Frequently Asked Questions

Air Pollution Data: The WHO Air Quality Database is a comprehensive resource that collects and presents data related to air quality from countries worldwide. Managed by the World Health Organization (WHO), the database primarily gathers information about concentrations of air pollutants, specifically those with a significant impact on health.

Climate Change Data: The GISS Surface Temperature Analysis (v4) from NASA’s Goddard Institute for Space Studies is a key resource for understanding climate change. This analysis presents data on global surface temperature anomalies, a significant metric for monitoring global warming trends.

Plastic Pollution Data: Our World in Data is a scientific online publication that focuses on large global problems such as poverty, disease, hunger, climate change, war, existential risks, and inequality. The ‘Extrapolated Change in Plastic Fate’ from Our World in Data is a dataset that projects the future of global plastic waste. It provides crucial insights into one of the major global pollution and waste management problems.

The Organisation for Economic Co-operation and Development (OECD) iLibrary (‘Plastic Waste by End-of-Life Fate – Projections,’ ‘Plastics Use by Polymer – Projections,’ and ‘Plastics Use by Type – Projections’) provide future projections for different aspects of global plastic waste, use, and disposal. These datasets provide crucial insights into future trends in plastic pollution.

Sustainable Transportation Data: The IEA was established in 1974 within the framework of the Organisation for Economic Co-operation and Development (OECD), with a mandate to promote energy security among its member countries.  The ‘Global EV Outlook 2021 – Analysis’ by the International Energy Agency (IEA) is a comprehensive report providing a range of data about electric vehicles worldwide. This is a key element of the transition towards more sustainable transportation methods.

Energy Production Data: The datasets from the International – U.S. Energy Information Administration (EIA) provide extensive data on worldwide energy production. This information is central to understanding progress towards more sustainable forms of energy production.

Greenhouse Gas Emissions Data: The ‘CO₂ and Greenhouse Gas Emissions’ article by Hannah Ritchie and others on Our World in Data provides comprehensive data on the sources and impacts of global greenhouse gas emissions, a critical aspect of climate change.

Biodiversity Data: ‘The IUCN Red List of Threatened Species’ from the International Union for Conservation of Nature provides data on the conservation status of different species around the world. This data is vital for understanding biodiversity loss, a key aspect of environmental sustainability.

Deforestation Data: The Global Forest Watch website provides data on deforestation rates and statistics by country. This is an essential resource for monitoring the loss of forests, which play a critical role in mitigating climate change and preserving biodiversity.

Agriculture Data: The Food and Agriculture Organization (FAO) is a specialized agency of the United Nations that leads international efforts to defeat hunger. Established on October 16, 1945, the FAO aims to ensure that people have regular access to enough high-quality food to lead active, healthy lives.

The real-time counter provides an essential tool to track and visualize various aspects of environmental data. This counter is updated semi-annually (July/January), reflecting the most recent data from various reputable sources such as the WHO, NASA’s Goddard Institute for Space Studies, OECD, and others.

However, it’s crucial to note that while the counter aims to provide the most up-to-date and accurate representation of the current state of global sustainability, the precise timing of the updates can be challenging to determine. This is due to the fact that the counter relies on external data sources, each with their individual updating schedules.

Scientific data collection and processing often involve rigorous procedures, from data gathering and validation to analysis and interpretation. This complex process means that the release schedules of new data or reports from these sources are subject to their own internal timelines, which can be influenced by a variety of factors such as the nature of the data, the complexity of the analysis, or even logistical considerations.

Furthermore, each data source may not specify or commit to a precise date for their next update, making it difficult to predict when new information will be available for inclusion in the real-time counter. Nevertheless, every effort is made to incorporate the most recent data as soon as it becomes available.

In conclusion, while the counter is updated every six months, the precise content of each update is subject to the availability of new data from the various sources used. Therefore, while the exact date of future updates may be challenging to predict, users can be assured that the counter will consistently provide the most recent, scientifically valid data available on environmental sustainability.

Our Real-Time Sustainability Counter relies on accurate, up-to-date data to keep you informed about key environmental metrics. To ensure data quality, we use a comprehensive verification process:

Data Sources: We only use data from reputable and trusted sources such as the World Health Organization (WHO), the International Energy Agency (IEA), the Organisation for Economic Co-operation and Development (OECD), the International Union for Conservation of Nature (IUCN), and NASA’s Goddard Institute for Space Studies, among others. These organizations are known for their rigorous data collection and validation methods.

