NOTE FOR USERS: For local-level projections, such as at a township and municipal-level, please use the original “2018 Series”. This is the data CMAP recommends be used for planning, grant applications, and other official purposes. CMAP is confident in the updated regional-level population projections; however, the projections for township and municipal level populations appear less reflective of current trends in nearterm population growth. Further refinements of the local forecasts are likely needed.CONTENTS:Filename: ONTO2050OriginalForecastData2018.zipTitle: Socioeconomic Forecast Data, 2018 SeriesThis .zip file contains data associated with the original ON TO 2050 forecast, adopted in October 2018. Includes:Excel file of regional projections of population and employment to the year 2050:CMAP_RegionalReferenceForecast_2015adj.xlsx (94kb)Excel file of local (county, municipality, Chicago community area) projections of household population and employment to the year 2050: ONTO2050LAAresults20181010.xlsx (291kb)GIS shapefile of projected local area allocations to the year 2050 by Local Allocation Zone (LAZ): CMAP_ONTO2050_ForecastByLAZ_20181010.shp (19.7mb)Filename: ONTO2050OriginalForecastDocumentation2018.zipTitle: Socioeconomic Forecast Documentation, 2018 SeriesThis .zip file contains PDF documentation of the original ON TO 2050 forecast, adopted in October 2018. Includes:Louis Berger forecast technical report (2016): CMAPSocioeconomicForecastFinal-Report04Nov2016.pdf (2.3mb)Louis Berger addendum (2017): CMAPSocioeconomicForecastRevisionAddendum20Jun2017.pdf (0.6mb)ON TO 2050 Forecast appendix (2018): ONTO2050appendixSocioeconomicForecast10Oct2018.pdf (2.6mb)Filename: Socioeconomic-Forecast-Appendix-Final-October-2022.pdfTitle: Socioeconomic Forecast Appendix, 2022 SeriesDocumentation & results for the updated socioeconomic forecast accompanying the ON TO 2050 plan update, adopted October 2022. PDF, 2.7mbFilename: RegionalDemographicForecast_TechnicalReport_202210.pdfTitle: 2050 Regional Demographic Forecast Technical Report, 2022 SeriesSummary of methodology and results for the ON TO 2050 plan update regional demographic forecast, developed in coordination with the Applied Population Lab at the University of Wisconsin, Madison. PDF, 1.7mbFilename: RegionalEmpForecast_TechnicalReport_202112.pdfTitle: 2050 Regional Employment Forecast Technical Report, 2022 SeriesSummary of methodology and results for the ON TO 2050 plan update regional employment forecast, developed by EBP and Moody's Analytics. PDF, 0.8mbFilename: CMAPRegionalForecastONTO2050update202209.xlsxTitle: Regional Projections, 2022 SeriesProjections of population and employment to the year 2050, produced for the ON TO 2050 plan update adopted October 2022. 60kbFilename: CMAPLocalForecastONTO2050update202210.xlsxTitle: County and Municipal Projections, October 2022 (2022 Series)Projections of population and employment to the year 2050 at the county and municipal level, produced for the ON TO 2050 plan update adopted October 2022.

The dataset is an insightful exploration into the loan patterns of 2022 in Kent County 27001, Delaware. It encompasses a wide range of data, from legal entity identifiers to detailed census tract information, along with borrower demographics and property specifics. This dataset not only sheds light on the types of loans prevalent in the area but also provides a snapshot of the socio-economic fabric of the county, making it a valuable asset for researchers and financial experts.

Abstract

UNHCR through REACH/IMPACT Initiative, conducted a Socio-Economic Vulnerability Assessment (SEVA) in 47 Priority Areas of Return and Reintegration (PARRs) across the country. To understand the current status of reintegration for displaced groups prior to Co-PROSPER implementation in the PARR districts, REACH/IMPACT Initiative conducted a baseline evaluation of the 50 newly selected PARR locations across four different dimensions: 1) community leadership inclusivity, 2) community relations and stability, 3) strengthening public services and equitable access, and 4) livelihood and economic outlook. Indices were created to determine a baseline for these four key objectives. The assessment covered 6,067 HH interviews in 47 PARRs and targeted refugee returnees, IDPs, IDP returnees and host communities. In addition, Neighborhood Maps (nMaps) were developed to identify key infrastructure and services in each PARR.

