What a Waste is a global project to aggregate data on solid waste management from around the world. This database features the statistics collected through the effort, covering nearly all countries and over 330 cities. The metrics included cover all steps from the waste management value chain, including waste generation, composition, collection, and disposal, as well as information on user fees and financing, the informal sector, administrative structures, public communication, and legal information. The information presented is the best available based on a study of current literature and limited conversations with waste agencies and authorities. While there may be variations in the definitions and quality of reporting for individual data points, general trends should reflect the global reality. All sources and any estimations are noted.

This dataset shows the increase of global plastic production, measured in tonnes per year, from 1950 through to 2019.In 1950 the world produced only 2 million tonnes per year. Since then, annual production has increased nearly 200-fold, reaching 381 million tonnes in 2015. The short downturn in annual production in 2009 and 2010 was predominantly the result of the 2008 global financial crisis — a similar dent is seen across several metrics of resource production and consumption, including energy.

Description:

There is growing recognition that human-provided food resources are becoming increasingly available to animals across the globe (Oro et al., 2013). The food resources that are wasted by humans have influenced predators’ ecology and behavior and can indirectly affect their co-occurring species, leading to mostly negative ecological effects (Newsome et al., 2014). However, large increases have been found in the abundances of terrestrial mammalian predators such as coyotes (Canis latrans), cats (Felis catus) and red foxes (Vulpes vulpes), which are associated with their access to waste foods provided by humans (Denny et al., 2002; Fedriani et al., 2001; Shapira et al., 2008). Therefore, under anthropogenic global changes where human activities are continually expanding, a spatially explicit data for waste foods is essential to assessing the ecological effects of anthropogenic food subsidies to species occurrences and abundances.

The repository contains a global dataset consisting of four different variables to depict anthropogenic food waste index: household food waste (tons/year), food service food waste (tons/year), retail food waste (tons/year), and total human-provided food waste (tons/year). To produce the dataset, I first allocated the food waste estimates (kg/capita/year) to 30 arc-second grid cells for each county. The food waste estimates for 2021 were generated by normalizing different food waste measurements to a single metric (i.e., kg/capita/year), accounting for known biases or different scopes of measurement, and aggregating a series of studies or observations if multiple observations existed in a geographic entity of interest (United Nations Environment Programme 2021). The food waste estimates were then multiplied by the estimated population count for 2021 produced by Sims et al. 2022. The data files were produced as global rasters at 30 arc-second (~1km at the equator) resolution in geotiff format under WGS 84 geographical coordinate system.

Keywords: Anthropogenic food subsidies, human-provided food wastes, household food waste, food service food waste, retail food waste, food availability, anthropogenic global changes, human activities

Reference:

United Nations Environment Programme (2021). Food Waste Index Report 2021. Nairobi.

Denny, E., Yaklovlevich, P., Eldridge, M.D.B. & Dickman, C.R. (2002) Social and genetic analysis of a population of free-living cats (Felis catus L.) exploiting a resource-rich habitat. Wildlife Research, 29, 405–413.

Fedriani, J.M., Fuller, T.K. & Sauvajot, R.M. (2001) Does availability of anthropogenic food enhance densities of omnivorous mammals? An example with coyotes in southern California. Ecography, 24, 325–331.

Newsome, T. M., Dellinger, J. A., Pavey, C. R., Ripple, W. J., Shores, C. R., Wirsing, A. J., & Dickman, C. R. (2015). The ecological effects of providing resource subsidies to predators. Global Ecology and Biogeography, 24, 1-11.

Oro, D., Genovart, M., Tavecchia, G., Fowler, M. S., & Martínez‐Abraín, A. (2013). Ecological and evolutionary implications of food subsidies from humans. Ecology letters, 16(12), 1501-1514.

Shapira, I., Sultan, H. & Shanas, U. (2008) Agricultural farming alters predator–prey interactions in nearby natural habitats. Animal Conservation, 11, 1–8.

Sims, K., Reith, A., Bright, E., McKee, J., & Rose, A. (2022). LandScan Global 2021 [Data set]. Oak Ridge National Laboratory. https://doi.org/10.48690/1527702.

Analysis of ‘Global Plastic Pollution’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/sohamgade/plastic-datasets on 12 November 2021.

--- Dataset description provided by original source is as follows ---

Context

More and more plastic is being generated every year and more of which is getting dumped into the ocean or is mishandled.

The focus of this study was to understand how much plastic a country produces over a year's period and how much of that plastic is mismanaged.

Content

Datasets contains: - Global plastic production from year 1950 to 2015. - Along with this, it contains waste generated per person (per day/kg) & mismanaged waste per person (per day/kg) for the year 2010.

