministry in charge of the Environment has entrusted the Interprofessional Technical Center for Atmospheric Pollution Studies (CITEPA) with a mission of “territorialization” or a “spatialization” of the national GHG inventory. The spatial resolution is the municipality level, and then aggeregated by EPCI. It is established from botha breakdown of national GHG emissions at municipal level and already spatialized information and can be downloaded through:

• ADEME Open Data Portal
• Datagouv Open Data Portal

3.3 Data modeling

We propose a data model for mapping the various data source identified

Column name	Type	Description	Examples
data.provider	string	Short name of the entity delivering access to GHG emissions data which could come from multiple data sources. Data provider may not be the data producer. For example, the World Resources Institute provides different GHG emissions data obtained from multiple sources (UNFCC, PIK…)	WRI OWID EEA WB GCP…
data.source	string	Short name of data source referring to the entity producing raw GHG emissions data obtained from organizations’ inventories or aggregated data generated from a compilation of different sources.	OWID PIK UNFCC GCP ADEME CITEPA
accounting.framework	string	Name of inventory framework and methodology used for GHG emissions assessment.	UNFCC ADEME
geo.scale	string	Category of geo-spatial scale and geographical boundary considered in emissions data. As seen previously, various GHG emissions data are aggregated at a group of countries levels such as OECD states, European states, Annex I parties…	country scale city scale Group of countries scale
geo.code.iso2c	string	Countries ISO 3166-1 alpha-2 codes	List of alpha-2 ISO codes
geo.code.iso3c	string	ISO 3166-1 alpha-3 codes	List of alpha-3 ISO codes
geo.name	string	ISO 3166-1 english country name	List of english ISO country names
year	date	Year of the corresponding GHG emission
sector	string	Activity sectors: “Total excluding LUCF”, “Total including LUCF”, “Energy”, “Industrial Processes”, “Agriculture”, “Waste”, “Land-Use Change and Forestry”, “Bunker Fuels”
scope	string	Scope number vorresponding to the emission data	Scope 1 Scope 2 Scope 3
gas	string	Name of quantified gas emissions	All GHG CO22 CH4 N2O F-Gas
value	numeric	QUantity of considered gas emission by sector and scope.
unit	string	Unit used for quantifying GHG emissions

{
    "data_provider": {
        "name": "EEA"
    },
    "data_source": {
        "name": "EEA",
    },
    "geo": {
        "name": "France",
        "scale": "Country",,
        "code_iso_2": "FR",
        "code_iso_3": "FRA"
    },
    "time": {
        "scale": "Year",
        "value": 2018
    },
    "emission": {
        "gas": ["CO2", "CH4", "NO2", "F-gas"],
        "value": 540,
        "unit": "MtCO2",
        "accounting_framework": "UNFCCC",
        "sector_name": "All",
        "Scope_name" : "Scope 1",
        "scopes":

    }
}

3.4 Data harmonization projects

• Country scale:
  • https://www.theshiftdataportal.org/
  • https://www.climatewatchdata.org/
  • https://essd.copernicus.org/articles/12/3269/2020/
• City scale:
  • https://www.nature.com/articles/sdata2018280

4 World scale data exploration

4.1 World Bank

4.1.1 Data description

The World Bank provides access to several indicators related to GHG emissions. This data is provided through API or by download.

In the example below, we will use the R package WDI, which provides easy access to World Bank indicators as shwon in the table below.

library(WDI)
 
# get datasets on emissions

datasets_emissions = as.data.frame(WDIsearch("emissions"))

# Show datasets on emissions
datasets_emissions  %>% 
  kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = F, font_size = 13) %>% 
  scroll_box(width = "100%", height = "400px")

indicator	name
EE.BOD.CGLS.ZS	Water pollution, clay and glass industry (% of total BOD emissions)
EE.BOD.CHEM.ZS	Water pollution, chemical industry (% of total BOD emissions)
EE.BOD.FOOD.ZS	Water pollution, food industry (% of total BOD emissions)
EE.BOD.MTAL.ZS	Water pollution, metal industry (% of total BOD emissions)
EE.BOD.OTHR.ZS	Water pollution, other industry (% of total BOD emissions)
EE.BOD.PAPR.ZS	Water pollution, paper and pulp industry (% of total BOD emissions)
EE.BOD.TOTL.KG	Organic water pollutant (BOD) emissions (kg per day)
EE.BOD.TXTL.ZS	Water pollution, textile industry (% of total BOD emissions)
EE.BOD.WOOD.ZS	Water pollution, wood industry (% of total BOD emissions)
EE.BOD.WRKR.KG	Organic water pollutant (BOD) emissions (kg per day per worker)
EN.ATM.CO2E.CP.KT	CO2 emissions from cement production (thousand metric tons)
EN.ATM.CO2E.FF.KT	CO2 emissions from fossil-fuels, total (thousand metric tons)
EN.ATM.CO2E.FF.ZS	CO2 emissions from fossil-fuels (% of total)
EN.ATM.CO2E.GDP	CO2 emissions, industrial (kg per 1987 US$ of GDP)
EN.ATM.CO2E.GF.KT	CO2 emissions from gaseous fuel consumption (kt)
EN.ATM.CO2E.GF.ZS	CO2 emissions from gaseous fuel consumption (% of total)
EN.ATM.CO2E.GL.KT	CO2 emissions from gas flaring (thousand metric tons)
EN.ATM.CO2E.KD.87.GD	CO2 emissions, industrial (kg per 1987 US$ of GDP)
EN.ATM.CO2E.KD.GD	CO2 emissions (kg per 2010 US$ of GDP)
EN.ATM.CO2E.KT	CO2 emissions (kt)
EN.ATM.CO2E.LF.KT	CO2 emissions from liquid fuel consumption (kt)
EN.ATM.CO2E.LF.ZS	CO2 emissions from liquid fuel consumption (% of total)
EN.ATM.CO2E.PC	CO2 emissions (metric tons per capita)
EN.ATM.CO2E.PP.GD	CO2 emissions (kg per PPP $ of GDP)
EN.ATM.CO2E.PP.GD.KD	CO2 emissions (kg per 2017 PPP $ of GDP)
EN.ATM.CO2E.SF.KT	CO2 emissions from solid fuel consumption (kt)
EN.ATM.CO2E.SF.ZS	CO2 emissions from solid fuel consumption (% of total)
EN.ATM.GHGO.KT.CE	Other greenhouse gas emissions, HFC, PFC and SF6 (thousand metric tons of CO2 equivalent)
EN.ATM.GHGO.ZG	Other greenhouse gas emissions (% change from 1990)
EN.ATM.GHGT.KT.CE	Total greenhouse gas emissions (kt of CO2 equivalent)
EN.ATM.GHGT.ZG	Total greenhouse gas emissions (% change from 1990)
EN.ATM.HFCG.KT.CE	HFC gas emissions (thousand metric tons of CO2 equivalent)
EN.ATM.METH.AG.KT.CE	Agricultural methane emissions (thousand metric tons of CO2 equivalent)
EN.ATM.METH.AG.ZS	Agricultural methane emissions (% of total)
EN.ATM.METH.EG.KT.CE	Methane emissions in energy sector (thousand metric tons of CO2 equivalent)
EN.ATM.METH.EG.ZS	Energy related methane emissions (% of total)
EN.ATM.METH.IN.ZS	Energy related methane emissions (% of total)
EN.ATM.METH.KT.CE	Methane emissions (kt of CO2 equivalent)
EN.ATM.METH.PC	Methane emissions (kt of CO2 equivalent per capita)
EN.ATM.METH.ZG	Methane emissions (% change from 1990)
EN.ATM.NOXE.AG.KT.CE	Agricultural nitrous oxide emissions (thousand metric tons of CO2 equivalent)
EN.ATM.NOXE.AG.ZS	Agricultural nitrous oxide emissions (% of total)
EN.ATM.NOXE.EG.KT.CE	Nitrous oxide emissions in energy sector (thousand metric tons of CO2 equivalent)
EN.ATM.NOXE.EG.ZS	Nitrous oxide emissions in energy sector (% of total)
EN.ATM.NOXE.EI.ZS	Nitrous oxide emissions in industrial and energy processes (% of total nitrous oxide emissions)
EN.ATM.NOXE.IN.KT.CE	Industrial nitrous oxide emissions (thousand metric tons of CO2 equivalent)
EN.ATM.NOXE.IN.ZS	Nitrous oxide emissions in industrial and energy processes (% of total nitrous oxide emissions)
EN.ATM.NOXE.KT.CE	Nitrous oxide emissions (thousand metric tons of CO2 equivalent)
EN.ATM.NOXE.MT.CE	Nitrous oxide emissions (metric tons of CO2 equivalent)
EN.ATM.NOXE.PC	Nitrous oxide emissions (metric tons of CO2 equivalent per capita)
EN.ATM.NOXE.ZG	Nitrous oxide emissions (% change from 1990)
EN.ATM.PFCG.KT.CE	PFC gas emissions (thousand metric tons of CO2 equivalent)
EN.ATM.SF6G.KT.CE	SF6 gas emissions (thousand metric tons of CO2 equivalent)
EN.CLC.GHGR.MT.CE	GHG net emissions/removals by LUCF (Mt of CO2 equivalent)
EN.CO2.BLDG.MT	CO2 emissions from residential buildings and commercial and public services (million metric tons)
EN.CO2.BLDG.ZS	CO2 emissions from residential buildings and commercial and public services (% of total fuel combustion)
EN.CO2.ETOT.MT	CO2 emissions from electricity and heat production, total (million metric tons)
EN.CO2.ETOT.ZS	CO2 emissions from electricity and heat production, total (% of total fuel combustion)
EN.CO2.MANF.MT	CO2 emissions from manufacturing industries and construction (million metric tons)
EN.CO2.MANF.ZS	CO2 emissions from manufacturing industries and construction (% of total fuel combustion)
EN.CO2.OTHX.MT	CO2 emissions from other sectors, excluding residential buildings and commercial and public services (million metric tons)
EN.CO2.OTHX.ZS	CO2 emissions from other sectors, excluding residential buildings and commercial and public services (% of total fuel combustion)
EN.CO2.TRAN.MT	CO2 emissions from transport (million metric tons)
EN.CO2.TRAN.ZS	CO2 emissions from transport (% of total fuel combustion)

We will select for this article the indicator EN.ATM.GHGT.KT.CE, which corresponds to the total greenhouse gas emissions (measured in kt of CO2 equivalent).

# get Total greenhouse gas emissions (kt of CO2 equivalent)
ghg_emissions_wb = WDI(indicator='EN.ATM.GHGT.KT.CE')

# Show data sample 
ghg_emissions_wb %>% 
  filter(country == "World") %>% 
  kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = F, font_size = 13) %>% 
  scroll_box(width = "100%", height = "400px")

iso2c	country	EN.ATM.GHGT.KT.CE	year
1W	World	NA	2020
1W	World	NA	2019
1W	World	NA	2018
1W	World	NA	2017
1W	World	NA	2016
1W	World	NA	2015
1W	World	NA	2014
1W	World	NA	2013
1W	World	53526303	2012
1W	World	52790527	2011
1W	World	50911114	2010
1W	World	48150621	2009
1W	World	48664441	2008
1W	World	49977387	2007
1W	World	48639988	2006
1W	World	47216059	2005
1W	World	45658898	2004
1W	World	44422305	2003
1W	World	43070575	2002
1W	World	40365673	2001
1W	World	40563437	2000
1W	World	40891983	1999
1W	World	44048271	1998
1W	World	43375208	1997
1W	World	39258545	1996
1W	World	39040328	1995
1W	World	38810307	1994
1W	World	38081370	1993
1W	World	39824355	1992
1W	World	38587504	1991
1W	World	38232170	1990
1W	World	36108992	1989
1W	World	35478326	1988
1W	World	35963102	1987
1W	World	34049392	1986
1W	World	33080641	1985
1W	World	32822992	1984
1W	World	33852269	1983
1W	World	34370124	1982
1W	World	32418039	1981
1W	World	33480327	1980
1W	World	33615148	1979
1W	World	32120464	1978
1W	World	31553905	1977
1W	World	30301514	1976
1W	World	28860203	1975
1W	World	28434027	1974
1W	World	29107899	1973
1W	World	28148781	1972
1W	World	26263241	1971
1W	World	27660218	1970
1W	World	NA	1969
1W	World	NA	1968
1W	World	NA	1967
1W	World	NA	1966
1W	World	NA	1965
1W	World	NA	1964
1W	World	NA	1963
1W	World	NA	1962
1W	World	NA	1961
1W	World	NA	1960

4.1.2 Data mapping

# Mapping function
OGS.MAP.WB = function(Worldbank_data, start_date, end_date, geoscale, geoname.filter){
  Worldbank_data_OGS = Worldbank_data %>% 
    add_column(data.source = "World.Bank",
               data.provider = "World.Bank",
               geo.scale = geoscale,
               sector = "All",
               gas = "All",
               geo.code.iso3c = NA,
               unit = "MtCO₂e") %>% 
    rename(geo.code.iso2c = iso2c,
           geo.name = country,
           value = EN.ATM.GHGT.KT.CE) %>% 
    select(data.source, data.provider, geo.scale, geo.code.iso2c, geo.code.iso3c, geo.name,
           year, sector, gas, value) %>% 
    filter(year >= start_date & year <= end_date) %>% 
    filter(geo.name == geoname.filter) %>%
    mutate(value = value * 0.001)
  
  return(Worldbank_data_OGS)
}

# mapping data
Worldbank_data_OGS = OGS.MAP.WB(Worldbank_data = ghg_emissions_wb,
                                start_date = 1970,
                                end_date = 2012,
                                geoscale = "World",
                                geoname.filter = "World" )

# plot timeserie
Worldbank_data_OGS %>% 
  plot_ly() %>% 
  add_trace(y = ~value, 
            x = ~year, 
            type = 'scatter',
            mode = 'lines+markers',
            orientation = "v",
            hoverinfo = 'text',
            text = ~paste('</br> Year: ', year,
                          '</br> Value: ', value,
                          '</br> Gas: ', gas,
                          '</br> Sector: ', sector,
                          '</br> Source: ', data.provider)) %>% 
  layout(title = "World GHG emissions (source: WRI - CAIT)",
         yaxis = list(title = "GHG emissions"), 
         xaxis = list(title = "Years"))

4.2 World Resources Institute

The World Resources Institute compile various sources of GHG emissions and provide access to this data through a specific tool: CLIMAT WATCH. Climate Watch enables the exploration of historical GHG emissions data, downnload and collection via a specified API.

# load data
ghg_emissions_wri = read.csv("https://raw.githubusercontent.com/OpenGeoScales/CarbonData/feature-article/datasets/raw/wri/historical_emissions/historical_emissions.csv", 
                             encoding = "UTF-8")

Historic emission data is provided in wide format and contains emissions for various sectors and gases. The table below describes the data content.