Geographic coverage

47 Priority Areas of Return and Reintegration (PARR)

Analysis unit

Households

Universe

Refugee returnees, IDPs, IDP returnees and host communities in Afghanistan 2022

Kind of data

Census/enumeration data [cen]

Sampling procedure

Two stage cluster sampling based on population size per cluster

Mode of data collection

Face-to-face [f2f]

This 6MB download is a zip file containing 5 pdf documents and 2 xlsx spreadsheets. Presentation on COVID-19 and the potential impacts on employment

May 2020Waka Kotahi wants to better understand the potential implications of the COVID-19 downturn on the land transport system, particularly the potential impacts on regional economies and communities.

To do this, in May 2020 Waka Kotahi commissioned Martin Jenkins and Infometrics to consider the potential impacts of COVID-19 on New Zealand’s economy and demographics, as these are two key drivers of transport demand. In addition to providing a scan of national and international COVID-19 trends, the research involved modelling the economic impacts of three of the Treasury’s COVID-19 scenarios, to a regional scale, to help us understand where the impacts might be greatest.

Waka Kotahi studied this modelling by comparing the percentage difference in employment forecasts from the Treasury’s three COVID-19 scenarios compared to the business as usual scenario.

The source tables from the modelling (Tables 1-40), and the percentage difference in employment forecasts (Tables 41-43), are available as spreadsheets.

Arataki - potential impacts of COVID-19 Final Report

Employment modelling - interactive dashboard

The modelling produced employment forecasts for each region and district over three time periods – 2021, 2025 and 2031. In May 2020, the forecasts for 2021 carried greater certainty as they reflected the impacts of current events, such as border restrictions, reduction in international visitors and students etc. The 2025 and 2031 forecasts were less certain because of the potential for significant shifts in the socio-economic situation over the intervening years. While these later forecasts were useful in helping to understand the relative scale and duration of potential COVID-19 related impacts around the country, they needed to be treated with care recognising the higher levels of uncertainty.

The May 2020 research suggested that the ‘slow recovery scenario’ (Treasury’s scenario 5) was the most likely due to continuing high levels of uncertainty regarding global efforts to manage the pandemic (and the duration and scale of the resulting economic downturn).

The updates to Arataki V2 were framed around the ‘Slower Recovery Scenario’, as that scenario remained the most closely aligned with the unfolding impacts of COVID-19 in New Zealand and globally at that time.

Find out more about Arataki, our 10-year plan for the land transport system

May 2021The May 2021 update to employment modelling used to inform Arataki Version 2 is now available. Employment modelling dashboard - updated 2021Arataki used the May 2020 information to compare how various regions and industries might be impacted by COVID-19. Almost a year later, it is clear that New Zealand fared better than forecast in May 2020.Waka Kotahi therefore commissioned an update to the projections through a high-level review of:the original projections for 2020/21 against performancethe implications of the most recent global (eg International monetary fund world economic Outlook) and national economic forecasts (eg Treasury half year economic and fiscal update)The treasury updated its scenarios in its December half year fiscal and economic update (HYEFU) and these new scenarios have been used for the revised projections.Considerable uncertainty remains about the potential scale and duration of the COVID-19 downturn, for example with regards to the duration of border restrictions, update of immunisation programmes. The updated analysis provides us with additional information regarding which sectors and parts of the country are likely to be most impacted. We continue to monitor the situation and keep up to date with other cross-Government scenario development and COVID-19 related work. The updated modelling has produced employment forecasts for each region and district over three time periods - 2022, 2025, 2031.The 2022 forecasts carry greater certainty as they reflect the impacts of current events. The 2025 and 2031 forecasts are less certain because of the potential for significant shifts over that time.