Acknowledgements

This dataset was gathered from Our World in Data from their article Plastic Pollution.

Inspiration

When are we going to be aware of the amount of plastic that we use?

--- Original source retains full ownership of the source dataset ---

United States US: Municipal Waste Generated: Kg per Capita data was reported at 810.850 kg in 2018. This records an increase from the previous number of 749.730 kg for 2017. United States US: Municipal Waste Generated: Kg per Capita data is updated yearly, averaging 752.980 kg from Dec 1990 (Median) to 2018, with 29 observations. The data reached an all-time high of 810.850 kg in 2018 and a record low of 725.380 kg in 2009. United States US: Municipal Waste Generated: Kg per Capita data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s United States – Table US.OECD.GGI: Environmental: Waste Management: OECD Member: Annual.

Global Lagrangian dataset of Marine litter

This dataset regroups 12 yearly files (global-marine-litter-[2010–2021].nc) combining monthly releases of 32,300 particles initially distributed across the globe following global Mismanaged Plastic Waste (MPW) inputs. The particles are advected with OceanParcels (Delandmeter, P and E van Sebille, 2019) using ocean surface velocity, a wind drag coefficient of 1%, and a small random walk component with a uniform horizontal turbulent diffusion coefficient of K_h = 1m²s^-1 representing unresolved turbulent motions in the ocean (see Chassignet et al. 2021 for more details).

Global oceanic current and atmospheric wind

Ocean surface velocities are obtained from GOFS3.1, a global ocean reanalysis based on the HYbrid Coordinate Ocean Model (HYCOM) and the Navy Coupled Ocean Data Assimilation (NCODA; Chassignet et al., 2009; Metzger et al., 2014). NCODA uses a three-dimensional (3D) variational scheme and assimilates satellite and altimeter observations as well as in-situ temperature and salinity measurements from moored buoys, Expendable Bathythermographs (XBTs), Argo floats (Cummings and Smedstad, 2013). Surface information is projected downward into the water column using Improved Synthetic Ocean Profiles (Helber et al., 2013). The horizontal resolution and the temporal frequency for the GOF3.1 outputs are 1/12° (8 km at the equator, 6 km at mid-latitudes) and 3-hourly, respectively. Details on the validation of the ocean circulation model are available in Metzger et al. (2017).

Wind velocities are obtained from JRA55, the Japanese 55-year atmospheric reanalysis. The JRA55, which spans from 1958 to the present, is the longest third-generation reanalysis that uses the full observing system and a 4D advanced data assimilation variational scheme. The horizontal resolution of JRA55 is about 55 km and the temporal frequency is 3-hourly (see Tsujino et al. (2018) for more details).

Marine Litter Sources

The marine litter sources are obtained by combining MPW direct inputs from coastal regions, which are defined as areas within 50 km of the coastline (Lebreton and Andrady 2019), and indirect inputs from inland regions via rivers (Lebreton et al. 2017).

File Format

The locations (lon, lat), the corresponding weight (tons), and the source (1: land, 0: river) associated with the 32,300 particles are described in the file initial-location-global.csv. The particle trajectories are regrouped into yearly files (marine-litter-[2010–2021].nc) which contain 12 monthly releases, resulting in a total of 387,600 trajectories per file. More precisely, in each of the yearly files, the first 32,300 lines contain the trajectories of particles released on January 1st, then lines 32,301–64,600 contain the trajectories of particles released on February 1st, and so on. The trajectories are recorded daily and are advected from their release until 2021-12-31, resulting in longer time series for earlier years of the dataset.

References

Chassignet, E. P., Hurlburt, H. E., Metzger, E. J., Smedstad, O. M., Cummings, J., Halliwell, G. R., et al. (2009). U.S. GODAE: global ocean prediction with the hybrid coordinate ocean model (HYCOM). Oceanography 22, 64–75. doi: 10.5670/oceanog.2009.39

Chassignet, E. P., Xu, X., and Zavala-Romero, O. (2021). Tracking Marine Litter With a Global Ocean Model: Where Does It Go? Where Does It Come From?. Frontiers in Marine Science, 8, 414, doi: 10.3389/fmars.2021.667591

Cummings, J. A., and Smedstad, O. M. (2013). “Chapter 13: variational data assimilation for the global ocean”, in Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications, Vol. II, eds S. Park and L. Xu (Berlin: Springer), 303–343. doi: 10.1007/978-3-642-35088-7_13

Delandmeter, P., and van Sebille, E. (2019). The Parcels v2.0 Lagrangian framework: new field interpolation schemes. Geosci. Model Dev. 12, 3571–3584. doi: 10.5194/gmd-12-3571-2019

Helber, R. W., Townsend, T. L., Barron, C. N., Dastugue, J. M., and Carnes, M. R. (2013). Validation Test Report for the Improved Synthetic Ocean Profile (ISOP) System, Part I: Synthetic Profile Methods and Algorithm. NRL Memo. Report, NRL/MR/7320—13-9364 Hancock, MS: Stennis Space Center.