Data Collection: We gather the necessary data from these sources, ensuring that we select the most relevant and recent information for our counter.

Data Cleaning: We standardize and clean the data, ensuring that all values are in the correct format, consistent, and free from errors. This step is crucial for maintaining the integrity of our counter.

Data Verification: We cross-reference the collected data with other credible sources to ensure its accuracy. Any significant discrepancies are thoroughly investigated to maintain the reliability of our counter.

Data Visualization: We then present this data in our real-time counter in a clear and easily understandable format. Our goal is to make complex data accessible and actionable.

Data Interpretation: We provide context and interpretation for the data displayed on our counter, helping you understand the significance of these numbers and their implications for global sustainability.

Regular Updates: Lastly, we are committed to regularly updating our counter.

For a real-time sustainability counter that considers data related to air quality, global temperature, plastic pollution, global energy outlook, and various other sustainability parameters, a range of reputable sources could be used for cross-referencing purposes. These may include:

United Nations Statistics Division (UNSD): This is a comprehensive source for global statistics, covering a broad range of topics including the environment, economy, and social issues.

World Meteorological Organization (WMO): This could be used as a reference for data related to atmospheric conditions, air quality, and global temperature.

United Nations Environment Programme (UNEP): The UNEP provides extensive data on various environmental aspects including pollution, climate change, and natural resources.

National Aeronautics and Space Administration (NASA) Earth Data: For data related to the Earth’s temperature, climate change, air quality, and other related parameters, NASA’s Earth Data could serve as an excellent reference.

European Environment Agency (EEA): The EEA provides a wide range of data on various environmental issues in Europe which could be used for cross-validation of similar data for the region.

World Bank Open Data: The World Bank provides free and open access to data about development in countries around the globe. The World Bank’s data on energy and environment could be a valuable cross-reference source.

Global Forest Watch: For data related to deforestation, Global Forest Watch could be used as a cross-reference.

Centers for Disease Control and Prevention (CDC): The CDC’s Air Quality Index could be used as a reference for air quality data.

Remember, the exact cross-reference sources would depend on the specific data points that the sustainability counter is tracking, and these sources would need to be assessed for their reliability and relevance to the specific data being cross-referenced.

The calculation process is divided into two primary phases:

Historical Growth Analysis: We start with data collected over several years from reliable and trusted sources. This data is then processed to determine the growth rate over the specified period. We utilize the Compound Annual Growth Rate (CAGR), a popular method for calculating an average growth rate over a continuous period.
The CAGR is used to calculate the rate at which the particular metric has increased on an annual basis. This growth rate is then converted into a per-second increase that allows our counter to display real-time increments.

Future Projections: We use the historical data and the growth trends to estimate future values. This process, known as extrapolation, allows us to predict the metric’s value for the upcoming years. This prediction is based on the trend and growth rate identified in the historical data.

Please bear in mind, these values are estimates that rely on historical trends. Actual future values can vary due to numerous factors. We ensure to regularly update our data and recalibrate our calculations to accommodate new data and trends.

Creating a real-time sustainability counter based on data from various sources requires making certain assumptions during the data collection and calculation process. Here are some of these assumptions:

Consistency and Accuracy of Data Sources: One primary assumption is that the data sources are reliable, consistent, and accurate in their data reporting. The counter assumes that the data collected from these sources accurately reflects the state of the phenomena being measured.

Temporal Consistency: The counter assumes that data collected from different years is comparable, and that there haven’t been significant changes in the method of data collection or reporting over time.

Uniform Distribution of Data Over Time: For a real-time counter, we usually assume that the change in the value of the metrics is evenly distributed over time. That is, if we know the total change in a year, we assume that this change happens at a constant rate every second.

Sustainability of Historical Trends: When projecting future values, it is often assumed that the historical trends will continue at the same rate. This is a basic assumption in any forecasting that uses historical data, but it’s important to remember that actual future rates may differ due to a multitude of factors.

Stability of the Compound Annual Growth Rate (CAGR): We assume that the CAGR, based on historical data, will remain consistent when predicting future values.