Column	Description
Country	The country name
Data.source	Five data sources: CAIT, PIK, GCP, UNFCCC_AI, UNFCCC_NAI
Sector	30 categories of sectors: Total including LUCF,Total excluding LUCF, Total fossil fuels and cement, Electricity/Heat, Coal, Oil…
Gas	8 categories of sectors: All GHG,KYOTOGHG, CO2, Aggregate GHGs, CH4, N2O, F-Gas , Aggregate F-gases
Unit	One unit: MtCO₂e
1850	Emission quantity in 1850
1851	Emission quantity in 1850
..	Emission quantity in 1851
2019	Emission quantity in 2019

Sector categories by data source:

We observe that sector catagories differ depending on data sources:

# get sectors from different data sources
sector_CAIT = ghg_emissions_wri %>% 
  filter(Data.source == "CAIT") %>% 
  distinct(Sector) %>% 
  select(Sector.CAIT = Sector) %>% 
  arrange(Sector.CAIT)

sector_PIK = ghg_emissions_wri %>% 
  # select PIK datasource
  filter(Data.source == "PIK") %>% 
  distinct(Sector) %>% 
  select(Sector.PIK = Sector) %>% 
  arrange(Sector.PIK)

sector_GCP = ghg_emissions_wri %>% 
  # select GCP datasource
  filter(Data.source == "GCP") %>% 
  distinct(Sector) %>% 
  select(Sector.GCP = Sector) %>% 
  arrange(Sector.GCP)

sector_UNFCCC_AI = ghg_emissions_wri %>% 
  # select UNFCCC_AI datasource
  filter(Data.source == "UNFCCC_AI") %>% 
  distinct(Sector) %>%  
  select(Sector.UNFCCC_AI = Sector) %>% 
  arrange(Sector.UNFCCC_AI)

sector_UNFCCC_NAI = ghg_emissions_wri %>% 
  # select UNFCCC_NAI datasource
  filter(Data.source == "UNFCCC_NAI") %>% 
  distinct(Sector) %>% 
  select(Sector.UNFCCC_NAI = Sector) %>% 
  arrange(Sector.UNFCCC_NAI)

# plot table
knitr::kable(list(sector_CAIT, sector_PIK, sector_GCP, sector_UNFCCC_AI, sector_UNFCCC_NAI)) %>% 
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = F, font_size = 12) %>% 
  scroll_box(width = "100%", height = "200px")

Sector.CAIT Agriculture Building Bunker Fuels Electricity/Heat Energy Fugitive Emissions Industrial Processes Land-Use Change and Forestry Manufacturing/Construction Other Fuel Combustion Total excluding LUCF Total including LUCF Transportation Waste

Sector.PIK Agriculture Energy Industrial Processes and Product Use Other Total excluding LULUCF Waste

Sector.GCP Bunkers Cement Coal Gas Gas flaring Oil Total fossil fuels and cement

Sector.UNFCCC_AI Agriculture Energy Industrial Processes and Product Use Land Use, Land-Use Change and Forestry Other Total GHG emissions with LULUCF Total GHG emissions without LULUCF Waste

Sector.UNFCCC_NAI Agriculture Energy Industrial Processes Land-Use Change and Forestry Other Solvent and Other Product Use Total GHG emissions excluding LULUCF/LUCF Total GHG emissions including LULUCF/LUCF Waste

Gas categories by data source

We observe that gases catagories differ depending on data sources:

# get gas from different data sources
gas_CAIT = ghg_emissions_wri %>% 
  filter(Data.source == "CAIT") %>% 
  distinct(Gas) %>% 
  select(Gas.CAIT = Gas) %>% 
  arrange(Gas.CAIT)

gas_PIK = ghg_emissions_wri %>% 
  # select PIK datasource
  filter(Data.source == "PIK") %>% 
  distinct(Gas) %>% 
  select(Gas.PIK = Gas) %>% 
  arrange(Gas.PIK)

gas_GCP = ghg_emissions_wri %>% 
  # select GCP datasource
  filter(Data.source == "GCP") %>% 
  distinct(Gas) %>% 
  select(Gas.GCP = Gas) %>% 
  arrange(Gas.GCP)

gas_UNFCCC_AI = ghg_emissions_wri %>% 
  # select UNFCCC_AI datasource
  filter(Data.source == "UNFCCC_AI") %>% 
  distinct(Gas) %>%  
  select(Gas.UNFCCC_AI = Gas) %>% 
  arrange(Gas.UNFCCC_AI)

gas_UNFCCC_NAI = ghg_emissions_wri %>% 
  # select UNFCCC_NAI datasource
  filter(Data.source == "UNFCCC_NAI") %>% 
  distinct(Gas) %>% 
  select(Gas.UNFCCC_NAI = Gas) %>% 
  arrange(Gas.UNFCCC_NAI)

# plot table
knitr::kable(list(gas_CAIT, gas_PIK, gas_GCP, gas_UNFCCC_AI, gas_UNFCCC_NAI)) %>% 
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = F, font_size = 12) %>% 
  scroll_box(width = "100%", height = "200px")

Gas.CAIT All GHG CH4 CO2 F-Gas N2O

Gas.PIK CH4 CO2 F-Gas KYOTOGHG N2O

Gas.GCP CO2

Gas.UNFCCC_AI Aggregate F-gases Aggregate GHGs CH4 CO2 N2O

Gas.UNFCCC_NAI Aggregate F-gases Aggregate GHGs CH4 CO2 N2O

Considered countries by data source

The number of considered countries is not the same depending on the data sources as shown in the table below:

ghg_emissions_wri %>% 
  group_by(Data.source) %>% 
  summarise(Nb.Country=length(unique(Country))) %>% 
  kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = F, font_size = 13) 

Data.source	Nb.Country
CAIT	195
GCP	196
PIK	216
UNFCCC_AI	45
UNFCCC_NAI	148

• CAIT data source contains data for 193 distinct countries (all countries that are member states of the United Nations) with aggregated data on world scale (World) and European Union (European Union (27))

• GCP data source contains data for the 193 countries members states of the United Nations plus the state of Palestine and aggregated data on world scale (World) and European Union (European Union (27))

• PIK data source contains data for the 193 countries members states of the United Nations in addition to aggregated data on groups of countries such as: Umbrella Group, Least Developed Countries, Non-Annex-I Parties to the Convention, Annex-I Parties to the Convention…

• UNFCCC Annex I data source contains data for 43 countries members of the Annex I convention with aggregated data on belonging parties (Annex-I Parties to the Convention) and European Union scale (European Union (27)).

• UNFCCC Non-Annex I data source contains data for 148 countries that are parts of the non-Annex I convention.

4.2.1 WRI-CAIT Data description

• Description:

CAIT Historic allow for easy access, analysis and visualization of the latest available international greenhouse gas emissions data. It includes information for 191 countries and the European Union, 50 U.S. states, 6 gases, multiple economic sectors, and 160 years - carbon dioxide emissions for 1850-2014 and multi-sector greenhouse gas emission for 1990-2014.

More description is available here

• License and reuse:

All CAIT data carries a Creative Commons Attribution-NonCommercial 4.0 International license. This means CAIT data and analysis can be used in non-commercial applications, provided clear attribution to WRI/CAIT is given. Additionally to citing CAIT it is recommended consider citing the data sources that CAIT is using.

CAIT 2.0 UNFCCC data derives directly from United Nations Framework Convention on Climate Change (UNFCCC) Secretariat. Additionally to citing CAIT 2.0 please consider citing UNFCCC data source that CAIT 2.0 is using: United Nations Framework Convention on Climate Change (UNFCCC) Secretariat. 2013. “Time Series – Annex I.” Bonn: UNFCCC.

# data processing

ghg_emissions_wri_CAIT = ghg_emissions_wri %>% 
  # select all world data & all gases data & total sector
  filter(Data.source == "CAIT",
         Country == "World", 
         Gas == "All GHG",
         Sector == "Total including LUCF") %>% 
  # pivot data
  pivot_longer(
    cols = starts_with("X"), 
    names_to = "year",
    names_prefix = "X",
    values_to = "value") %>% 
  mutate_at("year" , as.numeric) %>% 
  arrange(year)

# plot table
ghg_emissions_wri_CAIT %>% 
  kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = F, font_size = 13) %>% 
  scroll_box(width = "100%", height = "200px")

Country	Data.source	Sector	Gas	Unit	year	value
World	CAIT	Total including LUCF	All GHG	MtCO2e	1850	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1851	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1852	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1853	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1854	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1855	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1856	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1857	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1858	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1859	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1860	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1861	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1862	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1863	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1864	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1865	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1866	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1867	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1868	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1869	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1870	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1871	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1872	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1873	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1874	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1875	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1876	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1877	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1878	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1879	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1880	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1881	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1882	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1883	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1884	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1885	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1886	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1887	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1888	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1889	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1890	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1891	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1892	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1893	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1894	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1895	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1896	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1897	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1898	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1899	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1900	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1901	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1902	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1903	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1904	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1905	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1906	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1907	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1908	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1909	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1910	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1911	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1912	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1913	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1914	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1915	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1916	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1917	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1918	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1919	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1920	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1921	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1922	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1923	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1924	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1925	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1926	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1927	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1928	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1929	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1930	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1931	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1932	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1933	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1934	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1935	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1936	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1937	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1938	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1939	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1940	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1941	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1942	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1943	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1944	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1945	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1946	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1947	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1948	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1949	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1950	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1951	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1952	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1953	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1954	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1955	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1956	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1957	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1958	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1959	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1960	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1961	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1962	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1963	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1964	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1965	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1966	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1967	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1968	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1969	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1970	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1971	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1972	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1973	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1974	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1975	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1976	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1977	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1978	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1979	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1980	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1981	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1982	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1983	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1984	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1985	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1986	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1987	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1988	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1989	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	1990	34964.58
World	CAIT	Total including LUCF	All GHG	MtCO2e	1991	35125.92
World	CAIT	Total including LUCF	All GHG	MtCO2e	1992	34982.15
World	CAIT	Total including LUCF	All GHG	MtCO2e	1993	35080.3
World	CAIT	Total including LUCF	All GHG	MtCO2e	1994	35283.88
World	CAIT	Total including LUCF	All GHG	MtCO2e	1995	36004.23
World	CAIT	Total including LUCF	All GHG	MtCO2e	1996	36022.1
World	CAIT	Total including LUCF	All GHG	MtCO2e	1997	37338.36
World	CAIT	Total including LUCF	All GHG	MtCO2e	1998	36981.63
World	CAIT	Total including LUCF	All GHG	MtCO2e	1999	36817.13
World	CAIT	Total including LUCF	All GHG	MtCO2e	2000	37438.04
World	CAIT	Total including LUCF	All GHG	MtCO2e	2001	38396.69
World	CAIT	Total including LUCF	All GHG	MtCO2e	2002	39855.92
World	CAIT	Total including LUCF	All GHG	MtCO2e	2003	40703.74
World	CAIT	Total including LUCF	All GHG	MtCO2e	2004	42477.66
World	CAIT	Total including LUCF	All GHG	MtCO2e	2005	43360.31
World	CAIT	Total including LUCF	All GHG	MtCO2e	2006	43739.49
World	CAIT	Total including LUCF	All GHG	MtCO2e	2007	44590.03
World	CAIT	Total including LUCF	All GHG	MtCO2e	2008	44953.73
World	CAIT	Total including LUCF	All GHG	MtCO2e	2009	44907.04
World	CAIT	Total including LUCF	All GHG	MtCO2e	2010	46637.83
World	CAIT	Total including LUCF	All GHG	MtCO2e	2011	47915.71
World	CAIT	Total including LUCF	All GHG	MtCO2e	2012	48477.53
World	CAIT	Total including LUCF	All GHG	MtCO2e	2013	49037.46
World	CAIT	Total including LUCF	All GHG	MtCO2e	2014	49494.56
World	CAIT	Total including LUCF	All GHG	MtCO2e	2015	49828.88
World	CAIT	Total including LUCF	All GHG	MtCO2e	2016	49312.19
World	CAIT	Total including LUCF	All GHG	MtCO2e	2017	49947.42
World	CAIT	Total including LUCF	All GHG	MtCO2e	2018	N/A
World	CAIT	Total including LUCF	All GHG	MtCO2e	2019	N/A

4.2.2 WRI-CAIT Data mapping

OGS.MAP.WRI.CAIT = function(wri_data, start_date, end_date, geoscale, datasource.filter, geoname.filter, gas.filter, sector.filter){
  wri_data_OGS = wri_data %>% 
    filter(Data.source == datasource.filter,
           Country == geoname.filter) %>% 
    # recode sectors and gas values
    mutate(sector = fct_collapse(Sector, "All" = "Total including LUCF"),
           gas = fct_collapse(Gas, "All" = "All GHG")) %>% 
    # filter gas and sector
    filter(sector == sector.filter,
           gas == gas.filter) %>% 
    # pivot data
    pivot_longer(
      cols = starts_with("X"), 
      names_to = "year",
      names_prefix = "X",
      values_to = "value") %>% 
    mutate_at("year" , as.numeric) %>% 
    arrange(year) %>% 
    add_column(data.provider = "WRI.CAIT",
               geo.scale = geoscale,
               geo.code.iso3c = NA,
               geo.code.iso2c = NA,
               unit = "MtCO₂e") %>% 
    rename(data.source = Data.source,
           geo.name = Country,
           value = value) %>% 
    select(data.source, data.provider, geo.scale, geo.code.iso2c, geo.code.iso3c, geo.name,
           year, sector, gas, value) %>% 
    filter(year >= start_date & year <= end_date) %>% 
    mutate_at("value" , as.numeric) 
  
  return(wri_data_OGS)
}


wri_data_OGS = OGS.MAP.WRI.CAIT(wri_data = ghg_emissions_wri,
                                datasource.filter = "CAIT",
                                geoname.filter = "World",
                                gas.filter = "All",
                                sector.filter = "All",
                                geoscale = "World",
                                start_date = 1990,
                                end_date = 2016)

# plot timeserie
wri_data_OGS %>% 
  # filter(year >= 1990 & year < 2017) %>% 
  plot_ly() %>% 
  add_trace(y = ~value, 
            x = ~year, 
            type = 'scatter',
            mode = 'lines+markers',
            orientation = "v",
            hoverinfo = 'text',
            text = ~paste('</br> Year: ', year,
                          '</br> Value: ', value,
                          '</br> Gas: ', gas,
                          '</br> Sector: ', sector,
                          '</br> Source: ', data.provider)) %>% 
  layout(title = "World GHG emissions (source: WRI - CAIT)",
         yaxis = list(title = "GHG emissions"), 
         xaxis = list(title = "Years"))

4.3 Our world in data

4.3.1 Data description

# read co2 data
ghg_emissions_owid= read.csv("https://raw.githubusercontent.com/owid/co2-data/master/owid-co2-data.csv", 
                             encoding = "UTF-8")