Metzger, E. J., Smedstad, O. M., Thoppil, P. G., Hurlburt, H. E., Cummings, J. A., Wallcraft, A. J., et al. (2014). US Navy operational global ocean and Arctic ice prediction systems. Oceanography 27, 32–43, doi: 10.5670/oceanog.2014.66.

Metzger, E., Helber, R. W., Hogan, P. J., Posey, P. G., Thoppil, P. G., Townsend, T. L., et al. (2017). Global Ocean Forecast System 3.1 validation test. Technical Report. NRL/MR/7320–17-9722. Hancock, MS: Stennis Space Center, 61.

Lebreton, L., and Andrady, A. (2019). Future scenarios of global plastic waste generation and disposal. Palgrave Commun. 5:6, doi: 10.1057/s41599-018-0212-7.

Lebreton, L., van der Zwet, J., Damsteeg, J. W., Slat, B., Andrady, A., and Reisser, J. (2017). River plastic emissions to the world’s oceans. Nat. Commun. 8:15611, doi: 10.1038/ncomms15611.

Tsujino H., S. Urakawa, H. Nakano, R.J. Small, W.M. Kim, S.G. Yeager, G. Danabasoglu, T. Suzuki, J.L. Bamber, M. Bentsen, C. Böning, A. Bozec, E.P. Chassignet, E. Curchitser, F. Boeira Dias, P.J. Durack, S.M. Griffies, Y. Harada, M. Ilicak, S.A. Josey, C. Kobayashi, S. Kobayashi, Y. Komuro, W.G. Large, J. Le Sommer, S.J. Marsland, S. Masina, M. Scheinert, H. Tomita, M. Valdivieso, and D. Yamazaki, 2018. JRA-55 based surface dataset for driving ocean-sea-ice models (JRA55-do). Ocean Modelling, 130, 79-139, doi: 10.1016/j.ocemod.2018.07.002.

Global Municipal Waste Generated Per Capita by Country, 2023 Discover more data with ReportLinker!

Overview

The goal of this project was to create a structured dataset which can be used to train computer vision models to detect electronic waste devices, i.e., e-waste or Waste Electrical and Electronic Equipment (WEEE). Due to the often-subjective differences between e-waste and functioning electronic devices, a model trained on this dataset could also be used to detect electronic devices in general. However, it must be noted that for the purposes of e-waste recognition, this dataset does not differentiate between different brands or models of the same type of electronic devices, e.g. smartphones, and it also includes images of damaged equipment.

The structure of this dataset is based on the UNU-KEYS classification Wang et al., 2012, Forti et al., 2018. Each class in this dataset has a tag containing its corresponding UNU-KEY. This dataset structure has the following benefits: 1. It allows the user to easily classify e-waste devices regardless of which e-waste definition their country or organization uses, thanks to the correlation between the UNU-KEYS and other classifications such as the HS-codes or the EU-6 categories, defined in the WEEE directive; 2. It helps dataset contributors focus on adding e-waste devices with higher priority compared to arbitrarily chosen devices. This is because electronic devices in the same UNU-KEY category have similar function, average weight and life-time distribution as well as comparable material composition, both in terms of hazardous substances and valuable materials, and related end-of-life attributes Forti et al., 2018. 3. It gives dataset contributors a clear goal of which electronic devices still need to be added and a clear understanding of their progress in the seemingly endless task of creating an e-waste dataset.

This dataset contains annotated images of e-waste from every UNU-KEY category. According to Forti et al., 2018, there are a total of 54 UNU-KEY e-waste categories.

Description of Classes

At the time of writing, 22. Apr. 2024, the dataset has 19613 annotated images and 77 classes. The dataset has mixed bounding-box and polygon annotations. Each class of the dataset represents one type of electronic device. Different models of the same type of device belong to the same class. For example, different brands of smartphones are labelled as "Smartphone", regardless of their make or model. Many classes can belong to the same UNU-KEY category and therefore have the same tag. For example, the classes "Smartphone" and "Bar-Phone" both belong to the UNU-KEY category "0306 - Mobile Phones". The images in the dataset are anonymized, meaning that no people were annotated and images containing visible faces were removed.

The dataset was almost entirely built by cloning annotated images from the following open-source Roboflow datasets: [1]-[91]. Some of the images in the dataset were acquired from the Wikimedia Commons website. Those images were chosen to have an unrestrictive license, i.e., they belong to the public domain. They were manually annotated and added to the dataset.

