In a survey conducted from October 2021 to July 2022, respondents revealed that Gen Zers (or zoomers) cared about improving their environmental impact. Gen Zers who have attained a high education were those who found improving their environmental impact the most important, with **** percent stating they found it very important.

Combination of data from 2011 & 2016 Census of Population onto the same observation at household level, facilitating the analysis of environment-relevant Census questions such as central heating fuel in the context of socio-economic questions such as type of building, principal economic status, type of dwelling, etc.

The dataset contains economic, social, demographic, and environmental metrics for all countries in 2023. The cleaned and organised data comes from the World Bank and its open database.

The OECD Environmental Statistics database provide a unique collection of policy-relevant environmental statistics.

Climate and land-use change are major components of global environmental change with feedbacks between these components. The consequences of these interactions show that land use may exacerbate or alleviate climate change effects. Based on these findings it is important to use land-use scenarios that are consistent with the specific assumptions underlying climate-change scenarios. The Integrated Climate and Land-Use Scenarios (ICLUS) project developed land-use outputs that are based on a downscaled version of the Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) social, economic, and demographic storylines. ICLUS outputs are derived from a pair of models. A demographic model generates county-level population estimates that are distributed by a spatial allocation model (SERGoM v3) as housing density across the landscape. Land-use outputs were developed for the four main SRES storylines and a baseline ("base case"). The model is run for the conterminous USA and output is available for each scenario by decade to 2100. In addition to housing density at a 1 hectare spatial resolution, this project also generated estimates of impervious surface at a resolution of 1 square kilometer. This shapefile holds population data for all counties of the conterminous USA for all decades (2010-2100) and SRES population growth scenarios (A1, A2, B1, B2), as well as a 'base case' (BC) scenario, for use in the Integrated Climate and Land Use Scenarios (ICLUS) project.

As an inescapable concomitant with the traditional route of economic development, Pakistan has been facing natural resource degradation and pollution problems. The unsavory spectacle of air pollution, water contamination and other macro environmental impacts such as water logging, land degradation and desertification, are on rise. All this, in conjunction with rapid growth in population, have been instrumental to the expanding tentacles of poverty. In order to assess the environmental problems as a prelude to arrest the pace of degeneration and provide for sustainable course of economic development, the availability of adequate data is imperative. This publication is an attempt to provide relevant statistics compiled through secondary sources collected from different departments. The task of environmental data collection does not consist just in determining the frame and approaching the selected sources of information because environmental statistics per se do not exist as a ready-to-compile/pick category as generally perceived about data and statistics. The information on environment has generated through deliberate scientific observations and measurements in a consistent way, under the aegis of specialized agencies. Since it is skill and resource intensive pursuit and generally undertaken in public sector, the overall budgetary/financial constraints do take the toll of the canvas and continuity of environmental data generation down the time lane. Consequently, availability of the statistics falls short of desired level. Further, the studies pertaining to normal over a period of time are repeated after long time intervals, which may not conform with the quinquennial periodicity of this document. Similarly, many variables antecedental, associated with and, consequential to, environment are derived from population census, which is yet to be carried out even though the stipulated decennial time frame has long been overstepped. Nevertheless, the latest update of the compendium is a good attempt to mirror quite a few environmental factors as a means to raise awareness and help stay focus on the pivotality of environmental concerns for instituting sustainable development paradigm-the only way forward to ensuring the continuity of human race on the face of planet earth.

This dataset, sourced from the United States Census Bureau, presents time series data at the county, ZCTA, and state levels. It includes a select number of variables from the American Community Survey (ACS) 1-Year Estimates, ACS 5-Year Estimates, and the Decennial Census (SF1). A key feature of this dataset is the harmonization of variable codes across the different years and surveys, ensuring consistency and comparability over time. As a historical dataset designed for analysis, the cross year harmonization facilitates tracking changes over time and is useful for studies that look at long-term effects in areas like epidemiology, environmental health, and public policy. The ACS 1-Year Estimates offer annual insights into current conditions, aiding timely analyses. The ACS 5-Year Estimates provide increased statistical reliability for analyzing smaller populations and areas by pooling data over five years. The Decennial Census, with datasets for 2000, 2010, and 2020 available through the Census API, gives a decadal population count, serving as a foundational element for longitudinal studies.