Data reuse caveats: as per license.

Additionally, please read / use this data in conjunction with the Infometrics and Martin Jenkins reports, to understand the uncertainties and assumptions involved in modelling the potential impacts of COVID-19.

COVID-19’s effect on industry and regional economic outcomes for NZ Transport Agency [PDF 620 KB]

Data quality statement: while the modelling undertaken is high quality, it represents two point-in-time analyses undertaken during a period of considerable uncertainty. This uncertainty comes from several factors relating to the COVID-19 pandemic, including:

a lack of clarity about the size of the global downturn and how quickly the international economy might recover differing views about the ability of the New Zealand economy to bounce back from the significant job losses that are occurring and how much of a structural change in the economy is required the possibility of a further wave of COVID-19 cases within New Zealand that might require a return to Alert Levels 3 or 4.

While high levels of uncertainty remain around the scale of impacts from the pandemic, particularly in coming years, the modelling is useful in indicating the direction of travel and the relative scale of impacts in different parts of the country.

Data quality caveats: as noted above, there is considerable uncertainty about the potential scale and duration of the COVID-19 downturn. Please treat the specific results of the modelling carefully, particularly in the forecasts to later years (2025, 2031), given the potential for significant shifts in New Zealand's socio-economic situation before then.

As such, please use the modelling results as a guide to the potential scale of the impacts of the downturn in different locations, rather than as a precise assessment of impacts over the coming decade.

The Provincial Economic Review and Outlook (PERO), provides a review on the current state of the global, National and Provincial economies and reflects on the effectiveness of existing government policies. The evidence based and data-led document provides insight into the latest economic and socio-economic trends and outlook that will impact on policy, planning and budgeting in the Western Cape. All the PEROs can be found in the Provincial Treasury Resource Library:https://www.westerncape.gov.za/provincial-treasury/resource-libraryPublication Date: 19 September 2022Data SourcesVarious socio-economic data sources are listed in the PERO.The data provided in this publication is provided in good faith, and every reasonable effort has been made to ensure that it is correct and up to date. The publication made use of the most recent published economic data utilizing sources such as the South African Reserve Bank (SARB) and Statistics SA. Published economic data on a Provincial level is only available as mid-year estimates on an annual basis.

In real terms, the Vietnamese construction industry posted positive growth during the review period (2013–2017). Growth was supported by the implementation of the government’s Seventh Five-year National Socio-Economic Development Plan (2011–2015). As part of this, the government developed various industrial, agricultural and infrastructural projects across the country during 2011–2015. Read More

India ManpowerGroup Employment Outlook Survey: Net Employment Outlook: Education, Health, Social Work and Government data was reported at 40.000 % in Dec 2022. This records a decrease from the previous number of 42.000 % for Sep 2022. India ManpowerGroup Employment Outlook Survey: Net Employment Outlook: Education, Health, Social Work and Government data is updated quarterly, averaging 41.000 % from Mar 2022 (Median) to Dec 2022, with 4 observations. The data reached an all-time high of 45.000 % in Mar 2022 and a record low of 37.000 % in Jun 2022. India ManpowerGroup Employment Outlook Survey: Net Employment Outlook: Education, Health, Social Work and Government data remains active status in CEIC and is reported by ManpowerGroup India. The data is categorized under India Premium Database’s Labour Market – Table IN.GBA048: ManpowerGroup Employment Outlook Survey: by Industry.