Cite This Project

This work was done as part of the PhD of Dimitar Iliev, student at the Faculty of German Engineering and Industrial Management at the Technical University of Sofia, Bulgaria and in collaboration with the Faculty of Computer Science at Otto-von-Guericke-University Magdeburg, Germany.

If you use this dataset in a research paper, please cite it using the following BibTeX: @article{iliev2024EwasteDataset, author = "Iliev, Dimitar and Marinov, Marin and Ortmeier, Frank", title = "A proposal for a new e-waste image dataset based on the unu-keys classification", journal = "XXIII-rd International Symposium on Electrical Apparatus and Technologies SIELA 2024", year = 2024, volume = "23", number = "to appear", pages = {to appear} note = {under submission} }

Contribution Guidelines

Image Collection

1. Choose a specific electronic device type to add to the dataset and find its corresponding UNU-KEY. * The chosen type of device should have a characteristic design which an object detection model can learn. For example, CRT monitors look distinctly different than flat panel monitors and should therefore belong to a different class, regardless that they are both monitors. In contrast, LED monitors and LCD monitors look very similar and are therefore both labelled as Flat-Panel-Monitor in this dataset.
2. Collect images of this type of device. * Take note of the license of those images and their author/s to avoid copyright infringement. * Do not collect images with visible faces to protect personal data and comply w

China Municipal Waste Generated: Kg per Capita data was reported at 151.450 kg in 2017. This records an increase from the previous number of 144.000 kg for 2016. China Municipal Waste Generated: Kg per Capita data is updated yearly, averaging 111.920 kg from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 151.450 kg in 2017 and a record low of 57.500 kg in 1990. China Municipal Waste Generated: Kg per Capita data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s China – Table CN.OECD.GGI: Environmental: Waste Management: Non OECD Member: Annual.

Plastics in the marine environment have become a major concern because of their persistence at sea, and adverse consequences to marine life and potentially human health. Implementing mitigation strategies requires an understanding and quantification of marine plastic sources, taking spatial and temporal variability into account. Here we present a global model of plastic inputs from rivers into oceans based on waste management, population density and hydrological information. Our model is calibrated against measurements available in the literature. We estimate that between 1.15 and 2.41 million tonnes of plastic waste currently enters the ocean every year from rivers, with over 74% of emissions occurring between May and October. The top 20 polluting rivers, mostly located in Asia, account for 67% of the global total. The findings of this study provide baseline data for ocean plastic mass balance exercises, and assist in prioritizing future plastic debris monitoring and mitigation strategies.Modelled plastic inputs into the ocean from rivers worldwide.The compressed .zip folder is a shapefile containing 40,760 river input locations (EPSG:4326, WGS 84) with the following attributes:i_level: plastic input in tonnes per year. (level=low, mid or high for lower, midpoint and upper estimates)i_level_month: plastic input in tonnes per month. (level=low,mid,high; month=jan,feb,...,dec)runoff_month: monthly averaged runoff in catchment in mm per day. (month=jan,feb,...,dec)mpw: mismanaged plastic waste production in catchment in kg per year. area: catchment area in square meters.## Added .csv file with XY locations for river outfalls and attributes above ##

Waste disposal is a global challenge, especially in densely populated areas. Efficient waste segregation is critical for separating recyclable from non-recyclable materials. While developed countries have established and refined effective waste segmentation and recycling systems, our country still uses manual segregation to identify and process recyclable items. This study presents a dataset intended to improve automatic waste segmentation systems. The dataset consists of 784 images that have been manually annotated for waste classification. These images were primarily taken in and around Jadavpur University, including streets, parks, and lawns. Annotations were created with the Labelme program and are available in color annotation formats. The dataset includes 14 waste categories: plastic containers, plastic bottles, thermocol, metal bottles, plastic cardboard, glass, thermocol plates, plastic, paper, plastic cups, paper cups, aluminum foil, cloth, and nylon. The dataset includes a total of 2350 object segments.Other Information:Published in: Mendely DataLicense: http://creativecommons.org/licenses/by/4.0/See dataset on publisher's website: https://data.mendeley.com/datasets/gr99ny6b8p/1

By Hannah Ritchie, Veronika Samborska and Max Roser:

Plastic has added much value to our lives: it’s a cheap, versatile, and sterile material used in various applications, including construction, home appliances, medical instruments, and food packaging.

However, when plastic waste is mismanaged – not recycled, incinerated, or kept in sealed landfills – it becomes an environmental pollutant. One to two million tonnes of plastic enter our oceans yearly, affecting wildlife and ecosystems.

There are 4 dataset here:

1- Plastic production has more than doubled in the last two decades

The first synthetic plastic – Bakelite – was produced in 1907, marking the beginning of the global plastics industry.