The "Social, Economic, Environmental, Demographic Information System (SEEDIS)" is a research and development project at the Lawrence Berkeley Laboratory, supported by the U.S. Department of Energy (USDOE), U.S. Department of Labor (USDOL), and others. It was initiated in 1972 by USDOL as a demonstration project to link data from multiple sources. Since that time, the project has been expanded. SEEDIS's main purpose is to provide accurate, and timely information for policy formulation, implementation and management. The SEEDIS Project addresses these information needs by providing a unified framework for data management, information retrieval, statistical analysis, and graphic display of data from a collection of databases for various geographic levels and time periods, drawn from the U.S. Census Bureau, the U.S. Environmental Protection Agency (USEPA) and the Department of Health and Human Services.

SEEDIS contains information on Census, energy, environment, geography, health, population characteristics, and socioeconomic status. SEEDIS allows the user to produce graphical and map presentations of analyses of combinations of these data for a variety of geographic levels and scope.

SEEDIS' census information relates to population size by major racial and ethnic groupings for 1970 and 1980. These data are variously available at the national, state, county, city and census tract level.

SEEDIS' energy information relates to electrical generating capacity for 1960 through 1995. These data are available at the national, county, and standardized metropolitan statistical area (SMSA) level. The data system also contains 1970 residential housing data, and heating energy requirements in 1970, and biomass resources for 1976 and 2025 at the county geographic level.

SEEDIS' environmental information relates to air quality measurements for criteria pollutants. The data are available for 1974 through 1976 at the census tract level. They are derived from the AIRS data system (formerly SAROAD). Assessments include total suspended particulates (TSP), sulfur and nitrogen dioxides, photochemical oxidants, ozone, carbon monoxide, sulfates, and total and nonmethane hydrocarbons. For each pollutant, county estimates of pollutant concentration (at the position of the county population centroid) were calculated as the weighted geometric means of measurements from nearby stations, including stations in nearby counties. The location of the air quality monitoring stations is also available from the National Air Monitoring Stations (NAMS) data system.

SEEDIS' geographic information relates to the centroids of the 1970 household populations. The data are available for a variety of geographic levels. The areas, centroids, and boundaries of census tracts and counties are also included.

SEEDIS' health information relates to age-, sex-, and race-specific total mortality. The data are available for geographic levels as small as counties for the years 1969 through 1984. In addition, total annual leukemia mortality is available. Cancer incidence for 1973 through 1981 from the Surveillance, Epidemiologic, and End Results (SEER) registers is included for the states that participate in the program.

SEEDIS' population relates to age-, race-, and sex-specific population counts (from the 1980 Census) and estimates for the years 1950 to 1987. The data are available for varying geographic levels. Estimates are available from a variety of sources.

SEEDIS' economic information relates to labor force, employment by industry, income, education, fertility. It also contains data on the Census of Agriculture and many county- and state-specific data. LANGUAGE:

English ACCESS/AVAILABILITY:

Data Center: University of California at Berkeley (UCB) Dissemination Media: Hard copy (specialized data extraction service at cost), tape copies of selected data files File Format: Access Instructions: Contact the data center. Size: Memory Requirements: Operating System: Hardware Required: Software Required: Availability Status: On Request Documentation Available:

