Estimating the contribution of demographic parameters to changes in population growth is essential for understanding why populations fluctuate. Integrated Population Models (IPMs) offer a possibility to estimate contributions of additional demographic parameters, for which no data have been explicitly collected: typically immigration. Such parametersare often subsequently highlighted as important drivers of population growth. Yet, accuracy in estimating their temporal variation, and consequently their contribution to changes in population growth rate, has not been investigated.

To quantify the magnitude and cause of potential biases when estimating the contribution of immigration using IPMs, we simulated data (using Northern Wheatear Oenanthe oenanthe population estimates) from controlled scenarios to examine potential biases and how they depend on IPM parameterization, formulation of priors, the level of temporal variation in immigration, and sample size. We also used empirical data on populations with known rates of immigration: Soay Sheep Ovis aries and Mauritius kestrel Falco punctatus with zero immigration and grey wolf Canis lupus in Scandinavia with near-zero immigration.

IPMs strongly overestimated the contribution of immigration to changes in population growth in scenarios when immigration was simulated with zero temporal variation (proportion of variance attributed to immigration = 63% for the more constrained formulation and real sample size) and in the wild populations, where the true number of immigrants was zero or near-zero (Kestrel 19.1-98.2%, Sheep 4.2-36.1%, Wolf 84.0-99.2%). Although the estimation of immigration in the simulation study became more accurate with increasing temporal variation and sample size, it was often not possible to distinguish between an accurate estimation from data with high temporal variation versus an overestimation from data with low temporal variation. Unrealistically large sample sizes may be required to estimate the contribution of immigration well.

To minimise the risk of overestimating the contribution of immigration (or any additional parameter) in IPMs, we recommend to: (i) look for evidence of variation in immigration before investigating its contribution to population growth, (ii) simulate and model data for comparison to the real data, and (iii) use explicit data on immigration when possible.

1- Temporal fluctuations in growth rates can arise from both variation in age-specific vital rates and temporal fluctuations in age structure (i.e., the relative abundance of individuals in each age-class). However, empirical assessments of temporal fluctuations in age structure and their effects on population growth rate are rare. Most research has focused on understanding the contribution of changing vital rates to population growth rates and these analyses routinely assume that: (i) populations have stable age distributions, (ii) environmental influences on vital rates and age structure are stationary (i.e., the mean and/or variance of these processes does not change over time), and (iii) dynamics are independent of density. 2- Here we quantified fluctuations in age structure and assessed whether they were stationary for four populations of free-ranging vertebrates: moose (observed for 48 years), elk (15 years), tawny owls (15 years) and gray wolves (17 years). We also assessed the extent that fluctuations in age structure were useful for predicting annual population growth rates using models which account for density-dependence. 3- Fluctuations in age structure were of a similar magnitude to fluctuations in abundance. For three populations (moose, elk, owls), the mean and the skew of the age distribution fluctuated without stabilizing over the observed time periods. More precisely, the sample variance (interannual variance) of age structure indices increased with the length of the study period which suggests that fluctuations in age structure were non-stationary for these populations – at least over the 15-48 year periods analysed. 4- Fluctuations in age structure were associated with population growth rate for two populations. In particular, population growth varied from positive to negative for moose and from near zero to negative for elk as the average age of adults increased over its observed range. 5- Non-stationarity in age structure may represent an important mechanism by which abundance becomes non-stationary – and therefore difficult to forecast – over time scales of concern to wildlife managers. Overall, our results emphasize the need for vertebrate populations to be modelled using approaches that consider transient dynamics and density-dependence, and that do not rely on the assumption that environmental processes are stationary.