However, rapid growth in global plastic production didn’t happen until the 1950s. Over the next 70 years, however, annual production of plastics has increased nearly 230-fold to 460 million tonnes in 2019.

Even just in the last two decades, global plastic production has doubled.

2- Most ocean plastics today come from middle-income countries

Rich countries tend to produce the most plastic waste per person.

But what’s most important for plastic pollution is how much of this waste is mismanaged, meaning it is not recycled, incinerated, or kept in sealed landfills. Mismanagement means it’s at risk of leaking to the environment.

3- Only a small share of plastic gets recycled

While we might think that much of the world’s plastic waste is recycled, only 9% is.

Half of the world’s plastic still goes straight to landfill. Another fifth is mismanaged – meaning it is not recycled, incinerated, or kept in sealed landfills – putting it at risk of being leaked into rivers, lakes, and the ocean.

4- Better waste management is key to ending plastic pollution

Improving waste management strategies is crucial to ending plastic pollution.

It is a solvable problem, and making a difference here would do much more to reduce plastic pollution than even considerably reducing plastic production. Even if the world used half as much, we’d still have significant amounts of plastic flowing into our rivers and oceans.

Have a great analysis guys :)

The data archived here are the raw data required to reproduce all analysis presented in a manuscript published in PNAS.Read, Q. D., Hondula, K. L., & Muth, M. K. Biodiversity effects of food system sustainability actions from farm to fork. PNAS.This dataset is intended for use with the code archived in the git repository at https://doi.org/10.5281/zenodo.5949590. Please refer to the README.md file in that repository for instructions on how to download the data and reproduce the analysis.Note that the dataset is archived as a set of eight .zip archives. The archives all contain one or more directories. You will need to unzip all the archives into the same root directory to reproduce the directory structure described in the included documentation file.The data archived here are from a variety of different sources. As much as is practicable, they are presented here in their raw form as they were downloaded, without any processing. All processing steps can be replicated using the code archived in the accompanying code repository.Manuscript abstractDiet shifts and food waste reduction have the potential to reduce the land and biodiversity footprint of the food system. In this study, we estimated the amount of land used to produce food consumed in the United States, and the number of species threatened with extinction as a result of that land use. We predicted potential changes to biodiversity threat under scenarios of food waste reduction and shifts to recommended healthy and sustainable diets. Domestically produced beef and dairy, which require vast land areas, and imported fruit, which has an intense impact on biodiversity per unit land, have especially high biodiversity footprints. Adopting the Planetary Health diet or the USDA-recommended vegetarian diet nationwide would reduce the biodiversity footprint of food consumption. However, increases in consumption of foods grown in global biodiversity hotspots both inside and outside the United States, especially fruits and vegetables, would partially offset the reduction. In contrast, the USDA-recommended US-style and Mediterranean-style diets would increase the biodiversity threat due to increased consumption of dairy and farmed fish. Simply halving food waste would benefit global biodiversity over half as much as all Americans simultaneously shifting to a sustainable diet. Combining food waste reduction with adoption of a sustainable diet could reduce the biodiversity footprint of United States food consumption by roughly half. Species facing extinction because of unsustainable food consumption practices could be rescued by reducing agriculture's footprint; diet shifts and food waste reduction can help us get there.

This is a global dataset of 1571 locations where surface manta tows were conducted. Samples were divided into 4 size categories. Weights and particle counts were recoreded for each category.