Absence of Outliers: The calculations assume that there are no significant outliers in the data that would disproportionately affect the average or projected values.

Independence of Data Points: The data points are assumed to be independent of each other unless explicitly stated otherwise.

It’s essential to note that while these assumptions help in modeling and computation, real-world conditions can vary, and hence, periodic verification and recalibration of the models and counters may be necessary.

Uncertainty is an inherent part of any scientific measurement or prediction, including environmental metrics. It refers to the range or extent to which the exact value of a measurement or prediction might vary. The uncertainty could be due to variations in data, inherent randomness in natural processes, measurement errors, limitations in our understanding, or other factors.

Given the nature of environmental studies where there are many variables and complex systems involved, acknowledging and quantifying uncertainty becomes even more critical. It’s a way of being transparent about the fact that our understanding or measurement is not perfectly precise, and there is a range of possible true values.

Different methods can be used to quantify and represent uncertainty, depending on the situation:

Error Bars: Error bars are graphical representations used in data plots to indicate the variability of data. They visually show the range within which the actual value is expected to lie with a certain level of confidence. They can be used to represent the standard deviation, standard error, or confidence interval of a measurement.

Confidence Intervals: Confidence intervals are a range of values that are likely to contain the true value of an unknown population parameter. The confidence level represents the frequency (i.e., the proportion) of possible confidence intervals that contain the true value of the unknown population parameter. For example, a 95% confidence interval means that 95% of the confidence intervals calculated from many random samples will contain the true value.

Other Measures: Other statistical measures like variance, standard deviation, or Bayesian credible intervals might also be used to quantify uncertainty, depending on the context and nature of the data.

In conclusion, when it comes to environmental metrics, acknowledging uncertainty does not undermine the findings. Instead, it improves transparency and honesty in the data representation, leading to better decision-making. It also helps identify areas where more data or improved methodologies could enhance our understanding.

Billion Tons (Bt): This is a measure of mass, often used when talking about emissions of greenhouse gases or other large-scale environmental phenomena.

Million Tons (Mt): This is also a measure of mass, used for somewhat smaller quantities than Billion Tons. It might be used for things like the quantity of plastic waste generated each year.

Million Hectares (Mha): This is a unit of area, often used when talking about land use changes like deforestation or reforestation. One hectare is equal to 10,000 square meters or about 2.47 acres.

Species: This unit is used in the context of biodiversity and extinction. It refers to the number of distinct species in a certain context.

Tons (t): This is a common unit of mass, used across many contexts.

Celsius (°C): This unit of temperature is often used in the context of climate change to describe increases in global average temperature.

Fine particulate matter that are 2.5 microns or less in diameter (PM2.5): PM2.5 refers to tiny particles or droplets in the air that are two and one half microns or less in width. They are used as a measure of air pollution, as these tiny particles can be inhaled deep into the lungs and cause health problems.

1 quadrillion British thermal units (Quad BTU): A British thermal unit (BTU) is a measure of the heat content of fuels or energy sources. It is the quantity of heat required to raise the temperature of one pound of liquid water by 1 degree Fahrenheit at the temperature at which water has its greatest density (approximately 39 degrees Fahrenheit). A Quad BTU is one quadrillion (a thousand trillion) BTUs and is used in discussing global-scale energy use.

Cubic meters (m3): This is a measure of volume, often used when discussing water use, reservoir capacity, or similar topics in environmental science.

In our pursuit of transparency and inclusivity, we have prioritized data accessibility in the creation of our real-time sustainability counter. This means that we have taken measures to ensure that the data we present is not only easy to access but also easy to understand.

Easy Access: All the data used in our metrics is sourced from publicly available databases and reports. We have provided direct links to our sources, so anyone interested in diving deeper into the data can easily do so.

Understandability: We have presented the data in a manner that is intuitive and easy to comprehend, regardless of the user’s background in the subject matter. This includes using clear and simple language, providing explanations for technical terms, and giving context to the numbers.

Interoperability: Our data is compatible with different platforms and devices, ensuring that users can access and interact with it seamlessly, whether they’re on a desktop computer or a mobile device.

We believe that making our data accessible in this way is crucial for fostering an informed and engaged public.