Policy makers, analysts, and civil society face increasing challenges to reducing hunger and sustainably improving food security. Modeling alternative future scenarios and assessing their outcomes can help inform policy choices. The International Food Policy Research Institute’s IMPACT model is an integrated system of linked economic, climate, water, and crop models that allows for the exploration of such scenarios. The IMPACT model was used to evaluate impacts of climate change on aggregate food production, food consumption (kcal per person per day), net trade of major food commodity groups, and the population at risk of hunger. At IMPACT’s core is a partial equilibrium, multimarket economic model that simulates national and international agricultural markets. Links to climate, water, and crop models support the integrated study of changing environmental, biophysical, and socioeconomic trends, allowing for in-depth analysis of a variety of critical issues of interest to policymakers at national, regional, and global levels. IMPACT benefits from close interactions with scientists across CGIAR and other leading global economic modeling efforts around the world through the Agricultural Model Intercomparison and Improvement Project (AgMIP). This dataset summarizes results from the latest IMPACT projections to 2030 and 2050, for a scenario that includes the impacts of climate change and a “baseline” scenario that assumes no climate change (for comparison). These results update previous projections by showing aggregations to six regions: Central and West Asia and North Africa; Eastern and Southern Africa; Latin America and the Caribbean; South Asia; Southeast Asia; West and Central Africa; and the rest of the world.

In 2023, China's gross domestic product amounted to approximately 17.7 trillion U.S. dollars, which was the highest GDP across the Asia-Pacific region. Japan followed with a GDP of around 4.2 trillion dollars.  China, Asia-Pacific's titan The significance of the Asia-Pacific region to the world is multifaceted, ranging from geopolitical importance to being home to more than half of the world's population. Characterized by emerging countries and dynamic economic activities, the region plays a key role in the global economy. China, the most populous country after India, and the second largest economy on the planet, accounted for about half of the total gross domestic product (GDP) in APAC as of 2023. The GDP growth in China was characterized by high rates for decades. Following the COVID-19 pandemic, the country has struggled to catch up with the previous level of growth rates and was forecast to stay at more modest real GDP growth rates in the coming years.  A new paradigm of development in the Asia-Pacific region Even though the Asia-Pacific region has made significant economic improvements in the last decades, from a developmental perspective, tackling existing socio-economic issues will be critical for future growth. An aspect worth mentioning is the GDP per capita in the region. EU countries, for example, had about three times as much GDP per capita compared to East Asia and the Pacific region in 2022. China has been working towards changing its economic focus to high-tech and service sectors while reducing its concentration on agriculture.

In 2023, the gross domestic product (GDP) of China amounted to around 17.8 trillion U.S. dollars. In comparison to the GDP of the other BRIC countries India, Russia and Brazil, China came first that year and second in the world GDP ranking. The stagnation of China's GDP in U.S. dollar terms in 2022 and 2023 was mainly due to the appreciation of the U.S. dollar. China's real GDP growth was three percent in 2022 and 5.2 percent in 2023. In 2023, per capita GDP in China reached around 12,600 U.S. dollars. Economic performance in China Gross domestic product (GDP) is a primary economic indicator. It measures the total value of all goods and services produced in an economy over a certain time period. China's economy used to grow quickly in the past, but the growth rate of China’s real GDP gradually slowed down in recent years, and year-on-year GDP growth is forecasted to range at only around four percent in the years after 2023. Since 2010, China has been the world’s second-largest economy, surpassing Japan.China’s emergence in the world’s economy has a lot to do with its status as the ‘world’s factory’. Since 2013, China is the largest export country in the world. Some argue that it is partly due to the undervalued Chinese currency. The Big Mac Index, a simplified and informal way to measure the purchasing power parity between different currencies, indicates that the Chinese currency yuan was roughly undervalued by 31 percent in 2023. GDP development Although the impressive economic development in China has led millions of people out of poverty, China is still not in the league of industrialized countries on the per capita basis. To name one example, the U.S. per capita economic output was more than six times as large as in China in 2023. Meanwhile, the Chinese society faces increased income disparities. The Gini coefficient of China, a widely used indicator of economic inequality, has been larger than 0.45 over the last decade, whereas 0.40 is the warning level for social unrest.

The total population in the 'Demography' segment of the socioeconomic indicators market in Africa was forecast to continuously increase between 2024 and 2029 by in total 34.6 million (+6.07 percent). After the seventh consecutive increasing year, the indicator is estimated to reach 604.38 million and therefore a new peak in 2029. Notably, the total population of the 'Demography' segment of the socioeconomic indicators market was continuously increasing over the past years. The Statista Market Insights cover a broad range of additional markets.