Goal 12Ensure sustainable consumption and production patternsTarget 12.1: Implement the 10-Year Framework of Programmes on Sustainable Consumption and Production Patterns, all countries taking action, with developed countries taking the lead, taking into account the development and capabilities of developing countriesIndicator 12.1.1: Number of countries developing, adopting or implementing policy instruments aimed at supporting the shift to sustainable consumption and productionSG_SCP_CNTRY: Countries with sustainable consumption and production (SCP) national action plans or SCP mainstreamed as a priority or target into national policies (1 = YES; 0 = NO)SG_SCP_CORMEC: Countries with coordination mechanism for sustainable consumption and production (1 = YES; 0 = NO)SG_SCP_POLINS: Countries with policy instrument for sustainable consumption and production (1 = YES; 0 = NO)SG_SCP_OTHER: Country with Other implementing activities for sustainable consumption and production (1 = YES; 0 = NO)SG_SCP_TOTL: Countries with policies, instruments and mechanism in place for sustainable consumption and production (1 = YES; 0 = NO)Target 12.2: By 2030, achieve the sustainable management and efficient use of natural resourcesIndicator 12.2.1: Material footprint, material footprint per capita, and material footprint per GDPEN_MAT_FTPRPG: Material footprint per unit of GDP, by type of raw material (kilograms per constant 2010 United States dollar)EN_MAT_FTPRPC: Material footprint per capita, by type of raw material (tonnes)EN_MAT_FTPRTN: Material footprint, by type of raw material (tonnes)Indicator 12.2.2: Domestic material consumption, domestic material consumption per capita, and domestic material consumption per GDPEN_MAT_DOMCMPT: Domestic material consumption, by type of raw material (tonnes)EN_MAT_DOMCMPG: Domestic material consumption per unit of GDP, by type of raw material (kilograms per constant 2010 United States dollars)EN_MAT_DOMCMPC: Domestic material consumption per capita, by type of raw material (tonnes)Target 12.3: By 2030, halve per capita global food waste at the retail and consumer levels and reduce food losses along production and supply chains, including post-harvest lossesIndicator 12.3.1: (a) Food loss index and (b) food waste indexAG_FLS_IDX: Food loss percentage (%)AG_FOOD_WST_PC: Food waste per capita (KG)AG_FOOD_WST: Food waste (Tonnes)Target 12.4: By 2020, achieve the environmentally sound management of chemicals and all wastes throughout their life cycle, in accordance with agreed international frameworks, and significantly reduce their release to air, water and soil in order to minimize their adverse impacts on human health and the environmentIndicator 12.4.1: Number of parties to international multilateral environmental agreements on hazardous waste, and other chemicals that meet their commitments and obligations in transmitting information as required by each relevant agreementSG_HAZ_CMRMNTRL: Parties meeting their commitments and obligations in transmitting information as required by Montreal Protocol on hazardous waste, and other chemicalsSG_HAZ_CMRROTDAM: Parties meeting their commitments and obligations in transmitting information as required by Rotterdam Convention on hazardous waste, and other chemicalsSG_HAZ_CMRBASEL: Parties meeting their commitments and obligations in transmitting information as required by Basel Convention on hazardous waste, and other chemicalsSG_HAZ_CMRSTHOLM: Parties meeting their commitments and obligations in transmitting information as required by Stockholm Convention on hazardous waste, and other chemicalsSG_HAZ_CMRMNMT: Parties meeting their commitments and obligations in transmitting information as required by Minamata Convention on hazardous waste, and other chemicals (%)Indicator 12.4.2: (a) Hazardous waste generated per capita; and (b) proportion of hazardous waste treated, by type of treatmentEN_EWT_GENV: Electronic waste generated (Tonnes)EN_EWT_GENPCAP: Electronic waste generated, per capita (Kg)EN_EWT_RCYV: Electronic waste recycling (Tonnes)EN_EWT_RCYR: Electronic waste recycling, rate (%)EN_EWT_RCYPCAP: Electronic waste recycling, per capita (Kg)EN_HAZ_GENV: Hazardous waste generated (Tonnes)EN_HAZ_PCAP: Hazardous waste generated, per capita (Kg)EN_HAZ_GENGDP: Hazardous waste generated, per unit of GDP (kilograms per constant 2015 United States dollars)EN_HAZ_TREATV: Hazardous waste treated, by type of treatment (Tonnes)EN_HAZ_TRTDISR: Hazardous waste treated or disposed, rate (%)EN_HAZ_TRTDISV: Hazardous waste treated or disposed (Tonnes)EN_MWT_COLLV: Municipal waste collected (Tonnes)EN_MWT_TREATR: Municipal waste treated, by type of treatment (%)EN_MWT_GENV: Municipal waste generated (Tonnes)EN_EWT_COLLV: Electronic waste collected (Tonnes)EN_EWT_COLLPCAP: Electronic waste collected, per capita (KG)EN_EWT_COLLR: Electronic waste collection rate (%)EN_MWT_RCYV: Municipal waste recycled (Tonnes)Target 12.5: By 2030, substantially reduce waste generation through prevention, reduction, recycling and reuseIndicator 12.5.1: National recycling rate, tons of material recycledEN_EWT_RCYV: Electronic waste recycling (Tonnes)EN_EWT_RCYR: Electronic waste recycling, rate (%)EN_EWT_RCYPCAP: Electronic waste recycling, per capita (Kg)EN_MWT_RCYV: Municipal waste recycled (Tonnes)Target 12.6: Encourage companies, especially large and transnational companies, to adopt sustainable practices and to integrate sustainability information into their reporting cycleIndicator 12.6.1: Number of companies publishing sustainability reportsEN_SCP_FRMN: Number of companies publishing sustainability reports with disclosure by dimension, by level of requirement (Number)Target 12.7: Promote public procurement practices that are sustainable, in accordance with national policies and prioritiesIndicator 12.7.1: Degree of sustainable public procurement policies and action plan implementationTarget 12.8: By 2030, ensure that people everywhere have the relevant information and awareness for sustainable development and lifestyles in harmony with natureIndicator 12.8.1: Extent to which (i) global citizenship education and (ii) education for sustainable development are mainstreamed in (a) national education policies; (b) curricula; (c) teacher education; and (d) student assessmentTarget 12.a: Support developing countries to strengthen their scientific and technological capacity to move towards more sustainable patterns of consumption and productionIndicator 12.a.1: Installed renewable energy-generating capacity in developing countries (in watts per capita)EG_EGY_RNEW: Installed renewable electricity-generating capacity (watts per capita)Target 12.b: Develop and implement tools to monitor sustainable development impacts for sustainable tourism that creates jobs and promotes local culture and productsIndicator 12.b.1: Implementation of standard accounting tools to monitor the economic and environmental aspects of tourism sustainabilityST_EEV_STDACCT: Implementation of standard accounting tools to monitor the economic and environmental aspects of tourism (number of tables)ST_EEV_ACCSEEA: Implementation of standard accounting tools to monitor the economic and environmental aspects of tourism (SEEA tables)ST_EEV_ACCTSA: Implementation of standard accounting tools to monitor the economic and environmental aspects of tourism (Tourism Satellite Account tables)Target 12.c: Rationalize inefficient fossil-fuel subsidies that encourage wasteful consumption by removing market distortions, in accordance with national circumstances, including by restructuring taxation and phasing out those harmful subsidies, where they exist, to reflect their environmental impacts, taking fully into account the specific needs and conditions of developing countries and minimizing the possible adverse impacts on their development in a manner that protects the poor and the affected communitiesIndicator 12.c.1: Amount of fossil-fuel subsidies per unit of GDP (production and consumption)iER_FFS_CMPT: Fossil-fuel subsidies (consumption and production) (millions of constant United States dollars)ER_FFS_CMPT_PC: Fossil-fuel subsidies (consumption and production) per capita (constant United States dollars)ER_FFS_CMPT_GDP: Fossil-fuel subsidies (consumption and production) as a proportion of total GDP (%)