# plot table
ghg_emissions_owid %>% 
  filter(country == "World") %>% 
  kable() %>%
  kable_styling(bootstrap_options = c("striped", "hover"), full_width = F, font_size = 13) %>% 
  scroll_box(width = "100%", height = "200px")

iso_code	country	year	co2	co2_growth_prct	co2_growth_abs	consumption_co2	trade_co2	trade_co2_share	co2_per_capita	consumption_co2_per_capita	share_global_co2	cumulative_co2	share_global_cumulative_co2	co2_per_gdp	consumption_co2_per_gdp	co2_per_unit_energy	cement_co2	coal_co2	flaring_co2	gas_co2	oil_co2	other_industry_co2	cement_co2_per_capita	coal_co2_per_capita	flaring_co2_per_capita	gas_co2_per_capita	oil_co2_per_capita	other_co2_per_capita	share_global_coal_co2	share_global_oil_co2	share_global_gas_co2	share_global_flaring_co2	share_global_cement_co2	cumulative_coal_co2	cumulative_oil_co2	cumulative_gas_co2	cumulative_flaring_co2	cumulative_cement_co2	share_global_cumulative_coal_co2	share_global_cumulative_oil_co2	share_global_cumulative_gas_co2	share_global_cumulative_flaring_co2	share_global_cumulative_cement_co2	total_ghg	ghg_per_capita	methane	methane_per_capita	nitrous_oxide	nitrous_oxide_per_capita	primary_energy_consumption	energy_per_capita	energy_per_gdp	population	gdp
OWID_WRL	World	1750	9.351	NA	NA	NA	NA	NA	0.012	NA	100	9.351	100	NA	NA	NA	NA	9.351	NA	NA	NA	NA	NA	0.012	NA	NA	NA	NA	100	NA	NA	NA	NA	9.351	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	811562112	NA
OWID_WRL	World	1751	9.351	NA	NA	NA	NA	NA	NA	NA	100	18.701	100	NA	NA	NA	NA	9.351	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	18.701	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1752	9.354	0.039	0.004	NA	NA	NA	NA	NA	100	28.055	100	NA	NA	NA	NA	9.354	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	28.055	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1753	9.354	NA	NA	NA	NA	NA	NA	NA	100	37.409	100	NA	NA	NA	NA	9.354	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	37.409	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1754	9.358	0.039	0.004	NA	NA	NA	NA	NA	100	46.767	100	NA	NA	NA	NA	9.358	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	46.767	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1755	9.362	0.039	0.004	NA	NA	NA	NA	NA	100	56.129	100	NA	NA	NA	NA	9.362	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	56.129	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1756	10.006	6.888	0.645	NA	NA	NA	NA	NA	100	66.135	100	NA	NA	NA	NA	10.006	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	66.135	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1757	10.010	0.037	0.004	NA	NA	NA	NA	NA	100	76.145	100	NA	NA	NA	NA	10.010	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	76.145	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1758	10.014	0.037	0.004	NA	NA	NA	NA	NA	100	86.159	100	NA	NA	NA	NA	10.014	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	86.159	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1759	10.017	0.037	0.004	NA	NA	NA	NA	NA	100	96.176	100	NA	NA	NA	NA	10.017	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	96.176	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1760	10.017	NA	NA	NA	NA	NA	NA	NA	100	106.194	100	NA	NA	NA	NA	10.017	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	106.194	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1761	10.974	9.546	0.956	NA	NA	NA	NA	NA	100	117.167	100	NA	NA	NA	NA	10.974	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	117.167	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1762	10.977	0.033	0.004	NA	NA	NA	NA	NA	100	128.145	100	NA	NA	NA	NA	10.977	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	128.145	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1763	10.981	0.033	0.004	NA	NA	NA	NA	NA	100	139.126	100	NA	NA	NA	NA	10.981	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	139.126	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1764	10.985	0.033	0.004	NA	NA	NA	NA	NA	100	150.110	100	NA	NA	NA	NA	10.985	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	150.110	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1765	10.988	0.033	0.004	NA	NA	NA	NA	NA	100	161.099	100	NA	NA	NA	NA	10.988	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	161.099	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1766	12.260	11.571	1.271	NA	NA	NA	NA	NA	100	173.358	100	NA	NA	NA	NA	12.260	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	173.358	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1767	12.263	0.030	0.004	NA	NA	NA	NA	NA	100	185.622	100	NA	NA	NA	NA	12.263	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	185.622	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1768	12.267	0.030	0.004	NA	NA	NA	NA	NA	100	197.889	100	NA	NA	NA	NA	12.267	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	197.889	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1769	12.271	0.030	0.004	NA	NA	NA	NA	NA	100	210.160	100	NA	NA	NA	NA	12.271	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	210.160	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1770	12.274	0.030	0.004	NA	NA	NA	NA	NA	100	222.434	100	NA	NA	NA	NA	12.274	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	222.434	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1771	13.612	10.896	1.337	NA	NA	NA	NA	NA	100	236.046	100	NA	NA	NA	NA	13.612	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	236.046	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1772	13.615	0.027	0.004	NA	NA	NA	NA	NA	100	249.661	100	NA	NA	NA	NA	13.615	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	249.661	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1773	13.619	0.027	0.004	NA	NA	NA	NA	NA	100	263.280	100	NA	NA	NA	NA	13.619	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	263.280	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1774	13.623	0.027	0.004	NA	NA	NA	NA	NA	100	276.903	100	NA	NA	NA	NA	13.623	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	276.903	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1775	13.626	0.027	0.004	NA	NA	NA	NA	NA	100	290.530	100	NA	NA	NA	NA	13.626	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	290.530	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1776	15.037	10.352	1.411	NA	NA	NA	NA	NA	100	305.567	100	NA	NA	NA	NA	15.037	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	305.567	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1777	15.041	0.024	0.004	NA	NA	NA	NA	NA	100	320.607	100	NA	NA	NA	NA	15.041	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	320.607	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1778	15.044	0.024	0.004	NA	NA	NA	NA	NA	100	335.652	100	NA	NA	NA	NA	15.044	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	335.652	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1779	15.048	0.024	0.004	NA	NA	NA	NA	NA	100	350.700	100	NA	NA	NA	NA	15.048	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	350.700	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1780	15.055	0.049	0.007	NA	NA	NA	NA	NA	100	365.755	100	NA	NA	NA	NA	15.055	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	365.755	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1781	16.843	11.876	1.788	NA	NA	NA	NA	NA	100	382.599	100	NA	NA	NA	NA	16.843	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	382.599	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1782	16.847	0.022	0.004	NA	NA	NA	NA	NA	100	399.446	100	NA	NA	NA	NA	16.847	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	399.446	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1783	16.854	0.043	0.007	NA	NA	NA	NA	NA	100	416.300	100	NA	NA	NA	NA	16.854	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	416.300	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1784	16.858	0.022	0.004	NA	NA	NA	NA	NA	100	433.158	100	NA	NA	NA	NA	16.858	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	433.158	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1785	16.869	0.065	0.011	NA	NA	NA	NA	NA	100	450.027	100	NA	NA	NA	NA	16.869	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	450.027	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1786	19.152	13.532	2.283	NA	NA	NA	NA	NA	100	469.179	100	NA	NA	NA	NA	19.152	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	469.179	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1787	19.159	0.038	0.007	NA	NA	NA	NA	NA	100	488.338	100	NA	NA	NA	NA	19.159	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	488.338	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1788	19.163	0.019	0.004	NA	NA	NA	NA	NA	100	507.501	100	NA	NA	NA	NA	19.163	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	507.501	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1789	19.170	0.038	0.007	NA	NA	NA	NA	NA	100	526.671	100	NA	NA	NA	NA	19.170	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	526.671	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1790	19.177	0.038	0.007	NA	NA	NA	NA	NA	100	545.848	100	NA	NA	NA	NA	19.177	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	545.848	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1791	21.420	11.693	2.242	NA	NA	NA	NA	NA	100	567.268	100	NA	NA	NA	NA	21.420	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	567.268	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1792	21.896	2.224	0.476	NA	NA	NA	NA	NA	100	589.164	100	NA	NA	NA	NA	21.896	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	589.164	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1793	21.914	0.084	0.018	NA	NA	NA	NA	NA	100	611.078	100	NA	NA	NA	NA	21.914	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	611.078	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1794	21.881	-0.150	-0.033	NA	NA	NA	NA	NA	100	632.960	100	NA	NA	NA	NA	21.881	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	632.960	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1795	21.892	0.050	0.011	NA	NA	NA	NA	NA	100	654.852	100	NA	NA	NA	NA	21.892	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	654.852	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1796	22.951	4.837	1.059	NA	NA	NA	NA	NA	100	677.803	100	NA	NA	NA	NA	22.951	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	677.803	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1797	24.094	4.981	1.143	NA	NA	NA	NA	NA	100	701.898	100	NA	NA	NA	NA	24.094	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	701.898	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1798	25.095	4.151	1.000	NA	NA	NA	NA	NA	100	726.993	100	NA	NA	NA	NA	25.095	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	726.993	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1799	26.428	5.315	1.334	NA	NA	NA	NA	NA	100	753.421	100	NA	NA	NA	NA	26.428	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	753.421	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1800	28.092	6.294	1.663	NA	NA	NA	0.028	NA	100	781.513	100	NA	NA	NA	NA	28.092	NA	NA	NA	NA	NA	0.028	NA	NA	NA	NA	100	NA	NA	NA	NA	781.513	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	989818304	NA
OWID_WRL	World	1801	27.960	-0.470	-0.132	NA	NA	NA	NA	NA	100	809.473	100	NA	NA	NA	NA	27.960	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	809.473	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1802	36.783	31.555	8.823	NA	NA	NA	NA	NA	100	846.256	100	NA	NA	NA	NA	36.783	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	846.256	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1803	31.488	-14.394	-5.294	NA	NA	NA	NA	NA	100	877.744	100	NA	NA	NA	NA	31.488	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	877.744	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1804	34.310	8.960	2.821	NA	NA	NA	NA	NA	100	912.054	100	NA	NA	NA	NA	34.310	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	912.054	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1805	33.419	-2.595	-0.890	NA	NA	NA	NA	NA	100	945.473	100	NA	NA	NA	NA	33.419	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	945.473	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1806	35.046	4.868	1.627	NA	NA	NA	NA	NA	100	980.519	100	NA	NA	NA	NA	35.046	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	980.519	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1807	36.874	5.217	1.828	NA	NA	NA	NA	NA	100	1017.394	100	NA	NA	NA	NA	36.874	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	1017.394	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1808	35.064	-4.909	-1.810	NA	NA	NA	NA	NA	100	1052.458	100	NA	NA	NA	NA	35.064	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	1052.458	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1809	35.090	0.073	0.026	NA	NA	NA	NA	NA	100	1087.548	100	NA	NA	NA	NA	35.090	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	1087.548	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1810	37.380	6.526	2.290	NA	NA	NA	NA	NA	100	1124.929	100	NA	NA	NA	NA	37.380	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	1124.929	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1811	39.582	5.891	2.202	NA	NA	NA	NA	NA	100	1164.511	100	NA	NA	NA	NA	39.582	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	1164.511	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1812	41.007	3.601	1.425	NA	NA	NA	NA	NA	100	1205.518	100	NA	NA	NA	NA	41.007	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	1205.518	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1813	41.220	0.518	0.213	NA	NA	NA	NA	NA	100	1246.738	100	NA	NA	NA	NA	41.220	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	1246.738	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1814	42.129	2.204	0.909	NA	NA	NA	NA	NA	100	1288.867	100	NA	NA	NA	NA	42.129	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	1288.867	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1815	43.488	3.227	1.359	NA	NA	NA	NA	NA	100	1332.355	100	NA	NA	NA	NA	43.488	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	1332.355	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1816	47.665	9.605	4.177	NA	NA	NA	NA	NA	100	1380.020	100	NA	