Explore Environmental SDG Statistics and Trends UNEP is custodian agency for 25 Sustainable Goals Indicators, of these 10 are tier I and tier II and where data is reported to the Secretary General Global database. Currently UNEP reports data for the following tier I and tier II indicators: 6.3.2 Proportion of bodies of water with good ambient water quality 6.5.1 Degree of integrated water resources management implementation (0-100) 6.6.1 Change in the extent of water-related ecosystems over time 8.4.2 Domestic material consumption (DMC) and DMC per capita, per GDP 12.1.1 Number of countries with sustainable consumption and production (SCP) national action plans or SCP mainstreamed as a priority or target into national policies 12.2.2 Domestic material consumption (DMC) and DMC per capita, per GDP 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 agreement 12.c.1 Amount of fossil-fuel subsidies per unit of GDP (production and consumption). 14.5.1 Coverage of protected areas in relation to marine areas 15.1.2 Proportion of important sites for terrestrial and freshwater biodiversity that are covered by protected areas, by ecosystem type 15.4.1 Coverage by protected areas of important sites for mountain biodiversity SDG Global reporting: We also coordinate our contribution of data and storylines on the SDG indicators presented in the SDG global reports. The yearly report contains Sustainable Development Goals Report and the Statistical Annex report. Click on each year to access report. 2020 2019 2018 2017 2016 SDG Global Database: UNSD SDG Global Database

Dataset detailing where NJ recycling centers are located, and the population demographics of areas within 2 miles of the recycling centers.

With this information, it can be deduced which regions of NJ have facilities that are evenly distributed, and which do not. In addition, this data could say something about the locations of the facilities in proximity to minority communities. A correlation between the amount of facilities near certain demographics of people could be made, and it could be linked to reported disability, disease, and social status findings.

The environmental impact assessment-related API provided by the National Institute of Environmental Research of the Ministry of Environment allows you to understand the population and housing characteristics of a specific business district through population and housing attribute information predicted and surveyed, and allows you to look up the development status (number of cases) of nearby areas, business code, full-time population after the business, user population after the business, planned housing population after the business, etc. It is a service that provides detailed information such as the housing supply rate (%) surveyed based on the business district subject to the environmental impact assessment, environmental impact assessment business code, year, population by gender and total population, total number of households (households), total number of houses (households), arrangement of data base year, and name of city, county, district, town, township, and dong.

The dataset contains economic, social, demographic, and environmental metrics for all countries in 2022. The cleaned and organised data comes from the World Bank and its open database.

The Environment Statistics of Nepal 2024 is an updated version of the 2019 publication, providing a comprehensive overview of the state of Nepal's environment and its changes over time. This report includes a wide range of statistical tables covering environmental conditions, resource use, residuals, extreme events and disasters, human settlements, environmental health, and efforts in environmental protection, management, and engagement. It serves as a crucial resource for understanding key environmental trends and challenges across the country.

By 2030, the estimated environmental impact of e-commerce logistics will be much higher in the largest 100 urban areas worldwide. The delivery car fleet could reach *** million vehicles by the end of this decade, and total emissions caused by parcel and freight shipping are forecast to generate ** million metric tons of CO2. In addition, the average commute time, including last-mile delivery, is expected to increase from ** minutes in 2019 to ** minutes in 2030.

Percentage Population growth has been calculated from the change between the 2001 and the 2006 Population and Housing Census data. The 2001 data was concorded to 2006 boundaries by ABS, and the calculations were completed by BRS. The change between 2001-2006 has been presented as a percentage population growth and attributed to each Statistical Local Area and then rasterised. Capital cities have been masked out of this analysis.

National data on employment in the environmental and clean technology products sector by type of worker and by demographic characteristic. This includes full-time employment and part time employment, and it includes gender, age, level of education, immigration status, indigenous identity and visible minority status by environmental and clean technology products group. Variables of interest include number of jobs, hours worked, wages and salaries as well as average hourly wage.

Background: Efforts to support disadvantaged communities have been prioritized through initiatives like Justice40, the Inflation Reduction Act (IRA), and the Bipartisan Infrastructure Law (BIL). Identifying disadvantaged communities involves several datasets with associated variables related to vulnerability indicators and scores. There are three key datasets:

1. Climate and Economic Justice Screening Tool (CEJST) from the White House Council on Environmental Quality (CEQ).
2. Energy Justice Mapping Tool from the Department of Energy (DOE) Office of Economic Impact & Diversity.
3. Environmental Justice Screening Tool from the Environmental Protection Agency (EPA) Office of Environmental Justice and External Civil Rights.

Problem:

1. Each dataset employs similar but distinct criteria and outcomes.
2. The unique IDs or GEOIDs differ across datasets. CEJST uses the 2010 census boundaries, while the Energy Justice Mapping Tool and the Environmental Justice Screening Tool are based on the 2019 and 2021 census boundaries, respectively.