Water Quality Dataset

The basis of this dataset is taken from WaterBase water quality data shared on EAA. After most of the columns there were dropped, new data was created with the help of Worldbank, OSM, Foursquare, SEDAC. After removing the country and city information from the available location information, socioeconomic features of that country were added. However, the distance of certain road types close to those coordinates was also added with OSM. It is thought that such information plays an important role in the pollution of waters.

Features:

parameterWaterBodyCategory: Water body category code, as defined in the codelist. (Taken from EAA) observedPropertyDeterminandCode: Unique code of the determinand monitored, as defined in the codelist. (Taken from EAA) procedureAnalysedFraction: Specification of which fraction of the sample was analysed. (Taken from EAA) procedureAnalysedMedia: Type of media monitored. (Taken from EAA) resultUom: Unit of measure for the reported values. (Taken from EAA) phenomenonTimeReferenceYear: Year during which the data were sampled. (Taken from EAA) parameterSamplingPeriod: The period of the year during which the data used for the aggregation were sampled. (Taken from EAA) resultMeanValue: Mean value of the data used for aggregation. (Taken from EAA) waterBodyIdentifier: Unique international identifier of the water body in which the data were obtained. (Taken from EAA) Country: Country info generated by using coordinates. PopulationDensity: Population density of Country TerraMarineProtected_2016_2018: Mean of protected Terra Marine areas of Country Between 2016-2018 TouristMean_1990_2020: Mean of Tourist count of Country between 1990-2020 VenueCount: Venue count in near of given coordinates. netMigration_2011_2018: Mean of migration of given Country between 2011-2018 literacyRate_2010_2018: Literacy rate of Country between 2010-2018 combustibleRenewables_2009_2014: Compustible Renewable count in Country between 2009-2014 droughts_floods_temperature: gdp composition_food_organic_waste_percent composition_glass_percent composition_metal_percent composition_other_percent composition_paper_cardboard_percent composition_plastic_percent composition_rubber_leather_percent composition_wood_percent composition_yard_garden_green_waste_percent waste_treatment_recycling_percent

Sources: https://www.eea.europa.eu/data-and-maps/data/waterbase-water-quality-2 https://datacatalog.worldbank.org/dataset/what-waste-global-database

Analysis of ‘👣 Ecological Footprint per capita ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/nfa-2016-editione on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

National Footprint Accounts 2016 Edition

Dataset provides Ecological Footprint per capita data for years 1961-2012 in global hectares (gha).