CL: Children Out of School: % of Primary School Age data was reported at 1.293 % in 2022. This records a decrease from the previous number of 2.167 % for 2021. CL: Children Out of School: % of Primary School Age data is updated yearly, averaging 2.841 % from Dec 2007 (Median) to 2022, with 16 observations. The data reached an all-time high of 4.814 % in 2009 and a record low of 1.293 % in 2022. CL: Children Out of School: % of Primary School Age data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Chile – Table CL.World Bank.WDI: Social: Education Statistics. Children out of school are the percentage of primary-school-age children who are not enrolled in primary or secondary school. Children in the official primary age group that are in preprimary education should be considered out of school.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 24, 2024. https://apiportal.uis.unesco.org/bdds.;Weighted average;

Omnibus survey of attitudes and opinions.

About this dataset

Context

The dataset presents median household incomes for various household sizes in Social Circle, GA, as reported by the U.S. Census Bureau. The dataset highlights the variation in median household income with the size of the family unit, offering valuable insights into economic trends and disparities within different household sizes, aiding in data analysis and decision-making.

Key observations

• Of the 7 household sizes (1 person to 7-or-more person households) reported by the census bureau, Social Circle did not include 5, or 7-person households. Across the different household sizes in Social Circle the mean income is $81,437, and the standard deviation is $26,277. The coefficient of variation (CV) is 32.27%. This high CV indicates high relative variability, suggesting that the incomes vary significantly across different sizes of households.
• In the most recent year, 2021, The smallest household size for which the bureau reported a median household income was 1-person households, with an income of $37,291. It then further increased to $83,212 for 6-person households, the largest household size for which the bureau reported a median household income.

https://i.neilsberg.com/ch/social-circle-ga-median-household-income-by-household-size.jpeg" alt="Social Circle, GA median household income, by household size (in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Household Sizes:

• 1-person households
• 2-person households
• 3-person households
• 4-person households
• 5-person households
• 6-person households
• 7-or-more-person households

Variables / Data Columns

• Household Size: This column showcases 7 household sizes ranging from 1-person households to 7-or-more-person households (As mentioned above).
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific household size.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Social Circle median household income. You can refer the same here

About this dataset

Context

The dataset presents the median household income across different racial categories in Social Circle. It portrays the median household income of the head of household across racial categories (excluding ethnicity) as identified by the Census Bureau. The dataset can be utilized to gain insights into economic disparities and trends and explore the variations in median houshold income for diverse racial categories.

Key observations

Based on our analysis of the distribution of Social Circle population by race & ethnicity, the population is predominantly White. This particular racial category constitutes the majority, accounting for 58.26% of the total residents in Social Circle. Notably, the median household income for White households is $93,417. Interestingly, White is both the largest group and the one with the highest median household income, which stands at $93,417.

https://i.neilsberg.com/ch/social-circle-ga-median-household-income-by-race.jpeg" alt="Social Circle median household income diversity across racial categories">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race of the head of household: This column presents the self-identified race of the household head, encompassing all relevant racial categories (excluding ethnicity) applicable in Social Circle.
• Median household income: Median household income, adjusting for inflation, presented in 2022-inflation-adjusted dollars

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Social Circle median household income by race. You can refer the same here

Private New Capital Expenditure: Chain Volume: 2022-23: Actual: Trend: Buildings & Structures: Non Mining: Health Care & Social Assistance data was reported at 1,030.000 AUD mn in Sep 2024. This records a decrease from the previous number of 1,038.000 AUD mn for Jun 2024. Private New Capital Expenditure: Chain Volume: 2022-23: Actual: Trend: Buildings & Structures: Non Mining: Health Care & Social Assistance data is updated quarterly, averaging 1,064.000 AUD mn from Sep 2017 (Median) to Sep 2024, with 29 observations. The data reached an all-time high of 1,458.000 AUD mn in Mar 2019 and a record low of 956.000 AUD mn in Mar 2022. Private New Capital Expenditure: Chain Volume: 2022-23: Actual: Trend: Buildings & Structures: Non Mining: Health Care & Social Assistance data remains active status in CEIC and is reported by Australian Bureau of Statistics. The data is categorized under Global Database’s Australia – Table AU.O018: Private New Capital Expenditure: Actual: Chain Volume: 2022-23 Price.