SummaryWe modified a bi-seasonal Ricker model previously developed by Betini et al. (2013) to examine the effects of season-specific habitat loss in either the breeding or non-breeding period and different strengths of density dependence on the production of experimentally-derived signals of population decline. The bi-seasonal habitat loss model is parameterized using the r-K formulation of the Ricker model, with separate values of growth rate (r) and carrying capacity (K) for each season (i.e., rb = reproductive output, rnb = non-breeding mortality, Kb = carrying capacity in the breeding period, Knb = carrying capacity in the non-breeding period). Exponential habitat decay is simulated in either season using two additional terms: Hb (the proportion of initial food remaining in the breeding period) and Hnb (the proportion of initial food remaining in the non-breeding period). The code here is used to simulate five different rates of habitat loss in either the breeding or non-breeding period over breeding or non-breeding of 50 generations, with habitat loss commencing after 20 generations. We ran 1,000 replicates simulations for each scenario/parameterization (see below). Initial starting parameters for a particular simulation are sampled from a distribution to allow for some degree of variability (but not strictly stochasticity) in population dynamics. We randomly sampled 25 replicates from each parameterization for subsequent plotting and analysis, data from which are provided in the CSV file.A complete description of the simulation methods and analysis is available in the pre-print on EcoEvoRxiv.Contentsbiseasonal_Ricker_code.R — R code to produce a bi-seasonal Ricker model in which habitat loss is simulated in either the breeding or non-breeding period.biseasonal_Ricker_simdata.csv — a sample of 25 simulated time series of bi-seasonal population abundance under different seasons and rates of habitat loss and strengths of density dependence.Variable definitionsnitt_t_DD = unique replicate identifier (factor) combining the replicate number (nitt), treatment type (t), and strength of density dependence (DD) simulated (e.g., "14_control_flies" references simulation 14 for the control treatment with the strength of density dependence based on values derived from an experimental population of fruit flies) — see variables below.nitt = replicate identification number (not strictly unique to different treatments)strength_DD = four-level factor (flies, weak, moderate, strong) indicating the initial strength of density dependence used to parameterize the model. In all cases, the strength was the same in both the breeding and non-breeding period (i.e., weak and weak, moderate and moderate, etc.). See values in the methods section of the paper or in the specific code for each model parameterization.treat = 11-level factor (control, b02, b5, b10, b20, b25, n02, n05, n10, n20, n25) indicating the season and rate of habitat loss being simulated where "control" indicates no habitat loss and "bXX" and "nXX" indicate breeding habitat and non-breeding habitat loss, respectively, at 2, 5, 10, 20, or 25% per generation.seasonT = three-level factor (c, b, n) indicating the season of treatment (c = control = no habitat loss, b = breeding, n = non-breeding).lossT = 6-level factor (0, 2, 5, 10, 20, 25) indicating the rate of habitat loss as a percent decrease per generation. A value of zero (0) indicates no habitat loss applied (i.e., for controls).time = integer (range = 1 to 100) indicating the time step in the model. Each generation (see below) consists of two timesteps (one each for the non-breeding and breeding seasons).gen = integer (range = 1 to 50) indicating the generation in each simulation. Each generation is repeated twice (with one row for each season). Each replicate was simulated for 20 generations under control conditions before the commencement of habitat loss in generation 21.season = two-level factor (n = non-breeding, b = breeding) indicating the season within each generation.count = integer value indicating the population size simulated in each season of each generation.rate = continuous value expressing the change in population size from the previous timestep to the current (e.g., if the previous (non-breeding) population size was 189 and the current (breeding) value is 249, then rate = 249/189 = 1.32). For rows where season = b = breeding, this value represents the breeding growth rate; when season = n = non-breeding, this value indicates non-breeding survival.first_match = integer indicates the first timestep in which population size reached zero (0) indicating the season and generation in which a simulation became extinct.ReferencesBetini, G.S., Griswold, C.K., and Norris, D.R. (2013), Carry-over effects, sequential density dependence and the dynamics of populations in a seasonal environment. Proceedings of the Royal Society B 280: 20130110. https://doi.org/10.1098/rspb.2013.0110

These population projections were prepared by the Australian Bureau of Statistics (ABS) for Geoscience Australia. The projections are not official ABS data and are owned by Geoscience Australia. These projections are for Statistical Areas Level 2 (SA2s) and Local Government Areas (LGAs), and are projected out from a base population as at 30 June 2022, by age and sex. Projections are for 30 June 2023 to 2032, with results disaggregated by age and sex.

Method The cohort-component method was used for these projections. In this method, the base population is projected forward annually by calculating the effect of births, deaths and migration (the components) within each age-sex cohort according to the specified fertility, mortality and overseas and internal migration assumptions. The projected usual resident population by single year of age and sex was produced in four successive stages – national, state/territory, capital city/rest of state, and finally SA2s. Assumptions were made for each level and the resulting projected components and population are constrained to the geographic level above for each year.
These projections were derived from a combination of assumptions published in Population Projections, Australia, 2022 (base) to 2071 on 23 November 2023, and historical patterns observed within each state/territory.

Projections – capital city/rest of state regions The base population is 30 June 2022 Estimated Resident Population (ERP) as published in National, state and territory population, June 2022. For fertility, the total fertility rate (at the national level) is based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, of 1.6 babies per woman being phased in from 2022 levels over five years to 2027, before remaining steady for the remainder of the projection span. Observed state/territory, and greater capital city level fertility differentials were applied to the national data so that established trends in the state and capital city/rest of state relativities were preserved. Mortality rates are based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, and assume that mortality rates will continue to decline across Australia with state/territory differentials persisting. State/territory and capital city/rest of state differentials were used to ensure projected deaths are consistent with the historical trend. Annual net overseas migration (NOM) is based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, with an assumed gain (at the national level) of 400,000 in 2022-23, increasing to 315,000 in 2023-24, then declining to 225,000 in 2026-27, after which NOM is assumed to remain constant. State and capital city/rest of state shares are based on a weighted average of NOM data from 2010 to 2019 at the state and territory level to account for the impact of COVID-19. For internal migration, net gains and losses from states and territories and capital city/rest of state regions are based on the medium assumption used in Population Projections, Australia, 2022 (base) to 2071, and assume that net interstate migration will trend towards long-term historic average flows.