NA	NA	NA	47.665	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	1380.020	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1817	49.431	3.705	1.766	NA	NA	NA	NA	NA	100	1429.451	100	NA	NA	NA	NA	49.431	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	1429.451	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1818	49.644	0.430	0.213	NA	NA	NA	NA	NA	100	1479.095	100	NA	NA	NA	NA	49.644	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	1479.095	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1819	49.948	0.613	0.304	NA	NA	NA	NA	NA	100	1529.042	100	NA	NA	NA	NA	49.948	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	1529.042	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1820	50.688	1.482	0.740	NA	NA	NA	NA	NA	100	1579.730	100	NA	NA	NA	NA	50.688	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	1579.730	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1821	51.435	1.475	0.747	NA	NA	NA	NA	NA	100	1631.165	100	NA	NA	NA	NA	51.435	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	1631.165	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1822	53.465	3.946	2.030	NA	NA	NA	NA	NA	100	1684.630	100	NA	NA	NA	NA	53.465	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	1684.630	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1823	56.550	5.770	3.085	NA	NA	NA	NA	NA	100	1741.180	100	NA	NA	NA	NA	56.550	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	1741.180	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1824	58.525	3.492	1.975	NA	NA	NA	NA	NA	100	1799.706	100	NA	NA	NA	NA	58.525	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	1799.706	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1825	60.756	3.813	2.231	NA	NA	NA	NA	NA	100	1860.462	100	NA	NA	NA	NA	60.756	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	1860.462	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1826	61.420	1.092	0.663	NA	NA	NA	NA	NA	100	1921.882	100	NA	NA	NA	NA	61.420	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	1921.882	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1827	65.915	7.320	4.496	NA	NA	NA	NA	NA	100	1987.797	100	NA	NA	NA	NA	65.915	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	1987.797	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1828	66.637	1.095	0.722	NA	NA	NA	NA	NA	100	2054.434	100	NA	NA	NA	NA	66.637	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	2054.434	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1829	66.395	-0.363	-0.242	NA	NA	NA	NA	NA	100	2120.829	100	NA	NA	NA	NA	66.395	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	2120.829	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1830	89.123	34.231	22.728	NA	NA	NA	NA	NA	100	2209.953	100	NA	NA	NA	NA	89.123	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	2209.953	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1831	84.528	-5.155	-4.595	NA	NA	NA	NA	NA	100	2294.481	100	NA	NA	NA	NA	84.528	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	2294.481	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1832	85.111	0.689	0.583	NA	NA	NA	NA	NA	100	2379.592	100	NA	NA	NA	NA	85.111	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	2379.592	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1833	86.807	1.993	1.696	NA	NA	NA	NA	NA	100	2466.400	100	NA	NA	NA	NA	86.807	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	2466.400	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1834	88.486	1.933	1.678	NA	NA	NA	NA	NA	100	2554.885	100	NA	NA	NA	NA	88.486	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	2554.885	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1835	90.446	2.215	1.960	NA	NA	NA	NA	NA	100	2645.331	100	NA	NA	NA	NA	90.446	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	2645.331	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1836	104.776	15.844	14.330	NA	NA	NA	NA	NA	100	2750.107	100	NA	NA	NA	NA	104.776	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	2750.107	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1837	104.691	-0.080	-0.084	NA	NA	NA	NA	NA	100	2854.798	100	NA	NA	NA	NA	104.691	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	2854.798	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1838	108.044	3.202	3.353	NA	NA	NA	NA	NA	100	2962.842	100	NA	NA	NA	NA	108.044	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	2962.842	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1839	111.613	3.303	3.569	NA	NA	NA	NA	NA	100	3074.455	100	NA	NA	NA	NA	111.613	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	3074.455	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1840	118.930	6.556	7.317	NA	NA	NA	NA	NA	100	3193.385	100	NA	NA	NA	NA	118.930	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	3193.385	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1841	122.619	3.102	3.690	NA	NA	NA	NA	NA	100	3316.004	100	NA	NA	NA	NA	122.619	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	3316.004	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1842	129.504	5.615	6.885	NA	NA	NA	NA	NA	100	3445.508	100	NA	NA	NA	NA	129.504	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	3445.508	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1843	132.853	2.586	3.349	NA	NA	NA	NA	NA	100	3578.361	100	NA	NA	NA	NA	132.853	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	3578.361	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1844	141.423	6.451	8.570	NA	NA	NA	NA	NA	100	3719.784	100	NA	NA	NA	NA	141.423	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	3719.784	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1845	155.211	9.749	13.788	NA	NA	NA	NA	NA	100	3874.995	100	NA	NA	NA	NA	155.211	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	3874.995	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1846	157.794	1.664	2.583	NA	NA	NA	NA	NA	100	4032.789	100	NA	NA	NA	NA	157.794	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	4032.789	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1847	172.402	9.258	14.608	NA	NA	NA	NA	NA	100	4205.191	100	NA	NA	NA	NA	172.402	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	4205.191	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1848	173.816	0.820	1.414	NA	NA	NA	NA	NA	100	4379.008	100	NA	NA	NA	NA	173.816	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	4379.008	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1849	183.511	5.578	9.695	NA	NA	NA	NA	NA	100	4562.519	100	NA	NA	NA	NA	183.511	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	4562.519	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1850	196.896	7.294	13.385	NA	NA	NA	0.156	NA	100	4759.415	100	NA	NA	NA	NA	196.896	NA	NA	NA	NA	NA	0.156	NA	NA	NA	NA	100	NA	NA	NA	NA	4759.415	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	1262682112	NA
OWID_WRL	World	1851	198.805	0.970	1.909	NA	NA	NA	NA	NA	100	4958.220	100	NA	NA	NA	NA	198.805	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	4958.220	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1852	207.551	4.399	8.746	NA	NA	NA	NA	NA	100	5165.771	100	NA	NA	NA	NA	207.551	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	5165.771	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1853	217.209	4.653	9.658	NA	NA	NA	NA	NA	100	5382.980	100	NA	NA	NA	NA	217.209	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	5382.980	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1854	255.139	17.462	37.930	NA	NA	NA	NA	NA	100	5638.119	100	NA	NA	NA	NA	255.139	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	100	NA	NA	NA	NA	5638.119	NA	NA	NA	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1855	260.166	1.970	5.027	NA	NA	NA	NA	NA	100	5898.285	100	NA	NA	NA	NA	260.129	NA	NA	0.037	NA	NA	NA	NA	NA	NA	NA	100	100	NA	NA	NA	5898.249	0.037	NA	NA	NA	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1856	277.292	6.583	17.126	NA	NA	NA	NA	NA	100	6175.577	100	NA	NA	NA	NA	277.251	NA	NA	0.040	NA	NA	NA	NA	NA	NA	NA	100	100	NA	NA	NA	6175.500	0.077	NA	NA	NA	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1857	279.889	0.937	2.598	NA	NA	NA	NA	NA	100	6455.466	100	NA	NA	NA	NA	279.838	NA	NA	0.051	NA	NA	NA	NA	NA	NA	NA	100	100	NA	NA	NA	6455.338	0.128	NA	NA	NA	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1858	284.172	1.530	4.283	NA	NA	NA	NA	NA	100	6739.638	100	NA	NA	NA	NA	284.110	NA	NA	0.062	NA	NA	NA	NA	NA	NA	NA	100	100	NA	NA	NA	6739.448	0.191	NA	NA	NA	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1859	301.313	6.032	17.141	NA	NA	NA	NA	NA	100	7040.951	100	NA	NA	NA	NA	301.258	NA	NA	0.055	NA	NA	NA	NA	NA	NA	NA	100	100	NA	NA	NA	7040.705	0.245	NA	NA	NA	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1860	330.642	9.734	29.330	NA	NA	NA	NA	NA	100	7371.593	100	NA	NA	NA	NA	330.369	NA	NA	0.274	NA	NA	NA	NA	NA	NA	NA	100	100	NA	NA	NA	7371.074	0.519	NA	NA	NA	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1861	347.735	5.170	17.093	NA	NA	NA	NA	NA	100	7719.329	100	NA	NA	NA	NA	346.794	NA	NA	0.942	NA	NA	NA	NA	NA	NA	NA	100	100	NA	NA	NA	7717.868	1.461	NA	NA	NA	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1862	354.058	1.818	6.322	NA	NA	NA	NA	NA	100	8073.387	100	NA	NA	NA	NA	352.659	NA	NA	1.399	NA	NA	NA	NA	NA	NA	NA	100	100	NA	NA	NA	8070.526	2.860	NA	NA	NA	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1863	377.860	6.723	23.803	NA	NA	NA	NA	NA	100	8451.247	100	NA	NA	NA	NA	376.566	NA	NA	1.294	NA	NA	NA	NA	NA	NA	NA	100	100	NA	NA	NA	8447.093	4.154	NA	NA	NA	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1864	407.091	7.736	29.231	NA	NA	NA	NA	NA	100	8858.338	100	NA	NA	NA	NA	405.981	NA	NA	1.111	NA	NA	NA	NA	NA	NA	NA	100	100	NA	NA	NA	8853.073	5.265	NA	NA	NA	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1865	432.308	6.194	25.217	NA	NA	NA	NA	NA	100	9290.646	100	NA	NA	NA	NA	430.987	NA	NA	1.321	NA	NA	NA	NA	NA	NA	NA	100	100	NA	NA	NA	9284.061	6.585	NA	NA	NA	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1866	445.971	3.160	13.663	NA	NA	NA	NA	NA	100	9736.617	100	NA	NA	NA	NA	444.017	NA	NA	1.954	NA	NA	NA	NA	NA	NA	NA	100	100	NA	NA	NA	9728.078	8.539	NA	NA	NA	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1867	477.815	7.140	31.844	NA	NA	NA	NA	NA	100	10214.432	100	NA	NA	NA	NA	475.828	NA	NA	1.987	NA	NA	NA	NA	NA	NA	NA	100	100	NA	NA	NA	10203.906	10.526	NA	NA	NA	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1868	490.752	2.708	12.938	NA	NA	NA	NA	NA	100	10705.185	100	NA	NA	NA	NA	488.461	NA	NA	2.291	NA	NA	NA	NA	NA	NA	NA	100	100	NA	NA	NA	10692.367	12.817	NA	NA	NA	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1869	521.131	6.190	30.378	NA	NA	NA	NA	NA	100	11226.315	100	NA	NA	NA	NA	518.513	NA	NA	2.617	NA	NA	NA	NA	NA	NA	NA	100	100	NA	NA	NA	11210.881	15.435	NA	NA	NA	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1870	532.537	2.189	11.406	NA	NA	NA	NA	NA	100	11758.852	100	0.416	NA	NA	NA	529.401	NA	NA	3.136	NA	NA	NA	NA	NA	NA	NA	100	100	NA	NA	NA	11740.281	18.571	NA	NA	NA	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	1.28061e+12
OWID_WRL	World	1871	566.143	6.311	33.606	NA	NA	NA	NA	NA	100	12324.995	100	NA	NA	NA	NA	562.858	NA	NA	3.285	NA	NA	NA	NA	NA	NA	NA	100	100	NA	NA	NA	12303.139	21.856	NA	NA	NA	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1872	626.372	10.638	60.229	NA	NA	NA	NA	NA	100	12951.367	100	NA	NA	NA	NA	622.670	NA	NA	3.702	NA	NA	NA	NA	NA	NA	NA	100	100	NA	NA	NA	12925.809	25.558	NA	NA	NA	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1873	665.419	6.234	39.047	NA	NA	NA	NA	NA	100	13616.786	100	NA	NA	NA	NA	659.745	NA	NA	5.674	NA	NA	NA	NA	NA	NA	NA	100	100	NA	NA	NA	13585.554	31.232	NA	NA	NA	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1874	622.997	-6.375	-42.422	NA	NA	NA	NA	NA	100	14239.783	100	NA	NA	NA	NA	616.748	NA	NA	6.249	NA	NA	NA	NA	NA	NA	NA	100	100	NA	NA	NA	14202.302	37.481	NA	NA	NA	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1875	675.799	8.475	52.802	NA	NA	NA	NA	NA	100	14915.582	100	NA	NA	NA	NA	669.957	NA	NA	5.843	NA	NA	NA	NA	NA	NA	NA	100	100	NA	NA	NA	14872.258	43.324	NA	NA	NA	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1876	685.553	1.443	9.754	NA	NA	NA	NA	NA	100	15601.135	100	NA	NA	NA	NA	679.007	NA	NA	6.545	NA	NA	NA	NA	NA	NA	NA	100	100	NA	NA	NA	15551.266	49.869	NA	NA	NA	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1877	699.099	1.976	13.546	NA	NA	NA	NA	NA	100	16300.233	100	NA	NA	NA	NA	690.264	NA	NA	8.835	NA	NA	NA	NA	NA	NA	NA	100	100	NA	NA	NA	16241.529	58.704	NA	NA	NA	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1878	704.383	0.756	5.284	NA	NA	NA	NA	NA	100	17004.616	100	NA	NA	NA	NA	694.328	NA	NA	10.054	NA	NA	NA	NA	NA	NA	NA	100	100	NA	NA	NA	16935.858	68.758	NA	NA	NA	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1879	754.870	7.168	50.488	NA	NA	NA	NA	NA	100	17759.486	100	NA	NA	NA	NA	742.195	NA	NA	12.675	NA	NA	NA	NA	NA	NA	NA	100	100	NA	NA	NA	17678.053	81.433	NA	NA	NA	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1880	853.708	13.093	98.838	NA	NA	NA	NA	NA	100	18613.194	100	NA	NA	NA	NA	838.326	NA	NA	15.382	NA	NA	NA	NA	NA	NA	NA	100	100	NA	NA	NA	18516.379	96.815	NA	NA	NA	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1881	882.408	3.362	28.700	NA	NA	NA	NA	NA	100	19495.602	100	NA	NA	NA	NA	865.143	NA	NA	17.265	NA	NA	NA	NA	NA	NA	NA	100	100	NA	NA	NA	19381.522	114.080	NA	NA	NA	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1882	931.925	5.612	49.517	NA	NA	NA	NA	NA	100	20427.528	100	NA	NA	NA	NA	912.796	NA	0.165	18.964	NA	NA	NA	NA	NA	NA	NA	100	100	100	NA	NA	20294.319	133.044	0.165	NA	NA	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1883	991.036	6.343	59.111	NA	NA	NA	NA	NA	100	21418.564	100	NA	NA	NA	NA	974.126	NA	0.381	16.529	NA	NA	NA	NA	NA	NA	NA	100	100	100	NA	NA	21268.445	149.573	0.546	NA	NA	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1884	1002.179	1.124	11.142	NA	NA	NA	NA	NA	100	22420.742	100	NA	NA	NA	NA	982.645	NA	1.172	18.361	NA	NA	NA	NA	NA	NA	NA	100	100	100	NA	NA	22251.090	167.934	1.718	NA	NA	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1885	1009.675	0.748	7.497	NA	NA	NA	NA	NA	100	23430.418	100	NA	NA	NA	NA	986.953	NA	3.715	19.007	NA	NA	NA	NA	NA	NA	NA	100	100	100	NA	NA	23238.043	186.941	5.434	NA	NA	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1886	1025.480	1.565	15.804	NA	NA	NA	NA	NA	100	24455.897	100	NA	NA	NA	NA	995.930	NA	7.676	21.873	NA	NA	NA	NA	NA	NA	NA	100	100	100	NA	NA	24233.973	208.815	13.110	NA	NA	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1887	1076.762	5.001	51.283	NA	NA	NA	NA	NA	100	25532.660	100	NA	NA	NA	NA	1041.434	NA	11.787	23.541	NA	NA	NA	NA	NA	NA	NA	100	100	100	NA	NA	25275.407	232.356	24.897	NA	NA	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1888	1192.277	10.728	115.515	NA	NA	NA	NA	NA	100	26724.937	100	NA	NA	NA	NA	1150.859	NA	16.774	24.644	NA	NA	NA	NA	NA	NA	NA	100	100	100	NA	NA	26426.266	257.000	41.671	NA	NA	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1889	1191.805	-0.040	-0.471	NA	NA	NA	NA	NA	100	27916.742	100	NA	NA	NA	NA	1151.106	NA	12.227	28.473	NA	NA	NA	NA	NA	NA	NA	100	100	100	NA	NA	27577.372	285.473	53.897	NA	NA	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1890	1298.465	8.949	106.659	NA	NA	NA	NA	NA	100	29215.207	100	0.593	NA	NA	NA	1252.048	NA	11.688	34.728	NA	NA	NA	NA	NA	NA	NA	100	100	100	NA	NA	28829.420	320.201	65.585	NA	NA	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	2.18872e+12