General Information

The Pop-AUT database was developed for the DISCC-AT project, which required subnational population projections for Austria consistent with the updated Shared Socio-Economic Pathways (SSPs). For this database, the most recent version of the nationwide SSP population projections (IIASA-WiC POP 2023) are spatially downscaled, offering a detailed perspective at the subnational level in Austria. Recognizing the relevance of this information for a wider audience, the data has been made publicly accessible through an interactive dashboard. There, users are invited to explore how the Austrian population is projected to evolve under different SSP scenarios until the end of this century.

Methodology

The downscaling process of the nationwide Shared Socioeconomic Pathways (SSP) population projections is a four-step procedure developed to obtain subnational demographic projections for Austria. In the first step, population potential surfaces for Austria are derived. These indicate the attractiveness of a location in terms of habitability and are obtained using machine learning techniques, specifically random forest models, along with geospatial information such as land use, roads, elevation, distance to cities, and elevation (see, e.g., Wang et al. 2023).

The population potential surfaces play a crucial role in distributing the Austrian population effectively across the country. Calculations are based on the 1×1 km spatial resolution database provided by Wang et al. (2023), covering all SSPs in 5-year intervals from 2020 to 2100.

Moving to the second step, the updated nationwide SSP population projections for Austria (IIASA-WiC POP 2023) are distributed to all 1×1 km grid cells within the country. This distribution is guided by the previously computed grid cell-level population potential surfaces, ensuring a more granular representation of demographic trends.

The base year for all scenarios is 2015, obtained by downscaling the UN World Population Prospects 2015 count for Austria using the WorldPop (2015) 1×1 km population count raster.

In the third step, the 1×1 km population projections are temporally interpolated to obtain yearly projections for all SSP scenarios spanning the period from 2015 to 2100.

The final step involves the spatial aggregation of the gridded SSP-consistent population projections to the administrative levels of provinces (Bundesländer), districts (Bezirke), and municipalities (Gemeinden).

Dashboard

The data can be explored interactively through a dashboard.

Data Inputs

Updated nationwide SSP population projections: IIASA-WiC POP (2023) (https://zenodo.org/records/7921989)

Population potential surfaces: Wang, X., Meng, X., & Long, Y. (2022). Projecting 1 km-grid population distributions from 2020 to 2100 globally under shared socioeconomic pathways. Scientific Data, 9(1), 563.

Shapefiles: data.gv.at

WorldPop 2015: WorldPop (www.worldpop.org - School of Geography and Environmental Science, University of Southampton; Department of Geography and Geosciences, University of Louisville; Departement de Geographie, Universite de Namur) and Center for International Earth Science Information Network (CIESIN), Columbia University (2018). Global High Resolution Population Denominators Project - Funded by The Bill and Melinda Gates Foundation (OPP1134076). https://dx.doi.org/10.5258/SOTON/WP00647

Version

This is version 1.0, built upon the Review-Phase 2 version of the updated nationwide SSP population projections (IIASA-WiC POP 2023). Once these projections are revised, this dataset will be accordingly updated.

File Organization

The SSP-consistent population projections for Austria are accessible in two formats: .csv files for administrative units (provinces = Bundesländer, districts = Politische Bezirke, municipalities = Gemeinden) and 1×1 km raster files in GeoTIFF and NetCDF formats. All files encompass annual population counts spanning from 2015 to 2100.