To address these issues, this dataset consolidates information on disadvantaged communities and their associated variables by combining the three distinct datasets:

• CEJST: Provides binary data indicating whether a tract is a disadvantaged community. A community is classified as disadvantaged if it meets any of the following thresholds: 1) one or more indicators within categories such as climate change, energy, health, housing, pollution, transportation, and water & wastewater, coupled with low income; 2) one or more indicators in workforce development category and education; or 3) tribal lands. Environment and pollution indicators come from the EPA, while socio-demographic indicators are from the American Community Survey (ACS) for 2015-2019.

• Energy Justice Mapping Tool: Offers a DAC score, a continuous variable representing the sum of the 36 indicator percentiles. It includes environment, pollution, and socio-demographic indicators from the EPA and ACS (2015-2019).

• Environmental Justice Screening Tool: Includes the 13 Environmental Justice (EJ) Index and Supplemental Index. These continuous variables are weighted with socio-demographic indicators from ACS (2017-2021).

Data Descriptions

• Unit of Analysis: Census tract
• Geometry: 2021 census boundaries
• Columns:
  • Socio-demographic indicators
  • 13 Environmental Justice (EJ) index values
  • 13 Supplemental index values
  • Binary indicator for disadvantaged community classification
  • Disadvantaged Community (DAC) scores
• Datasets:
  • results/DAC.csv: Contains all columns from the three datasets.
  • results/DAC_s.csv: A shorter version, including socio-demographic indicators and EJ and Supplemental indices (Environmental Justice Screening Tool), disadvantaged community classification (CEJST), and DAC scores (Energy Justice Mapping Tool).
• Code:
  • syntax/code.R: This script illustrates the methodology for merging the three datasets, culminating in the creation of the two CSV files located in the results directory.

Purpose:

The dataset aims to help researchers identify overall disadvantaged communities or determine which specific communities are classified as disadvantaged. By consolidating these datasets, researchers can more effectively analyze and compare the various criteria used to define disadvantaged communities, enhancing the comprehensiveness of their studies.

Note

For complete data descriptions and sources, please refer to the original datasets.

• Climate and Economic Justice Screening Tool (CEJST)
• Energy Justice Mapping Tool
• Environmental Justice Screening Tool

This dataset provides insights into household environmental and water-related conditions in Saudi Arabia. It includes indicators on water access and usage (such as drinking water sources, handwashing facilities, and sewage system connections), sanitation and hygiene (toilet types, fire safety equipment, and water conservation tools), and waste management (waste disposal methods, waste segregation, and tank cleaning). Additionally, it covers household exposure to pollution (air, noise, light, and visual pollution), green practices (organic food purchases and tree planting), and environmental awareness (concerns, knowledge, and key environmental issues). These indicators help assess household infrastructure, environmental behavior, and sustainability practices.Notes:Main source measures households' use of drinking water by sources Bottled water is gallon containers or bottles Includes covered well, open well, covered spring and surface water Service disruption is measured from all major water sources for at least one disruption during the year

This map service displays demographic data used in EJSCREEN. All demographic data were derived from American Community Survey 2006-2010 estimates. EJSCREEN is an environmental justice screening tool that provides EPA with a nationally consistent approach to screening for potential areas of EJ concern that may warrant further investigation. The EJ indexes are block group level results that combine multiple demographic factors with a single environmental variable (such as proximity to traffic) that can be used to help identify communities living with the greatest potential for negative environmental and health effects. The EJSCREEN tool is currently for internal EPA use only. It is anticipated that as users become accustomed to this new tool, individual programs within the Agency will develop program use guidelines and a community of practice will develop around them within the EPA Geoplatform. Users should keep in mind that screening tools are subject to substantial uncertainty in their demographic and environmental data, particularly when looking at small geographic areas, such as Census block groups. Data on the full range of environmental impacts and demographic factors in any given location are almost certainly not available directly through this tool, and its initial results should be supplemented with additional information and local knowledge before making any judgments about potential areas of EJ concern.