OWID_WRL	World	1891	1358.881	4.653	60.416	NA	NA	NA	NA	NA	100	30574.088	100	NA	NA	NA	NA	1309.013	NA	8.947	40.921	NA	NA	NA	NA	NA	NA	NA	100	100	100	NA	NA	30138.433	361.122	74.533	NA	NA	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1892	1370.085	0.824	11.204	NA	NA	NA	NA	NA	100	31944.172	100	NA	NA	NA	NA	1322.020	NA	7.775	40.289	NA	NA	NA	NA	NA	NA	NA	100	100	100	NA	NA	31460.453	401.411	82.308	NA	NA	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1893	1353.676	-1.198	-16.408	NA	NA	NA	NA	NA	100	33297.849	100	NA	NA	NA	NA	1303.732	NA	7.288	42.657	NA	NA	NA	NA	NA	NA	NA	100	100	100	NA	NA	32764.185	444.068	89.596	NA	NA	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1894	1400.866	3.486	47.190	NA	NA	NA	NA	NA	100	34698.715	100	NA	NA	NA	NA	1352.360	NA	6.698	41.808	NA	NA	NA	NA	NA	NA	NA	100	100	100	NA	NA	34116.545	485.877	96.294	NA	NA	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1895	1485.283	6.026	84.417	NA	NA	NA	NA	NA	100	36183.998	100	NA	NA	NA	NA	1429.571	NA	7.042	48.671	NA	NA	NA	NA	NA	NA	NA	100	100	100	NA	NA	35546.115	534.547	103.336	NA	NA	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1896	1533.713	3.261	48.430	NA	NA	NA	NA	NA	100	37717.712	100	NA	NA	NA	NA	1474.161	NA	6.848	52.704	NA	NA	NA	NA	NA	NA	NA	100	100	100	NA	NA	37020.276	587.252	110.184	NA	NA	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1897	1606.313	4.734	72.600	NA	NA	NA	NA	NA	100	39324.025	100	NA	NA	NA	NA	1543.341	NA	7.288	55.684	NA	NA	NA	NA	NA	NA	NA	100	100	100	NA	NA	38563.618	642.936	117.471	NA	NA	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1898	1694.280	5.476	87.967	NA	NA	NA	NA	NA	100	41018.304	100	NA	NA	NA	NA	1628.475	NA	8.460	57.345	NA	NA	NA	NA	NA	NA	NA	100	100	100	NA	NA	40192.093	700.280	125.931	NA	NA	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1899	1850.909	9.245	156.629	NA	NA	NA	NA	NA	100	42869.213	100	NA	NA	NA	NA	1779.779	NA	10.904	60.226	NA	NA	NA	NA	NA	NA	NA	100	100	100	NA	NA	41971.871	760.506	136.836	NA	NA	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA	NA
OWID_WRL	World	1900	1953.614	5.549	102.705	NA	NA	NA	1.184	NA	100	44822.827	100	NA	NA	NA	1.386	1873.155	NA	11.542	67.532	NA	0.001	1.135	NA	0.007	0.041	NA	100	100	100	NA	100	43845.026	828.038	148.377	NA	1.386	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	1650000000	NA
OWID_WRL	World	1901	2018.421	3.317	64.806	NA	NA	NA	1.216	NA	100	46841.248	100	NA	NA	NA	1.656	1928.919	NA	12.861	74.985	NA	0.001	1.162	NA	0.008	0.045	NA	100	100	100	NA	100	45773.945	903.023	161.238	NA	3.042	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	1659947520	NA
OWID_WRL	World	1902	2069.946	2.553	51.526	NA	NA	NA	1.239	NA	100	48911.194	100	NA	NA	NA	2.145	1973.072	NA	13.696	81.033	NA	0.001	1.181	NA	0.008	0.049	NA	100	100	100	NA	100	47747.017	984.056	174.934	NA	5.187	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	1669996544	NA
OWID_WRL	World	1903	2258.613	9.115	188.667	NA	NA	NA	1.344	NA	100	51169.808	100	NA	NA	NA	2.533	2155.776	NA	14.528	85.776	NA	0.002	1.283	NA	0.009	0.051	NA	100	100	100	NA	100	49902.794	1069.833	189.462	NA	7.720	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	1680114944	NA
OWID_WRL	World	1904	2282.759	1.069	24.146	NA	NA	NA	1.351	NA	100	53452.567	100	NA	NA	NA	2.732	2168.548	NA	15.165	96.315	NA	0.002	1.283	NA	0.009	0.057	NA	100	100	100	NA	100	52071.341	1166.148	204.627	NA	10.451	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	1690269440	NA
OWID_WRL	World	1905	2431.145	6.500	148.385	NA	NA	NA	1.430	NA	100	55883.712	100	NA	NA	NA	3.491	2313.287	NA	17.170	97.198	NA	0.002	1.360	NA	0.010	0.057	NA	100	100	100	NA	100	54384.628	1263.345	221.796	NA	13.942	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	1700426240	NA
OWID_WRL	World	1906	2554.215	5.062	123.071	NA	NA	NA	1.493	NA	100	58437.927	100	NA	NA	NA	4.470	2432.620	NA	19.027	98.099	NA	0.003	1.422	NA	0.011	0.057	NA	100	100	100	NA	100	56817.248	1361.444	240.823	NA	18.411	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	1710550656	NA
OWID_WRL	World	1907	2887.833	13.061	333.617	NA	NA	NA	1.678	NA	100	61325.760	100	NA	NA	NA	4.601	2743.719	NA	19.877	119.636	NA	0.003	1.595	NA	0.012	0.070	NA	100	100	100	NA	100	59560.967	1481.080	260.701	NA	23.012	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	1720606976	NA
OWID_WRL	World	1908	2779.288	-3.759	-108.545	NA	NA	NA	1.606	NA	100	64105.048	100	NA	NA	NA	4.795	2625.838	NA	19.676	128.979	NA	0.003	1.517	NA	0.011	0.075	NA	100	100	100	NA	100	62186.805	1610.059	280.376	NA	27.808	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	1730558976	NA
OWID_WRL	World	1909	2890.654	4.007	111.366	NA	NA	NA	1.661	NA	100	66995.701	100	NA	NA	NA	6.024	2726.523	NA	23.523	134.584	NA	0.003	1.567	NA	0.014	0.077	NA	100	100	100	NA	100	64913.328	1744.643	303.899	NA	33.831	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	1740369408	NA
OWID_WRL	World	1910	3032.156	4.895	141.502	NA	NA	NA	1.733	NA	100	70027.857	100	NA	NA	NA	7.040	2856.120	NA	24.915	144.080	NA	0.004	1.632	NA	0.014	0.082	NA	100	100	100	NA	100	67769.448	1888.723	328.814	NA	40.871	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	1750000000	NA
OWID_WRL	World	1911	3087.264	1.817	55.108	NA	NA	NA	1.755	NA	100	73115.122	100	NA	NA	NA	7.214	2901.556	NA	25.718	152.777	NA	0.004	1.649	NA	0.015	0.087	NA	100	100	100	NA	100	70671.004	2041.500	354.532	NA	48.085	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	1759443072	NA
OWID_WRL	World	1912	3233.403	4.734	146.139	NA	NA	NA	1.828	NA	100	76348.525	100	NA	NA	NA	7.583	3040.508	NA	28.315	156.997	NA	0.004	1.719	NA	0.016	0.089	NA	100	100	100	NA	100	73711.512	2198.497	382.847	NA	55.668	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	1768814592	NA
OWID_WRL	World	1913	3498.266	8.191	264.863	NA	NA	NA	1.967	NA	100	79846.791	100	0.879	NA	NA	8.465	3278.396	NA	29.598	181.807	NA	0.005	1.844	NA	0.017	0.102	NA	100	100	100	NA	100	76989.908	2380.304	412.445	NA	64.133	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	1778264832	3.98137e+12
OWID_WRL	World	1914	3173.996	-9.269	-324.270	NA	NA	NA	1.775	NA	100	83020.786	100	NA	NA	NA	8.062	2949.976	NA	30.136	185.822	NA	0.005	1.650	NA	0.017	0.104	NA	100	100	100	NA	100	79939.884	2566.126	442.582	NA	72.195	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	1787947136	NA
OWID_WRL	World	1915	3130.389	-1.374	-43.607	NA	NA	NA	1.741	NA	100	86151.175	100	NA	NA	NA	7.867	2893.297	NA	31.866	197.359	NA	0.004	1.609	NA	0.018	0.110	NA	100	100	100	NA	100	82833.181	2763.485	474.447	NA	80.062	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	1798018432	NA
OWID_WRL	World	1916	3378.662	7.931	248.274	NA	NA	NA	1.868	NA	100	89529.838	100	NA	NA	NA	8.390	3116.991	NA	38.377	214.905	NA	0.005	1.723	NA	0.021	0.119	NA	100	100	100	NA	100	85950.172	2978.390	512.824	NA	88.452	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	1808639488	NA
OWID_WRL	World	1917	3533.556	4.584	154.893	NA	NA	NA	1.942	NA	100	93063.393	100	NA	NA	NA	8.457	3245.537	NA	40.509	239.052	NA	0.005	1.783	NA	0.022	0.131	NA	100	100	100	NA	100	89195.709	3217.441	553.333	NA	96.909	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	1819975424	NA
OWID_WRL	World	1918	3483.435	-1.418	-50.121	NA	NA	NA	1.901	NA	100	96546.828	100	NA	NA	NA	6.479	3195.730	NA	36.519	244.706	NA	0.004	1.744	NA	0.020	0.134	NA	100	100	100	NA	100	92391.439	3462.148	589.852	NA	103.389	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	1832196096	NA
OWID_WRL	World	1919	3019.921	-13.306	-463.513	NA	NA	NA	1.636	NA	100	99566.749	100	NA	NA	NA	7.354	2701.550	NA	37.948	273.069	NA	0.004	1.464	NA	0.021	0.148	NA	100	100	100	NA	100	95092.989	3735.217	627.800	NA	110.743	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	1845476992	NA
OWID_WRL	World	1920	3513.395	16.341	493.473	NA	NA	NA	1.889	NA	100	103080.144	100	NA	NA	NA	9.015	3112.383	NA	41.685	350.311	NA	0.005	1.673	NA	0.022	0.188	NA	100	100	100	NA	100	98205.372	4085.528	669.486	NA	119.758	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	1860000000	NA
OWID_WRL	World	1921	3083.123	-12.247	-430.272	NA	NA	NA	1.644	NA	100	106163.267	100	NA	NA	NA	8.886	2654.889	NA	34.993	384.356	NA	0.005	1.415	NA	0.019	0.205	NA	100	100	100	NA	100	100860.261	4469.884	704.479	NA	128.643	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	1875909120	NA
OWID_WRL	World	1922	3234.231	4.901	151.109	NA	NA	NA	1.708	NA	100	109397.498	100	NA	NA	NA	10.384	2757.275	NA	40.107	426.464	NA	0.005	1.456	NA	0.021	0.225	NA	100	100	100	NA	100	103617.536	4896.348	744.586	NA	139.028	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	1893171584	NA
OWID_WRL	World	1923	3670.605	13.492	436.374	NA	NA	NA	1.920	NA	100	113068.104	100	NA	NA	NA	12.431	3131.989	NA	52.526	473.659	NA	0.007	1.638	NA	0.027	0.248	NA	100	100	100	NA	100	106749.525	5370.008	797.112	NA	151.459	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	1911708672	NA
OWID_WRL	World	1924	3682.986	0.337	12.381	NA	NA	NA	1.907	NA	100	116751.090	100	NA	NA	NA	13.500	3138.818	NA	59.711	470.958	NA	0.007	1.625	NA	0.031	0.244	NA	100	100	100	NA	100	109888.343	5840.966	856.823	NA	164.959	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	1931439872	NA
OWID_WRL	World	1925	3709.804	0.728	26.817	NA	NA	NA	1.900	NA	100	120460.894	100	NA	NA	NA	14.341	3138.599	NA	63.926	492.938	NA	0.007	1.608	NA	0.033	0.252	NA	100	100	100	NA	100	113026.942	6333.903	920.749	NA	179.300	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	1952281984	NA
OWID_WRL	World	1926	3642.200	-1.822	-67.604	NA	NA	NA	1.845	NA	100	124103.094	100	NA	NA	NA	14.651	3067.560	NA	69.148	490.840	NA	0.007	1.554	NA	0.035	0.249	NA	100	100	100	NA	100	116094.502	6824.743	989.897	NA	193.951	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	1974148992	NA
OWID_WRL	World	1927	3978.374	9.230	336.174	NA	NA	NA	1.992	NA	100	128081.467	100	NA	NA	NA	15.355	3349.093	NA	76.021	537.905	NA	0.008	1.677	NA	0.038	0.269	NA	100	100	100	NA	100	119443.595	7362.649	1065.917	NA	209.306	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	1996950912	NA
OWID_WRL	World	1928	3959.653	-0.471	-18.721	NA	NA	NA	1.960	NA	100	132041.120	100	NA	NA	NA	35.673	3291.700	NA	82.628	549.653	NA	0.018	1.629	NA	0.041	0.272	NA	100	100	100	NA	100	122735.295	7912.301	1148.546	NA	244.979	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2020593536	NA
OWID_WRL	World	1929	4252.190	7.388	292.537	NA	NA	NA	2.079	NA	100	136293.310	100	0.835	NA	NA	36.938	3509.547	NA	102.901	602.804	NA	0.018	1.716	NA	0.050	0.295	NA	100	100	100	NA	100	126244.842	8515.106	1251.446	NA	281.917	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2044978048	5.08999e+12
OWID_WRL	World	1930	3919.399	-7.826	-332.791	NA	NA	NA	1.893	NA	100	140212.709	100	NA	NA	NA	35.625	3202.022	NA	104.232	577.520	NA	0.017	1.547	NA	0.050	0.279	NA	100	100	100	NA	100	129446.863	9092.626	1355.679	NA	317.541	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2070000000	NA
OWID_WRL	World	1931	3502.982	-10.624	-416.416	NA	NA	NA	1.672	NA	100	143715.692	100	NA	NA	NA	31.021	2818.368	NA	92.901	560.692	NA	0.015	1.345	NA	0.044	0.268	NA	100	100	100	NA	100	132265.231	9653.318	1448.580	NA	348.563	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2095524352	NA
OWID_WRL	World	1932	3157.845	-9.853	-345.137	NA	NA	NA	1.489	NA	100	146873.537	100	NA	NA	NA	24.851	2507.728	NA	88.235	537.031	NA	0.012	1.182	NA	0.042	0.253	NA	100	100	100	NA	100	134772.959	10190.349	1536.815	NA	373.414	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2121306496	NA
OWID_WRL	World	1933	3324.972	5.292	167.126	NA	NA	NA	1.549	NA	100	150198.508	100	NA	NA	NA	23.991	2623.163	NA	90.935	586.882	NA	0.011	1.222	NA	0.042	0.273	NA	100	100	100	NA	100	137396.122	10777.231	1627.750	NA	397.405	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2147063296	NA
OWID_WRL	World	1934	3616.315	8.762	291.344	NA	NA	NA	1.665	NA	100	153814.824	100	NA	NA	NA	28.718	2864.469	NA	101.973	621.154	NA	0.013	1.319	NA	0.047	0.286	NA	100	100	100	NA	100	140260.592	11398.385	1729.723	NA	426.123	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2172497152	NA
OWID_WRL	World	1935	3794.457	4.926	178.141	NA	NA	NA	1.727	NA	100	157609.280	100	NA	NA	NA	32.331	2981.135	NA	111.400	669.591	NA	0.015	1.357	NA	0.051	0.305	NA	100	100	100	NA	100	143241.727	12067.976	1841.123	NA	458.454	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2197296384	NA
OWID_WRL	World	1936	4168.554	9.859	374.098	NA	NA	NA	1.877	NA	100	161777.834	100	NA	NA	NA	39.091	3273.864	NA	125.565	730.035	NA	0.018	1.474	NA	0.057	0.329	NA	100	100	100	NA	100	146515.591	12798.011	1966.689	NA	497.545	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2221135616	NA