As per Cognitive Market Research's latest report, The Global Packaged Coffee market size was USD 27.95 Billion in 2022 and it is forecasted to reach USD 48.63 Billion by 2030. Packaged Coffee Industry's Compound Annual Growth Rate will be 7.2% from 2023 to 2030. Factors Affecting Packaged Coffee Market Growth

Increasing demand for ready-to-drink coffee boosts the Packaged Coffee market growth:

A sizable trend that has an impact on a sizable section of the worldwide population is the consumption of healthy foods and beverages. Coffee that is ready to drink is another functional beverage with health advantages. Poor eating practices, busy lifestyles, and demanding job schedules are all contributing to an increase in childhood and adult obesity rates, which drives consumers to choose more practical, nutritious foods. For instance, in February 2020, Stok cold brew launched cold brew RTD coffee. (Source:https://www.stokbrew.com/cold-brew-drinks-recipes/ )

Restraining Factors for Packaged Coffee Market

Variation in the cost of raw materials restrains the packaged coffee market:

The price of coffee beans is significantly impacted by the rising cost of agricultural inputs and the unpredictability of the weather. As a result, the ongoing fluctuation in the prices of coffee beans has adversely affected the cost of producing this product. Additionally, the price of packaged coffee is more expensive than conventional coffee, which is a factor impeding market expansion.

Impact of the COVID-19 Pandemic on the Packaged Coffee Market:

The emergence of the coronavirus has had a significant impact on both public health and the global economy. The COVID-19 pandemic has led to widespread socio-economic instability and has negatively affected various industries, including the food and beverage sector. One such industry that has been adversely affected is the coffee industry, with its production, consumption, and worldwide trade being impacted. This is primarily due to the imposition of full or partial lockdowns in many countries, which has forced numerous businesses such as offices, shops, and restaurants to remain shut, leading to a decline in the consumption of coffee outside of homes. Introduction of Packaged Coffee

Coffee that has been packaged and sold in a ready-to-drink state is referred to as ready-to-drink coffee. The packaging comes in a variety of forms, including the most popular PET bottles as well as cans, glasses, and tetra packs. The demand for beverages that are ready to drink has increased dramatically in recent years. The adoption of fast-paced lifestyles is the cause of the rising demand. Coffee is one of the most widely consumed beverages in the world and in recent years, demand for packaged coffee has grown significantly. The convenience and instant nature of packaged coffee are the main market drivers, manufacturing companies, distribution, and retailers all have opportunities to grow as a result of the rising demand for this product.

In 2020, 57 percent of the respondents of a survey carried in the US selected the vehicle's power and performance as one of the factors that would affect their vehicle purchasing decision. While 54 percent said the look of the vehicle was a factor, 21 percent said that the emotion that car imparts was a factor.

Policy makers, analysts, and civil society face increasing challenges to reducing hunger and sustainably improving food security. Modeling alternative future scenarios and assessing their outcomes can help inform policy choices. The International Food Policy Research Institute’s IMPACT model is an integrated system of linked economic, climate, water, and crop models that allows for the exploration of such scenarios. The IMPACT model was used to evaluate impacts of climate change on aggregate food production, food consumption (kcal per person per day), net trade of major food commodity groups, and the population at risk of hunger. At IMPACT’s core is a partial equilibrium, multimarket economic model that simulates national and international agricultural markets. Links to climate, water, and crop models support the integrated study of changing environmental, biophysical, and socioeconomic trends, allowing for in-depth analysis of a variety of critical issues of interest to policymakers at national, regional, and global levels. IMPACT benefits from close interactions with scientists across CGIAR and other leading global economic modeling efforts around the world through the Agricultural Model Intercomparison and Improvement Project (AgMIP). This dataset summarizes results from the latest IMPACT projections to 2030 and 2050, for a scenario that includes the impacts of climate change and a “baseline” scenario that assumes no climate change (for comparison). These results update previous projections by showing aggregations to six regions: Central and West Asia and North Africa; Eastern and Southern Africa; Latin America and the Caribbean; South Asia; Southeast Asia; West and Central Africa; and the rest of the world.