Ecological Footprint is a measure of how much area of biologically productive land and water an individual, population, or activity requires to produce all the resources it consumes and to absorb the waste it generates, using prevailing technology and resource management practices. The Ecological Footprint is measured in global hectares. Because trade is global, an individual or country's Footprint includes land or sea from all over the world. Without further specification, Ecological Footprint generally refers to the Ecological Footprint of consumption. Ecological Footprint is often referred to in short form as Footprint.

This dataset was created by Global Footprint Network and contains around 8000 samples along with Quality Score, Year, technical information and other features such as: - Country Code - Ef Percap - and more.

How to use this dataset

• Analyze Country in relation to Quality Score
• Study the influence of Year on Country Code
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Global Footprint Network

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--- Original source retains full ownership of the source dataset ---

This Food Expiry Tracker dataset was created to analyze household food purchasing patterns and consumption behavior in relation to expiration dates. The dataset contains 500 entries tracking 15 variables across various food categories and storage methods, providing insights into how different factors influence whether food is consumed before expiry or wasted.

Food waste is a critical global challenge - approximately 1.3 billion tonnes of food are wasted globally each year 1. Households are responsible for over 60% of this waste, making consumer-level tracking and prediction essential for developing effective waste reduction strategies.

This record is for Approval for Access product AfA330. International Waste Shipments Records of International shipments permitted under the Transfrontier Shipment of Waste Regulations 2007. Shipments into or out of the UK qualify as International Waste Shipments. They are registered to the country where the producer or receiver is registered, regardless of the exit or entrance point from/to the UK. The Environment Agency holds details of producers and receivers registered in England. This dataset initially covers Refuse Derived Fuel, with other waste types being added over time. Refuse-derived fuel (RDF) is waste typically from the mechanical treatment of waste (for example sorting, crushing, compacting, pelletising, etc). RDF consists largely of combustible components of both municipal and commercial industrial waste, such as plastics and biodegradable waste. Shipments are recorded in four groups; Waste leaving the UK, Waste arriving in another country from the UK, Waste leaving another country destined for the UK, and Waste arriving in the UK from another country. • Waste exported from England, permitted under the Transfrontier Shipment of Waste Regulations 2007. • Waste received from England, permitted under the Transfrontier Shipment of Waste Regulations 2007. • Waste imported from England, permitted under the Transfrontier Shipment of Waste Regulations 2007. • Waste received in England, permitted under the Transfrontier Shipment of Waste Regulations 2007. Permit holders give indicative figures for how much waste they wish to have approved for import/export. They are not forecasts or projections. • Indicative amounts of waste anticipated for export, permitted under the Transfrontier Shipment of Waste Regulations 2007. These are broad approvals. They give an inaccurate overestimate of actual exports. • Indicative amounts of waste anticipated for import, permitted under the Transfrontier Shipment of Waste Regulations 2007. These are broad approvals. They give an inaccurate overestimate of actual imports. Attribution statement: © Environment Agency copyright and/or database right 2015. All rights reserved.

This record is for Approval for Access product AfA415. Records of International shipments permitted under the Transfrontier Shipment of Waste Regulations 2007. Shipments into or out of the UK qualify as International Waste Shipments. They are registered to the country where the producer or receiver is registered, regardless of the exit or entrance point from/to the UK. The Environment Agency holds details of producers and receivers registered in England. This dataset initially covers Refuse Derived Fuel, with other waste types being added over time. Refuse-derived fuel (RDF) is waste typically from the mechanical treatment of waste (for example sorting, crushing, compacting, pelletising, etc). RDF consists largely of combustible components of both municipal and commercial industrial waste, such as plastics and biodegradable waste. Shipments are recorded in four groups; Waste leaving the UK, Waste arriving in another country from the UK, Waste leaving another country destined for the UK, and Waste arriving in the UK from another country. • AfA328 – waste exported from England, permitted under the Transfrontier Shipment of Waste Regulations 2007. • AfA329 - waste received from England, permitted under the Transfrontier Shipment of Waste Regulations 2007. • AfA330 - waste imported from England, permitted under the Transfrontier Shipment of Waste Regulations 2007. • AfA331 - waste received in England, permitted under the Transfrontier Shipment of Waste Regulations 2007. Permit holders give indicative figures for how much waste they wish to have approved for import/export. They are not forecasts or projections. • AfA414 - indicative amounts of waste anticipated for export, permitted under the Transfrontier Shipment of Waste Regulations 2007. These are broad approvals. They give an inaccurate overestimate of actual exports. • AfA415 - indicative amounts of waste anticipated for import, permitted under the Transfrontier Shipment of Waste Regulations 2007. These are broad approvals. They give an inaccurate overestimate of actual imports. Attribution statement: © Environment Agency copyright and/or database right 2015. All rights reserved