OWID_WRL	World	1937	4456.655	6.911	288.100	NA	NA	NA	1.986	NA	100	166234.489	100	NA	NA	NA	41.148	3452.448	NA	140.613	822.446	NA	0.018	1.539	NA	0.063	0.367	NA	100	100	100	NA	100	149968.039	13620.457	2107.301	NA	538.692	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2243677440	NA
OWID_WRL	World	1938	4193.546	-5.904	-263.109	NA	NA	NA	1.852	NA	100	170428.035	100	NA	NA	NA	38.920	3221.120	NA	135.532	797.975	NA	0.017	1.422	NA	0.060	0.352	NA	100	100	100	NA	100	153189.159	14418.432	2242.833	NA	577.612	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2264574208	NA
OWID_WRL	World	1939	4435.793	5.777	242.247	NA	NA	NA	1.943	NA	100	174863.828	100	NA	NA	NA	36.049	3427.144	NA	141.366	831.234	NA	0.016	1.501	NA	0.062	0.364	NA	100	100	100	NA	100	156616.302	15249.666	2384.199	NA	613.661	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2283468800	NA
OWID_WRL	World	1940	4847.084	9.272	411.292	NA	NA	NA	2.107	NA	100	179710.912	100	NA	NA	NA	31.651	3788.360	NA	153.570	873.504	NA	0.014	1.647	NA	0.067	0.380	NA	100	100	100	NA	100	160404.663	16123.169	2537.769	NA	645.311	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2300000000	NA
OWID_WRL	World	1941	4955.476	2.236	108.392	NA	NA	NA	2.141	NA	100	184666.388	100	NA	NA	NA	43.463	3876.834	NA	153.463	881.716	NA	0.019	1.675	NA	0.066	0.381	NA	100	100	100	NA	100	164281.497	17004.885	2691.232	NA	688.774	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2314055168	NA
OWID_WRL	World	1942	4935.659	-0.400	-19.817	NA	NA	NA	2.121	NA	100	189602.048	100	NA	NA	NA	38.219	3894.969	NA	166.049	836.422	NA	0.016	1.674	NA	0.071	0.360	NA	100	100	100	NA	100	168176.466	17841.308	2857.281	NA	726.993	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2326540544	NA
OWID_WRL	World	1943	5018.865	1.686	83.206	NA	NA	NA	2.146	NA	100	194620.912	100	NA	NA	NA	34.653	3912.609	NA	182.900	888.703	NA	0.015	1.673	NA	0.078	0.380	NA	100	100	100	NA	100	172089.074	18730.011	3040.181	NA	761.646	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2338643968	NA
OWID_WRL	World	1944	5101.040	1.637	82.175	NA	NA	NA	2.169	NA	100	199721.952	100	NA	NA	NA	22.524	3861.790	NA	197.601	1019.124	NA	0.010	1.642	NA	0.084	0.433	NA	100	100	100	NA	100	175950.864	19749.135	3237.782	NA	784.171	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2351580160	NA
OWID_WRL	World	1945	4240.829	-16.863	-860.211	NA	NA	NA	1.792	NA	100	203962.781	100	NA	NA	NA	22.934	2992.181	NA	216.746	1008.968	NA	0.010	1.264	NA	0.092	0.426	NA	100	100	100	NA	100	178943.046	20758.103	3454.528	NA	807.105	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2366593024	NA
OWID_WRL	World	1946	4629.673	9.169	388.844	NA	NA	NA	1.941	NA	100	208592.454	100	NA	NA	NA	36.312	3228.828	NA	223.895	1140.638	NA	0.015	1.354	NA	0.094	0.478	NA	100	100	100	NA	100	182171.874	21898.741	3678.423	NA	843.417	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2384963584	NA
OWID_WRL	World	1947	5125.894	10.718	496.221	NA	NA	NA	2.129	NA	100	213718.348	100	NA	NA	NA	43.237	3612.469	NA	247.966	1222.223	NA	0.018	1.500	NA	0.103	0.508	NA	100	100	100	NA	100	185784.343	23120.964	3926.388	NA	886.653	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2408024320	NA
OWID_WRL	World	1948	5396.503	5.279	270.609	NA	NA	NA	2.214	NA	100	219114.852	100	NA	NA	NA	51.766	3723.983	NA	280.214	1340.541	NA	0.021	1.528	NA	0.115	0.550	NA	100	100	100	NA	100	189508.326	24461.505	4206.602	NA	938.419	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2437183232	NA
OWID_WRL	World	1949	5239.276	-2.913	-157.227	NA	NA	NA	2.118	NA	100	224354.128	100	NA	NA	NA	58.355	3542.528	NA	300.549	1337.845	NA	0.024	1.432	NA	0.121	0.541	NA	100	100	100	NA	100	193050.854	25799.350	4507.151	NA	996.774	100	100	100	NA	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2473954560	NA
OWID_WRL	World	1950	5998.350	14.488	759.074	NA	NA	NA	2.365	NA	100	230352.479	100	0.726	NA	NA	66.939	3854.980	73.624	353.283	1649.525	NA	0.026	1.520	0.029	0.139	0.650	NA	100	100	100	100	100	196905.833	27448.875	4860.434	73.624	1063.712	100	100	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2536431104	8.26090e+12
OWID_WRL	World	1951	6373.785	6.259	375.434	NA	NA	NA	2.467	NA	100	236726.264	100	0.728	NA	NA	75.576	4058.695	79.875	416.327	1743.311	NA	0.029	1.571	0.031	0.161	0.675	NA	100	100	100	100	100	200964.528	29192.186	5276.761	153.500	1139.289	100	100	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2584034048	8.76367e+12
OWID_WRL	World	1952	6460.163	1.355	86.378	NA	NA	NA	2.456	NA	100	243186.427	100	0.704	NA	NA	81.134	4007.545	85.364	447.229	1838.891	NA	0.031	1.523	0.032	0.170	0.699	NA	100	100	100	100	100	204972.073	31031.077	5723.991	238.863	1220.423	100	100	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2630862080	9.16940e+12
OWID_WRL	World	1953	6641.785	2.811	181.622	NA	NA	NA	2.480	NA	100	249828.212	100	0.685	NA	NA	90.026	4042.021	81.348	474.686	1953.705	NA	0.034	1.510	0.030	0.177	0.730	NA	100	100	100	100	100	209014.094	32984.782	6198.676	320.212	1310.449	100	100	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2677608960	9.69107e+12
OWID_WRL	World	1954	6784.944	2.155	143.159	NA	NA	NA	2.490	NA	100	256613.156	100	0.682	NA	NA	97.787	4037.623	77.612	501.531	2070.391	NA	0.036	1.482	0.028	0.184	0.760	NA	100	100	100	100	100	213051.717	35055.173	6700.207	397.824	1408.235	100	100	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2724847104	9.94678e+12
OWID_WRL	World	1955	7437.038	9.611	652.094	NA	NA	NA	2.682	NA	100	264050.194	100	0.702	NA	NA	108.186	4370.550	96.836	547.454	2314.013	NA	0.039	1.576	0.035	0.197	0.834	NA	100	100	100	100	100	217422.267	37369.185	7247.660	494.659	1516.422	100	100	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2773019904	1.06071e+13
OWID_WRL	World	1956	7917.910	6.466	480.872	NA	NA	NA	2.805	NA	100	271968.104	100	0.713	NA	NA	117.213	4594.487	108.174	587.533	2510.504	NA	0.042	1.628	0.038	0.208	0.889	NA	100	100	100	100	100	222016.754	39879.689	7835.193	602.833	1633.635	100	100	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2822443008	1.11100e+13
OWID_WRL	World	1957	8179.340	3.302	261.429	NA	NA	NA	2.847	NA	100	280147.444	100	0.711	NA	NA	123.183	4678.644	106.386	647.176	2623.951	NA	0.043	1.628	0.037	0.225	0.913	NA	100	100	100	100	100	226695.398	42503.641	8482.369	709.219	1756.818	100	100	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2873306112	1.14910e+13
OWID_WRL	World	1958	8412.113	2.846	232.773	NA	NA	NA	2.875	NA	100	288559.557	100	0.713	NA	NA	130.396	4750.659	95.115	706.630	2729.314	NA	0.045	1.624	0.033	0.242	0.933	NA	100	100	100	100	100	231446.056	45232.955	9188.998	804.333	1887.214	100	100	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2925687040	1.18390e+13
OWID_WRL	World	1959	8848.509	5.188	436.396	NA	NA	NA	2.970	NA	100	297408.066	100	0.719	NA	NA	145.957	4943.832	91.694	759.285	2907.741	NA	0.049	1.659	0.031	0.255	0.976	NA	100	100	100	100	100	236389.888	48140.695	9948.283	896.027	2033.171	100	100	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	2979576064	1.23121e+13
OWID_WRL	World	1960	9334.894	5.497	486.386	NA	NA	NA	3.076	NA	100	306742.960	100	0.724	NA	NA	156.941	5130.917	89.140	835.374	3122.522	NA	0.052	1.691	0.029	0.275	1.029	NA	100	100	100	100	100	241520.806	51263.217	10783.657	985.167	2190.112	100	100	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	3034949888	1.29028e+13
OWID_WRL	World	1961	9356.003	0.226	21.109	NA	NA	NA	3.026	NA	100	316098.963	100	0.698	NA	NA	165.445	4915.325	86.219	882.812	3306.202	NA	0.054	1.590	0.028	0.286	1.069	NA	100	100	100	100	100	246436.130	54569.420	11666.470	1071.386	2355.557	100	100	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	3091844096	1.34101e+13
OWID_WRL	World	1962	9687.509	3.543	331.506	NA	NA	NA	3.075	NA	100	325786.472	100	0.687	NA	NA	177.697	4901.276	85.931	968.788	3553.818	NA	0.056	1.556	0.027	0.308	1.128	NA	100	100	100	100	100	251337.406	58123.238	12635.257	1157.317	2533.254	100	100	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	3150420992	1.41374e+13
OWID_WRL	World	1963	10236.031	5.662	548.522	NA	NA	NA	3.188	NA	100	336022.503	100	0.696	NA	NA	187.210	5101.000	90.506	1049.370	3807.944	NA	0.058	1.589	0.028	0.327	1.186	NA	100	100	100	100	100	256438.406	61931.182	13684.627	1247.823	2720.465	100	100	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	3211001088	1.47203e+13
OWID_WRL	World	1964	10769.609	5.213	533.578	NA	NA	NA	3.289	NA	100	346792.112	100	0.677	NA	NA	205.788	5195.154	112.792	1155.366	4100.509	NA	0.063	1.587	0.034	0.353	1.252	NA	100	100	100	100	100	261633.560	66031.691	14839.993	1360.615	2926.253	100	100	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	3273978112	1.58570e+13
OWID_WRL	World	1965	11269.289	4.640	499.680	NA	NA	NA	3.374	NA	100	358061.401	100	0.671	NA	0.262	215.178	5280.001	130.443	1233.892	4409.775	NA	0.064	1.581	0.039	0.369	1.320	NA	100	100	100	100	100	266913.562	70441.466	16073.885	1491.058	3141.431	100	100	100	100	100	NA	NA	NA	NA	NA	NA	43070.29	12896.90	2.571	3339584000	1.67555e+13
OWID_WRL	World	1966	11793.838	4.655	524.549	NA	NA	NA	3.461	NA	100	369855.239	100	0.666	NA	0.260	230.268	5327.529	144.566	1342.855	4748.620	NA	0.068	1.563	0.042	0.394	1.393	NA	100	100	100	100	100	272241.091	75190.086	17416.740	1635.624	3371.699	100	100	100	100	100	NA	NA	NA	NA	NA	NA	45406.99	13323.95	2.570	3407922944	1.76661e+13
OWID_WRL	World	1967	12171.497	3.202	377.659	NA	NA	NA	3.499	NA	100	382026.736	100	0.661	NA	0.258	238.147	5243.604	190.783	1436.176	5062.786	NA	0.068	1.507	0.055	0.413	1.455	NA	100	100	100	100	100	277484.695	80252.871	18852.917	1826.407	3609.846	100	100	100	100	100	NA	NA	NA	NA	NA	NA	47126.35	13546.84	2.559	3478769920	1.84194e+13
OWID_WRL	World	1968	12835.996	5.459	664.499	NA	NA	NA	3.614	NA	100	394862.732	100	0.658	NA	0.257	254.905	5301.229	203.800	1562.833	5513.230	NA	0.072	1.493	0.057	0.440	1.552	NA	100	100	100	100	100	282785.924	85766.101	20415.749	2030.207	3864.751	100	100	100	100	100	NA	NA	NA	NA	NA	NA	49985.64	14074.12	2.567	3551599104	1.94729e+13
OWID_WRL	World	1969	13691.842	6.668	855.846	NA	NA	NA	3.776	NA	100	408554.574	100	0.665	NA	0.256	268.359	5501.878	244.035	1712.032	5965.538	NA	0.074	1.517	0.067	0.472	1.645	NA	100	100	100	100	100	288287.801	91731.639	22127.782	2274.242	4133.110	100	100	100	100	100	NA	NA	NA	NA	NA	NA	53394.06	14726.63	2.591	3625680896	2.06074e+13
OWID_WRL	World	1970	14826.863	8.290	1135.020	NA	NA	NA	4.007	NA	100	423381.436	100	0.680	NA	0.261	282.282	5680.360	277.745	1794.121	6792.355	NA	0.076	1.535	0.075	0.485	1.836	NA	100	100	100	100	100	293968.161	98523.993	23921.903	2551.987	4415.392	100	100	100	100	100	NA	NA	NA	NA	NA	NA	56709.19	15325.00	2.596	3700436992	2.18429e+13
OWID_WRL	World	1971	15425.831	4.040	598.969	NA	NA	NA	4.085	NA	100	438807.268	100	0.677	NA	0.261	300.044	5681.463	321.128	1936.725	7186.471	NA	0.079	1.505	0.085	0.513	1.903	NA	100	100	100	100	100	299649.624	105710.464	25858.628	2873.115	4715.436	100	100	100	100	100	NA	NA	NA	NA	NA	NA	59090.43	15649.94	2.587	3775759872	2.28411e+13
OWID_WRL	World	1972	16142.605	4.647	716.774	NA	NA	NA	4.191	NA	100	454949.873	100	0.673	NA	0.259	316.962	5675.726	346.570	2055.258	7748.089	NA	0.082	1.474	0.090	0.534	2.012	NA	100	100	100	100	100	305325.351	113458.553	27913.886	3219.685	5032.398	100	100	100	100	100	NA	NA	NA	NA	NA	NA	62273.70	16168.05	2.596	3851651072	2.39881e+13
OWID_WRL	World	1973	17000.713	5.316	858.108	NA	NA	NA	4.328	NA	100	471950.586	100	0.662	NA	0.258	335.999	5837.205	402.006	2136.413	8289.090	NA	0.086	1.486	0.102	0.544	2.110	NA	100	100	100	100	100	311162.556	121747.644	30050.298	3621.691	5368.397	100	100	100	100	100	NA	NA	NA	NA	NA	NA	65857.67	16767.14	2.563	3927781120	2.56992e+13
OWID_WRL	World	1974	16925.872	-0.440	-74.842	NA	NA	NA	4.227	NA	100	488876.457	100	0.634	NA	0.256	336.717	5847.758	391.234	2180.941	8169.223	NA	0.084	1.461	0.098	0.545	2.040	NA	100	100	100	100	100	317010.314	129916.866	32231.239	4012.925	5705.114	100	100	100	100	100	NA	NA	NA	NA	NA	NA	66173.94	16527.81	2.475	4003793920	2.67355e+13
OWID_WRL	World	1975	16902.973	-0.135	-22.899	NA	NA	NA	4.143	NA	100	505779.430	100	0.619	NA	0.254	333.850	5974.763	336.444	2193.919	8063.996	NA	0.082	1.465	0.082	0.538	1.977	NA	100	100	100	100	100	322985.077	137980.862	34425.158	4349.369	6038.964	100	100	100	100	100	NA	NA	NA	NA	NA	NA	66518.52	16305.64	2.439	4079480064	2.72686e+13
OWID_WRL	World	1976	17799.760	5.306	896.788	NA	NA	NA	4.284	NA	100	523579.190	100	0.622	NA	0.254	356.318	6242.261	395.348	2323.262	8482.571	NA	0.086	1.502	0.095	0.559	2.042	NA	100	100	100	100	100	329227.338	146463.434	36748.420	4744.717	6395.282	100	100	100	100	100	NA	NA	NA	NA	NA	NA	70137.94	16881.72	2.449	4154667008	2.86389e+13
OWID_WRL	World	1977	18287.936	2.743	488.176	NA	NA	NA	4.324	NA	100	541867.126	100	0.614	NA	0.252	371.939	6373.924	381.321	2367.022	8793.730	NA	0.088	1.507	0.090	0.560	2.079	NA	100	100	100	100	100	335601.262	155257.164	39115.442	5126.038	6767.220	100	100	100	100	100	NA	NA	NA	NA	NA	NA	72630.75	17172.39	2.441	4229506048	2.97599e+13
OWID_WRL	World	1978	18958.832	3.669	670.896	NA	NA	NA	4.404	NA	100	560825.958	100	0.610	NA	0.252	394.791	6568.674	390.089	2489.867	9115.411	NA	0.092	1.526	0.091	0.578	2.118	NA	100	100	100	100	100	342169.936	164372.575	41605.308	5516.128	7162.011	100	100	100	100	100	NA	NA	NA	NA	NA	NA	75125.75	17452.70	2.415	4304534016	3.11021e+13
OWID_WRL	World	1979	19464.238	2.666	505.406	NA	NA	NA	4.443	NA	100	580290.196	100	0.603	NA	0.250	401.223	6816.022	360.196	2630.153	9256.644	NA	0.092	1.556	0.082	0.600	2.113	NA	100	100	100	100	100	348985.958	173629.219	44235.461	5876.324	7563.234	100	100	100	100	100	NA	NA	NA	NA	NA	NA	77736.77	17746.07	2.404	4380506112	3.23425e+13
OWID_WRL	World	1980	19369.452	-0.487	-94.786	NA	NA	NA	4.345	NA	100	599659.648	100	0.585	NA	0.251	403.947	7017.390	316.485	2708.026	8923.604	NA	0.091	1.574	0.071	0.607	2.002	NA	100	100	100	100	100	356003.348	182552.823	46943.487	6192.809	7967.181	100	100	100	100	100	NA	NA	NA	NA	NA	NA	77171.18	17310.71	2.329	4458002944	3.31401e+13
OWID_WRL	World	1981	18841.367	-2.726	-528.085	NA	NA	NA	4.153	NA	100	618501.015	100	0.564	NA	0.246	403.666	6973.884	237.302	2739.036	8487.479	NA	0.089	1.537	0.052	0.604	1.871	NA	100	100	100	100	100	362977.232	191040.302	49682.523	6430.111	8370.847	100	100	100	100	100	NA	NA	NA	NA	NA	NA	76739.23	16914.10	2.299	4536996864	3.33737e+13
OWID_WRL	World	1982	18700.965	-0.745	-140.402	NA	NA	NA	4.050	NA	100	637201.980	100	0.558	NA	0.245	401.622	7099.887	235.796	2680.506	8283.154	NA	0.087	1.538	0.051	0.581	1.794	NA	100	100	100	100	100	370077.119	199323.456	52363.029	6665.908	8772.468	100	100	100	100	100	NA	NA	NA	NA	NA	NA	76414.32	16549.26	2.279	4617387008	3.35294e+13
OWID_WRL	World	1983	18876.275	0.937	175.310	NA	NA	NA	4.017	NA	100	656078.254	100	0.552	NA	0.243	411.161	7312.340	213.145	2725.148	8214.480	NA	0.087	1.556	0.045	0.580	1.748	NA	100	100	100	100	100	377389.459	207537.936	55088.177	6879.053	9183.629	100	100	100	100	100	NA	NA	NA	NA	NA	NA	77623.87	16517.23	2.272	4699569152	3.41694e+13
OWID_WRL	World	1984	19426.234	2.913	549.959	NA	NA	NA	4.061	NA	100	675504.488	100	0.549	NA	0.239	418.837	7624.128	187.119	2942.506	8253.644	NA	0.088	1.594	0.039	0.615	1.725	NA	100	100	100	100	100	385013.587	215791.580	58030.683	7066.172	9602.467	100	100	100	100	100	NA	NA	NA	NA	NA	NA	81291.51	16992.33	2.298	4784011776	3.53701e+13
OWID_WRL	World	1985	20116.824	3.555	690.590	NA	NA	NA	4.130	NA	100	695621.312	100	0.551	NA	0.241	422.731	8177.460	181.886	3064.540	8270.208	NA	0.087	1.679	0.037	0.629	1.698	NA	100	100	100	100	100	393191.047	224061.788	61095.223	7248.058	10025.197	100	100	100	100	100	NA	NA	NA	NA	NA	NA	83360.79	17113.96	2.287	4870922240	3.64500e+13
OWID_WRL	World	1986	20401.695	1.416	284.870	NA	NA	NA	4.113	NA	100	716023.007	100	0.540	NA	0.239	439.966	8268.885	170.183	3005.575	8517.086	NA	0.089	1.667	0.034	0.606	1.717	NA	100	100	100	100	100	401459.932	232578.874	64100.798	7418.240	10465.163	100	100	100	100	100	NA	NA	NA	NA	NA	NA	85194.75	17174.40	2.252	4960567808	3.78313e+13
OWID_WRL	World	1987	21062.704	3.240	661.009	NA	NA	NA	4.169	NA	100	737085.711	100	0.536	NA	0.239	458.359	8567.808	163.551	3255.566	8617.420	NA	0.091	1.696	0.032	0.644	1.706	NA	100	100	100	100	100	410027.740	241196.294	67356.363	7581.792	10923.522	100	100	100	100	100	NA	NA	NA	NA	NA	NA	88171.10	17450.91	2.243	5052521984	3.93124e+13
OWID_WRL	World	1988	21865.971	3.814	803.267	NA	NA	NA	4.250	NA	100	758951.682	100	0.533	NA	0.239	485.930	8858.933	186.185	3414.657	8920.266	NA	0.094	1.722	0.036	0.664	1.734	NA	100	100	100	100	100	418886.673	250116.560	70771.020	7767.976	11409.453	100	100	100	100	100	NA	NA	NA	NA	NA	NA	91500.97	17782.97	2.234	5145425920	4.09526e+13
OWID_WRL	World	1989	22193.362	1.497	327.391	NA	NA	NA	4.237	NA	100	781145.044	100	0.525	NA	0.238	497.250	8882.261	151.453	3592.678	9069.719	NA	0.095	1.696	0.029	0.686	1.732	NA	100	100	100	100	100	427768.934	259186.278	74363.699	7919.430	11906.703	100	100	100	100	100	NA	NA	NA	NA	NA	NA	93310.19	17815.99	2.205	5237441024	4.23267e+13
OWID_WRL	World	1990	22697.612	2.272	504.250	NA	NA	NA	4.261	NA	100	803842.656	100	0.525	NA	0.240	499.196	8720.617	247.908	3860.862	9218.816	150.213	0.094	1.637	0.047	0.725	1.731	0.028	100	100	100	100	100	436489.551	268405.094	78224.561	8167.337	12405.899	100	100	100	100	100	34967.34	6.564	7415.58	1.392	2460.54	0.462	94383.41	17717.16	2.183	5327230976	4.32400e+13
OWID_WRL	World	1991	23169.529	2.079	471.917	NA	NA	NA	4.279	NA	100	827012.185	100	0.530	NA	0.244	512.831	8639.866	262.648	3939.118	9676.619	138.447	0.095	1.596	0.049	0.728	1.787	0.026	100	100	100	100	100	445129.417	278081.713	82163.679	8429.985	12918.730	100	100	100	100	100	35125.44	6.488	7387.74	1.364	2447.93	0.452	94998.44	17545.88	2.176	5414288896	4.36640e+13
OWID_WRL	World	1992	22444.951	-3.127	-724.578	NA	NA	NA	4.082	NA	100	849457.136	100	0.501	NA	0.235	534.533	8423.192	232.040	3966.600	9160.068	128.518	0.097	1.532	0.042	0.721	1.666	0.023	100	100	100	100	100	453552.609	287241.782	86130.278	8662.025	13453.263	100	100	100	100	100	34985.53	6.362	7322.48	1.332	2440.49	0.444	95643.59	17393.16	2.136	5498919936	4.47760e+13
OWID_WRL	World	1993	22682.603	1.059	237.652	NA	NA	NA	4.064	NA	100	872139.739	100	0.500	NA	0.235	555.314	8522.452	228.386	4074.315	9183.989	118.147	0.099	1.527	0.041	0.730	1.645	0.021	100	100	100	100	100	462075.061	296425.770	90204.593	8890.411	14008.577	100	100	100	100	100	35079.63	6.285	7300.62	1.308	2428.77	0.435	96369.91	17265.65	2.121	5581598208	4.54466e+13
OWID_WRL	World	1994	22843.788	0.711	161.185	NA	NA	NA	4.034	NA	100	894983.527	100	0.491	NA	0.234	591.947	8572.822	231.587	4112.049	9221.056	114.327	0.105	1.514	0.041	0.726	1.628	0.020	100	100	100	100	100	470647.883	305646.826	94316.642	9121.998	14600.524	100	100	100	100	100	35287.61	6.231	7274.33	1.285	2514.99	0.444	97641.65	17241.58	2.100	5663150080	4.65049e+13
OWID_WRL	World	1995	23332.159	2.138	488.371	NA	NA	NA	4.062	NA	100	918315.686	100	0.483	NA	0.234	625.866	8822.304	230.257	4220.642	9314.526	118.564	0.109	1.536	0.040	0.735	1.622	0.021	100	100	100	100	100	479470.188	314961.352	98537.284	9352.255	15226.390	100	100	100	100	100	36012.63	6.269	7283.49	1.268	2570.66	0.448	99620.58	17342.77	2.062	5744212992	4.83222e+13
OWID_WRL	World	1996	24050.844	3.080	718.685	NA	NA	NA	4.129	NA	100	942366.530	100	0.483	NA	0.234	639.119	9035.080	233.118	4406.063	9623.161	114.303	0.110	1.551	0.040	0.756	1.652	0.020	100	100	100	100	100	488505.267	324584.513	102943.347	9585.373	15865.509	100	100	100	100	100	36025.05	6.185	7152.83	1.228	2577.46	0.442	102592.22	17612.72	2.061	5824891904	4.97675e+13
OWID_WRL	World	1997	24191.148	0.583	140.305	NA	NA	NA	4.097	NA	100	966557.678	100	0.471	NA	0.233	658.367	8986.534	238.107	4422.390	9771.633	114.117	0.111	1.522	0.040	0.749	1.655	0.019	100	100	100	100	100	497491.802	334356.147	107365.737	9823.480	16523.875	100	100	100	100	100	37342.50	6.324	7369.05	1.248	2587.71	0.438	103650.06	17552.79	2.018	5905046016	5.13524e+13
OWID_WRL	World	1998	24112.119	-0.327	-79.029	NA	NA	NA	4.029	NA	100	990669.797	100	0.463	NA	0.231	654.507	8749.415	226.832	4495.893	9871.746	113.726	0.109	1.462	0.038	0.751	1.649	0.019	100	100	100	100	100	506241.217	344227.893	111861.629	10050.312	17178.382	100	100	100	100	100	36976.63	6.178	7274.29	1.215	2599.16	0.434	104270.80	17422.62	2.000	5984794112	5.21305e+13
OWID_WRL	World	1999	24431.051	1.323	318.932	NA	NA	NA	4.029	NA	100	1015100.848	100	0.452	NA	0.230	684.409	8679.499	219.234	4628.797	10104.990	114.122	0.113	1.431	0.036	0.763	1.666	0.019	100	100	100	100	100	514920.716	354332.883	116490.427	10269.546	17862.791	100	100	100	100	100	36810.09	6.070	7207.23	1.188	2572.36	0.424	106165.38	17506.79	1.966	6064239104	5.40115e+13
OWID_WRL	World	2000	25119.042	2.816	687.992	NA	NA	NA	4.089	NA	100	1040219.890	100	0.438	NA	0.231	715.796	8999.104	264.501	4745.742	10280.736	113.164	0.117	1.465	0.043	0.772	1.673	0.018	100	100	100	100	100	523919.820	364613.620	121236.168	10534.047	18578.587	100	100	100	100	100	37420.92	6.091	7173.99	1.168	2554.89	0.416	108822.06	17713.38	1.899	6143494144	5.73017e+13
OWID_WRL	World	2001	25332.203	0.849	213.160	NA	NA	NA	4.071	NA	100	1065552.092	100	0.432	NA	0.230	747.540	9048.280	266.260	4791.210	10367.720	111.193	0.120	1.454	0.043	0.770	1.666	0.018	100	100	100	100	100	532968.100	374981.339	126027.378	10800.307	19326.127	100	100	100	100	100	38379.96	6.168	7262.65	1.167	2592.42	0.417	110048.80	17685.26	1.877	6222626816	5.86318e+13
OWID_WRL	World	2002	25911.186	2.286	578.984	NA	NA	NA	4.112	NA	100	1091463.279	100	0.428	NA	0.230	787.887	9437.304	276.993	4936.982	10358.792	113.229	0.125	1.498	0.044	0.783	1.644	0.018	100	100	100	100	100	542405.404	385340.130	130964.360	11077.300	20114.014	100	100	100	100	100	39824.70	6.320	7561.80	1.200	2662.79	0.423	112538.21	17858.18	1.860	6301772800	6.05064e+13
OWID_WRL	World	2003	27176.184	4.882	1264.998	NA	NA	NA	4.259	NA	100	1118639.463	100	0.436	NA	0.233	846.510	10210.176	277.337	5106.119	10621.093	114.949	0.133	1.600	0.043	0.800	1.664	0.018	100	100	100	100	100	552615.580	395961.223	136070.479	11354.637	20960.524	100	100	100	100	100	40678.67	6.375	7586.58	1.189	2676.14	0.419	116667.07	18282.98	1.869	6381185024	6.24252e+13
OWID_WRL	World	2004	28470.451	4.763	1294.267	NA	NA	NA	4.406	NA	100	1147109.914	100	0.431	NA	0.233	905.653	10877.645	301.041	5274.069	10992.582	119.461	0.140	1.684	0.047	0.816	1.701	0.018	100	100	100	100	100	563493.225	406953.806	141344.548	11655.678	21866.177	100	100	100	100	100	42452.22	6.570	7770.13	1.203	2742.89	0.425	122395.79	18943.32	1.855	6461158912	6.59958e+13
OWID_WRL	World	2005	29410.889	3.303	940.438	NA	NA	NA	4.496	NA	100	1176520.803	100	0.416	NA	0.232	957.464	11537.730	321.168	5399.904	11071.852	122.770	0.146	1.764	0.049	0.825	1.692	0.019	100	100	100	100	100	575030.956	418025.658	146744.452	11976.846	22823.641	100	100	100	100	100	43354.52	6.627	7749.03	1.185	2740.70	0.419	126626.72	19356.24	1.790	6541906944	7.07299e+13
OWID_WRL	World	2006	30374.554	3.277	963.665	NA	NA	NA	4.586	NA	100	1206895.357	100	0.404	NA	0.233	1045.562	12194.676	322.802	5524.521	11159.281	127.711	0.158	1.841	0.049	0.834	1.685	0.019	100	100	100	100	100	587225.632	429184.940	152268.973	12299.648	23869.204	100	100	100	100	100	43723.50	6.601	7927.19	1.197	2774.44	0.419	130323.33	19675.85	1.736	6623518208	7.50739e+13
OWID_WRL	World	2007	31293.862	3.027	919.308	NA	NA	NA	4.667	NA	100	1238189.220	100	0.392	NA	0.233	1120.179	12774.790	337.991	5710.415	11221.838	128.649	0.167	1.905	0.050	0.852	1.673	0.019	100	100	100	100	100	600000.422	440406.778	157979.388	12637.639	24989.382	100	100	100	100	100	44584.43	6.648	7945.85	1.185	2822.20	0.421	134465.27	20051.65	1.684	6705947136	7.98616e+13
OWID_WRL	World	2008	31946.034	2.084	652.172	NA	NA	NA	4.705	NA	100	1270135.253	100	0.385	NA	0.235	1136.902	13260.656	351.570	5873.324	11199.617	123.965	0.167	1.953	0.052	0.865	1.650	0.018	100	100	100	100	100	613261.078	451606.395	163852.712	12989.210	26126.284	100	100	100	100	100	44952.27	6.621	8017.25	1.181	2802.99	0.413	136130.34	20051.34	1.641	6789088768	8.29638e+13
OWID_WRL	World	2009	31464.200	-1.508	-481.833	NA	NA	NA	4.578	NA	100	1301599.454	100	0.382	NA	0.234	1168.219	13096.209	342.766	5764.762	10989.512	102.732	0.170	1.906	0.050	0.839	1.599	0.015	100	100	100	100	100	626357.287	462595.907	169617.474	13331.976	27294.503	100	100	100	100	100	44936.19	6.538	8100.53	1.179	2803.03	0.408	134213.76	19528.34	1.629	6872766976	8.23905e+13
OWID_WRL	World	2010	33131.911	5.300	1667.711	NA	NA	NA	4.763	NA	100	1334731.365	100	0.374	NA	0.235	1242.448	13930.110	350.361	6206.111	11288.151	114.729	0.179	2.002	0.050	0.892	1.623	0.016	100	100	100	100	100	640287.397	473884.058	175823.585	13682.337	28536.951	100	100	100	100	100	46552.37	6.692	8170.01	1.174	2860.55	0.411	140722.29	20227.95	1.591	6956824064	8.84541e+13
OWID_WRL	World	2011	34209.583	3.253	1077.672	NA	NA	NA	4.858	NA	100	1368940.948	100	0.364	NA	0.237	1333.891	14748.066	343.432	6372.084	11294.423	117.687	0.189	2.095	0.049	0.905	1.604	0.017	100	100	100	100	100	655035.463	485178.481	182195.670	14025.769	29870.842	100	100	100	100	100	47802.54	6.789	8280.11	1.176	2943.30	0.418	144255.09	20487.31	1.535	7041193984	9.39521e+13
OWID_WRL	World	2012	34760.008	1.609	550.426	NA	NA	NA	4.878	NA	100	1403700.956	100	0.360	NA	0.238	1371.644	14901.935	348.956	6497.149	11523.496	116.828	0.192	2.091	0.049	0.912	1.617	0.016	100	100	100	100	100	669937.398	496701.978	188692.819	14374.725	31242.486	100	100	100	100	100	48348.77	6.785	8374.14	1.175	2970.41	0.417	146252.98	20524.35	1.513	7125828096	9.66492e+13
OWID_WRL	World	2013	34987.264	0.654	227.255	NA	NA	NA	4.852	NA	100	1438688.219	100	0.352	NA	0.235	1427.393	14921.136	349.063	6540.736	11629.569	119.366	0.198	2.069	0.048	0.907	1.613	0.017	100	100	100	100	100	684858.534	508331.547	195233.555	14723.788	32669.879	100	100	100	100	100	48961.60	6.790	8339.46	1.157	2941.00	0.408	149089.93	20676.55	1.502	7210582016	9.92797e+13
OWID_WRL	World	2014	35244.868	0.736	257.605	NA	NA	NA	4.831	NA	100	1473933.087	100	0.346	NA	0.234	1483.543	14944.736	360.754	6590.456	11744.752	120.627	0.203	2.049	0.049	0.903	1.610	0.017	100	100	100	100	100	699803.270	520076.299	201824.011	15084.542	34153.422	100	100	100	100	100	49440.79	6.777	8477.25	1.162	2999.32	0.411	150489.50	20628.31	1.475	7295290880	1.01999e+14
OWID_WRL	World	2015	35209.447	-0.101	-35.422	NA	NA	NA	4.771	NA	100	1509142.534	100	0.335	NA	0.232	1428.375	14624.451	362.053	6762.248	11914.683	117.635	0.194	1.982	0.049	0.916	1.615	0.016	100	100	100	100	100	714427.722	531990.982	208586.259	15446.595	35581.797	100	100	100	100	100	49854.98	6.756	8660.01	1.173	3052.82	0.414	151720.06	20558.84	1.447	7379796992	1.04855e+14
OWID_WRL	World	2016	35220.412	0.031	10.966	NA	NA	NA	4.719	NA	100	1544362.946	100	0.329	NA	0.229	1463.585	14364.382	371.409	6939.908	11969.329	111.800	0.196	1.924	0.050	0.930	1.604	0.015	100	100	100	100	100	728792.104	543960.311	215526.166	15818.004	37045.382	100	100	100	100	100	49358.03	6.613	8550.06	1.146	3054.00	0.409	153848.43	20612.00	1.444	7464022016	1.06561e+14
OWID_WRL	World	2017	35696.349	1.351	475.936	NA	NA	NA	4.729	NA	100	1580059.295	100	NA	NA	NA	1476.475	14413.361	403.984	7114.554	12174.822	113.152	0.196	1.910	0.054	0.943	1.613	0.015	100	100	100	100	100	743205.465	556135.133	222640.721	16221.989	38521.857	100	100	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	7547858944	NA
OWID_WRL	World	2018	36419.712	2.026	723.363	NA	NA	NA	4.773	NA	100	1616479.007	100	NA	NA	NA	1514.612	14618.299	429.496	7489.099	12253.085	115.121	0.198	1.916	0.056	0.981	1.606	0.015	100	100	100	100	100	757823.764	568388.218	230129.820	16651.484	40036.469	100	100	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	7631091200	NA
OWID_WRL	World	2019	36441.388	0.060	21.676	NA	NA	NA	4.724	NA	100	1652920.394	100	NA	NA	NA	1563.761	14362.167	429.496	7615.714	12355.129	115.121	0.203	1.862	0.056	0.987	1.602	0.015	100	100	100	100	100	772185.931	580743.347	237745.534	17080.980	41600.230	100	100	100	100	100	NA	NA	NA	NA	NA	NA	NA	NA	NA	7713467904	NA

4.3.2 Data mapping

OGS.MAP.OWID = function(owid_data, geoscale, geoname.filter){
  owid_data_OGS = owid_data %>% 
    filter(country == geoname.filter) %>% 
    add_column(data.provider = "OWID",
               data.source = "OWID",
               geo.scale = geoscale,
               sector = "All",
               geo.code.iso2c = NA,
               unit = "MtCO₂",
               gas = "CO2") %>% 
    rename(geo.code.iso3c = iso_code,
           geo.name = country,
           value = co2) %>% 
    select(data.source, data.provider, geo.scale, geo.code.iso2c, geo.code.iso3c, geo.name,
           year, sector, gas, value)  
  
  return(owid_data_OGS)
}

owid_data_OGS = OGS.MAP.OWID(owid_data = ghg_emissions_owid,
                             geoscale = "World",
                             geoname.filter = "World")

# plot timeserie
owid_data_OGS %>% 
  # filter(year >= 1990 & year < 2017) %>% 
  plot_ly() %>% 
  add_trace(y = ~value, 
            x = ~year, 
            type = 'scatter',
            mode = 'lines+markers',
            orientation = "v",
            hoverinfo = 'text',
            text = ~paste('</br> Year: ', year,
                          '</br> Value: ', value,
                          '</br> Gas: ', gas,
                          '</br> Sector: ', sector,
                          '</br> Source: ', data.provider)) %>% 
  layout(title = "World GHG emissions (source: WRI - CAIT)",
         yaxis = list(title = "GHG emissions"), 
         xaxis = list(title = "Years"))

4.4 Data sources compilation

OGS_ghg_emission = bind_rows(Worldbank_data_OGS, 
                             wri_data_OGS,
                             owid_data_OGS)

# plot timeserie
OGS_ghg_emission %>% 
  filter(year >= 1990) %>%
  plot_ly() %>% 
  add_trace(y = ~value, 
            x = ~year, 
            color = ~data.provider,
            type = 'scatter',
            # type = "line",
            mode = 'lines+markers',
            orientation = "v",
            hoverinfo = 'text',
            text = ~paste('</br> Year: ', year,
                          '</br> Value: ', value,
                          '</br> Gas: ', gas,
                          '</br> Sector: ', sector,
                          '</br> Source: ', data.provider)) %>% 
  layout(title = "World GHG emissions",
         yaxis = list(title = "GHG emissions (MtCO₂)"), 
         xaxis = list(title = "Years"))

4.5 Conclusion

5 Country scale data exploration: France use case

5.1 European Environmental Agency Data

5.1.1 Data Exploration

• Data used in this article has been downloaded from here: https://www.eea.europa.eu/data-and-maps/data/national-emissions-reported-to-the-unfccc-and-to-the-eu-greenhouse-gas-monitoring-mechanism-16

• EEA GHG emissions data represents national emissions reported to the UNFCCC and to the EU Greenhouse Gas Monitoring Mechanis, sent by countries to UNFCCC and the EU Greenhouse Gas Monitoring Mechanism (EU Member States). This data set reflects the GHG inventory data for 2020 as reported under the United Nations Framework Convention for Climate Change with respect to sector categories described in this section.

ghg_emissions_eea <- readr::read_csv(file = "https://raw.githubusercontent.com/OpenGeoScales/CarbonData/main/datasets/raw/eea/UNFCCC_v23.csv")

The table below describes a complete description of variable definition:

Column name	Description
Country_code	International Country code (ISO 3166-1-Alpha-2 code elements)
Country	country name
Format_name	Name of guideline (e.g., IPCC Common Reporting Format)
Pollutant_name	Short name of pollutant (CO2, CH4, All greenhouse gases - (CO2 equivalent), Unspecified mix of HFCs and PFCs - (CO2 equivalent), HFCs - (CO2 equivalent), SF6 - (CO2 equivalent), N2O, NF3 - (CO2 equivalent), PFCs - (CO2 equivalent))
Sector_code	Sector code (e.g., 1,1.A.1.b, Sectors/Totals_excl…)
Sector_name	Sector name (e.g., Total (without LULUCF),5 - Waste management, 2 - Industrial Processes and Product Use…)
Parent_sector_code	Parent sector code (e.g., 1,4.A, 2.B…)
Unit	Unit of the measure value (Gg CO2 equivalent)
Year	Year (1985-2018)
emissions	Emission value
Notation	Notation key
PublicationDate	Publication Date

5.1.2 Data Mapping

OGS.MAP.EEA = function(eea_data, geoscale, geoname.filter, sector.filter, gas.filter){
  eea_data_OGS = eea_data %>% 
    # recode sectors and gas values
    mutate(sector = fct_collapse(Sector_name, "All" = "Total (with LULUCF)"),
           gas = fct_collapse(Pollutant_name, "All" = "All greenhouse gases - (CO2 equivalent)")) %>% 
    # rename columns
    add_column(data.provider = "EEA",
               geo.scale = geoscale,
               geo.code.iso3c = NA,
               unit = "MtCO₂e") %>% 
    rename(data.source = DataSource,
           geo.code.iso2c = Country_code,
           geo.name = Country,
           value = emissions,
           year = Year) %>% 
    select(data.source, data.provider, geo.scale, geo.code.iso2c, geo.code.iso3c, geo.name,
           year, sector, gas, value) %>% 
    # filter
    filter(geo.name == geoname.filter & sector == sector.filter & gas == gas.filter) %>% 
    mutate_at("value" , as.numeric) %>% 
    mutate_at("year" , as.numeric) %>% 
    mutate(value = value * 0.001) %>% 
    arrange(year)
  
  return(eea_data_OGS)
}


eea_data_OGS_fr = OGS.MAP.EEA(ghg_emissions_eea,
                              geoscale = "Country",
                              geoname.filter = "France",
                              sector.filter = "All",
                              gas.filter = "All")

# plot timeserie

eea_data_OGS_fr %>% 
  plot_ly() %>% 
  add_trace(y = ~value, 
            x = ~year, 
            color = ~data.provider,
            type = 'scatter',
            # type = "line",
            mode = 'lines+markers',
            orientation = "v",
            hoverinfo = 'text',
            text = ~paste('</br> Year: ', year,
                          '</br> Value: ', value,
                          '</br> Gas: ', gas,
                          '</br> Sector: ', sector,
                          '</br> Source: ', data.provider)) %>% 
  layout(title = "France -  GHG emissions (source: EEA)",
         yaxis = list(title = "GHG emissions (MtCO₂)"), 
         xaxis = list(title = "Years"))

5.2 The World Bank

Worldbank_data_OGS_fr = OGS.MAP.WB(Worldbank_data = ghg_emissions_wb,
                                start_date = 1970,
                                end_date = 2012,
                                geoscale = "Country",
                                geoname.filter = "France" )

# plot timeserie
Worldbank_data_OGS_fr %>% 
  plot_ly() %>% 
  add_trace(y = ~value, 
            x = ~year, 
            type = 'scatter',
            mode = 'lines+markers',
            orientation = "v",
            hoverinfo = 'text',
            text = ~paste('</br> Year: ', year,
                          '</br> Value: ', value,
                          '</br> Gas: ', gas,
                          '</br> Sector: ', sector,
                          '</br> Source: ', data.provider)) %>% 
  layout(title = "France -  GHG emissions (source: Wrold Bank)",
         yaxis = list(title = "GHG emissions"), 
         xaxis = list(title = "Years"))

5.3 The World resources Institute - CAIT

wri_CAIT_data_OGS_fr = OGS.MAP.WRI.CAIT(wri_data = ghg_emissions_wri,
                                datasource.filter = "CAIT",
                                geoname.filter = "France",
                                gas.filter = "All",
                                sector.filter = "All",
                                geoscale = "World",
                                start_date = 1990,
                                end_date = 2016)

# plot timeserie
wri_CAIT_data_OGS_fr %>% 
  plot_ly() %>% 
  add_trace(y = ~value, 
            x = ~year, 
            type = 'scatter',
            mode = 'lines+markers',
            orientation = "v",
            hoverinfo = 'text',
            text = ~paste('</br> Year: ', year,
                          '</br> Value: ', value,
                          '</br> Gas: ', gas,
                          '</br> Sector: ', sector,
                          '</br> Source: ', data.provider)) %>% 
  layout(title = "France GHG emissions (source: WRI - CAIT)",
         yaxis = list(title = "GHG emissions"), 
         xaxis = list(title = "Years"))

5.4 The World resources Institute - UNFCCC

OGS.MAP.WRI.UNFCCC = function(wri_data, start_date, end_date, geoscale,sector.filter,gas.filter,geoname.filter,datasource.filter){
  wri_UNFCCC_data_OGS = wri_data %>% 
    filter(Data.source == datasource.filter,
           Country == geoname.filter)%>% 
    # recode sectors and gas values
    mutate(sector = fct_collapse(Sector, "All" = "Total GHG emissions with LULUCF"),
           gas = fct_collapse(Gas, "All" = "Aggregate GHGs")) %>% 
    # filter gas and sector
    filter(sector == sector.filter,
           gas == gas.filter) %>% 
    # pivot data
    pivot_longer(
      cols = starts_with("X"), 
      names_to = "year",
      names_prefix = "X",
      values_to = "value") %>% 
    mutate_at("year" , as.numeric) %>% 
    arrange(year) %>% 
    add_column(data.provider = "WRI.UNFCCC",
               geo.scale = geoscale,
               geo.code.iso3c = NA,
               geo.code.iso2c = NA,
               unit = "MtCO₂e") %>% 
    rename(data.source = Data.source,
           geo.name = Country,
           value = value) %>% 
    select(data.source, data.provider, geo.scale, geo.code.iso2c, geo.code.iso3c, geo.name,
           year, sector, gas, value) %>% 
    filter(year >= start_date & year <= end_date) %>% 
    mutate_at("value" , as.numeric) 
  
  return(wri_UNFCCC_data_OGS)
}


wri_UNFCCC_data_OGS_fr = OGS.MAP.WRI.UNFCCC(wri_data = ghg_emissions_wri,
                                datasource.filter = "UNFCCC_AI",
                                geoname.filter = "France",
                                gas.filter = "All",
                                sector.filter = "All",
                                geoscale = "Country",
                                start_date = 1990,
                                end_date = 2016)

# plot timeserie
wri_UNFCCC_data_OGS_fr %>% 
  plot_ly() %>% 
  add_trace(y = ~value, 
            x = ~year, 
            type = 'scatter',
            mode = 'lines+markers',
            orientation = "v",
            hoverinfo = 'text',
            text = ~paste('</br> Year: ', year,
                          '</br> Value: ', value,
                          '</br> Gas: ', gas,
                          '</br> Sector: ', sector,
                          '</br> Source: ', data.provider)) %>% 
  layout(title = "France GHG emissions (source: WRI - UNFCCC)",
         yaxis = list(title = "GHG emissions"), 
         xaxis = list(title = "Years"))

5.5 Data sources compilation

In this section we will combine the different data sources in order to compare ghg emissions’ values

# combine data sources
OGS_ghg_emission_fr = bind_rows(Worldbank_data_OGS_fr, 
                                wri_CAIT_data_OGS_fr,
                                wri_UNFCCC_data_OGS_fr,
                                eea_data_OGS_fr)

# plot timeserie
OGS_ghg_emission_fr %>% 
  filter(year >= 1990) %>%
  plot_ly() %>% 
  add_trace(y = ~value, 
            x = ~year, 
            color = ~data.provider,
            type = 'scatter',
            # type = "line",
            mode = 'lines+markers',
            orientation = "v",
            hoverinfo = 'text',
            text = ~paste('</br> Year: ', year,
                          '</br> Value: ', value,
                          '</br> Gas: ', gas,
                          '</br> Sector: ', sector,
                          '</br> Source: ', data.provider)) %>% 
  layout(title = "France GHG emissions",
         yaxis = list(title = "GHG emissions (MtCO₂)"), 
         xaxis = list(title = "Years"))

5.6 Conclusion

6 City scale data exploration: France use case

6.1 Introduction

GHG reporting for sub-national territories may refer to multiple geographical divisions such as: municipalities, districts, regions… Different approaches may be undertaken for assessing carbon footprint of territories:

• Spatial inventory of GHG emissions: It refers to national methods used under Kyoto Protocol applied at the territorial level; It covers direct emissions and Scope 2 emissions. Examples of frameworks: OMINEA
• Holistic approach: It consists of assessing all the emissions produced by territory activity, including direct and indirect emissions. Examples of frameworks: Bilan Carbone® Territoire; Global Protocol for Community…
• Consumption approach: It refers to assessing all emissions ,ecessary for inhabitants in a territory, including direct and indirect emissions from importation.

ADEME provides in this article a comprehensive comparison of these different approaches for GHG assessment.

6.2 ADEME - BEGES

6.2.1 Data exploration

6.2.2 Data mapping

6.3 CITEPA - IGT

6.3.1 Data exploration

This spatialized inventory is intended to give relevant orders of magnitude for the year of 2016. It does not as accurate and precise as the inventory work carried out at a regional level by the approaved associations for monitoring air quality (Atmo France), regional energy agencies (RARE), or national work with high spatial and temporal resolutions such as National Spatial Inventory.

The dataset proposed here integrates the effects of all greenhouse gases, detailing emissions by municipality and by sector. If the data are dissiminated to the municipality, they are only representative at a scale aggregated to the EPCI.

citepa_IGT = read.csv(file ="https://raw.githubusercontent.com/OpenGeoScales/CarbonData/main/datasets/raw/citepa/igt/IGT%20-%20Pouvoir%20de%20r%C3%A9chauffement%20global-full.csv",
                                   encoding = "UTF-8")

citepa_IGT_OGS = citepa_IGT %>% 
  mutate(Total_emissions = rowSums(.[3:12], na.rm = TRUE))
  

# plot map of vulnerability index

library(leaflet)
library(RColorBrewer)

pal <- colorQuantile(palette = "RdYlBu", n = 5,
                     domain = citepa_IGT_OGS$Total_emissions,
                     reverse = TRUE)

labels <- sprintf(
  "<strong>%s</strong><br/><strong>%s</strong><br/>",
  paste("Name", citepa_IGT_OGS$Commune, sep=": "), 
  paste("Total emissions", round(citepa_IGT_OGS$Total_emissions,2), sep =": ")
) %>% 
  lapply(htmltools::HTML)

leaflet(data = citepa_IGT_OGS, height = 400, width = "100%") %>% 
  addTiles() %>%
  addProviderTiles(providers$CartoDB.DarkMatter) %>% 
  addCircleMarkers(~lon, ~lat, 
                   radius = 1,
                   color = ~pal(Total_emissions),
                   stroke = FALSE,
                   fillOpacity = 0.7,
                   weight = 100,
                   popup = ~as.character(Commune), 
                   label = labels) %>% 
  addLegend(pal = pal, values = ~Total_emissions, opacity = 0.9, 
            title = "Total emissions",
            position = "bottomright",
            labFormat = labelFormat(suffix = " "))

6.3.2 Data mapping

6.4 Data sources compilation

6.5 Conclusion

7 Conclusion

8 References

• IPCC, 2013. Climate change 2013: The physical science basis. Working Group I contribution to the IPCC Fifth Assessment Report. Cambridge, United Kingdom: Cambridge University Press. www.ipcc.ch/report/ar5/wg1.