Projections – Statistical Areas Level 2 The base population for each SA2 is the estimated resident population in each area by single year of age and sex, at 30 June 2022, as published in Regional population by age and sex, 2022 on 28 September 2023. The SA2-level fertility and mortality assumptions were derived by combining the medium scenario state/territory assumptions from Population Projections, Australia, 2022 (base) to 2071, with recent fertility and mortality trends in each SA2 based on annual births (by sex) and deaths (by age and sex) published in Regional Population, 2021-22 and Regional Population by Age and Sex, 2022. Assumed overseas and internal migration for each SA2 is based on SA2-specific annual overseas and internal arrivals and departures estimates published in Regional Population, 2021-22 and Regional Population by Age and Sex, 2022. The internal migration data was strengthened with SA2-specific data from the 2021 Census, based on the usual residence one year before Census night question. Assumptions were applied by SA2, age and sex. Assumptions were adjusted for some SA2s, to provide more plausible future population levels, and age and sex distribution changes, including areas where populations may not age over time, for example due to significant resident student and defence force populations. Most assumption adjustments were made via the internal migration component. For some SA2s with zero or a very small population base, but where significant population growth is expected, replacement migration age/sex profiles were applied. All SA2-level components and projected projections are constrained to the medium series of capital city/rest of state data in Population Projections, Australia, 2022 (base) to 2071.

Projections – Local Government Areas The base population for each LGA is the estimated resident population in each area by single year of age and sex, at 30 June 2022, as published in Regional population by age and sex, 2022 on 28 September 2023. Projections for 30 June 2023 to 2032 were created by converting from the SA2-level population projections to LGAs by age and sex. This was done using an age-specific population correspondence, where the data for each year of the projection span were converted based on 2021 population shares across SA2s. The LGA and SA2 projections are congruous in aggregation as well as in isolation. Unlike the projections prepared at SA2 level, no LGA-specific projection assumptions were used.

Nature of projections and considerations for usage The nature of the projection method and inherent fluctuations in population dynamics mean that care should be taken when using and interpreting the projection results. The projections are not forecasts, but rather illustrate future changes which would occur if the stated assumptions were to apply over the projection period. These projections do not attempt to allow for non-demographic factors such as major government policy decisions, economic factors, catastrophes, wars and pandemics, which may affect future demographic behaviour. To illustrate a range of possible outcomes, alternative projection series for national, state/territory and capital city/rest of state areas, using different combinations of fertility, mortality, overseas and internal migration assumptions, are prepared. Alternative series are published in Population Projections, Australia, 2022 (base) to 2071. Only one series of SA2-level projections was prepared for this product. Population projections can take account of planning and other decisions by governments known at the time the projections were derived, including sub-state projections published by each state and territory government. The ABS generally does not have access to the policies or decisions of commonwealth, state and local governments and businesses that assist in accurately forecasting small area populations. Migration, especially internal migration, accounts for the majority of projected population change for most SA2s. Volatile and unpredictable small area migration trends, especially in the short-term, can have a significant effect on longer-term projection results. Care therefore should be taken with SA2s with small total populations and very small age-sex cells, especially at older ages. While these projections are calculated at the single year of age level, small numbers, and fluctuations across individual ages in the base population and projection assumptions limit the reliability of SA2-level projections at single year of age level. These fluctuations reduce and reliability improves when the projection results are aggregated to broader age groups such as the five-year age bands in this product. For areas with small elderly populations, results aggregated to 65 and over are more reliable than for the individual age groups above 65. With the exception of areas with high planned population growth, SA2s with a base total population of less than 500 have generally been held constant for the projection period in this product as their populations are too small to be reliably projected at all, however their (small) age/sex distributions may change slightly. These SA2s are listed in the appendix. The base (2022) SA2 population estimates and post-2022 projections by age and sex include small artificial cells, including 1s and 2s. These are the result of a confidentialisation process and forced additivity, to control SA2 and capital city/rest of state age/sex totals, being applied to their original values. SA2s and LGAs in this product are based on the Australian Statistical Geography Standard (ASGS) boundaries as at the 2021 Census (ASGS Edition 3). For further information, see Australian Statistical Geography Standard (ASGS) Edition 3.

Made possible by the Digital Atlas of Australia The Digital Atlas of Australia is a key Australian Government initiative being led by Geoscience Australia, highlighted in the Data and Digital Government Strategy. It brings together trusted datasets from across government in an interactive, secure, and easy-to-use geospatial platform. The Australian Bureau of Statistics (ABS) is working in partnership with Geoscience Australia to establish a set of web services to make ABS data available in the Digital Atlas of Australia.

Contact the Australian Bureau of Statistics If you have questions or feedback about this web service, please email geography@abs.gov.au. To subscribe to updates about ABS web services and geospatial products, please complete this form. For information about how the ABS manages any personal information you provide view the ABS privacy policy.

Data and geography references Source data publication: Population Projections, Australia, 2022 (base)

The statistic shows the 20 countries with the lowest fertility rates in 2024. All figures are estimates. In 2024, the fertility rate in Taiwan was estimated to be at 1.11 children per woman, making it the lowest fertility rate worldwide. Fertility rate The fertility rate is the average number of children born per woman of child-bearing age in a country. Usually, a woman aged between 15 and 45 is considered to be in her child-bearing years. The fertility rate of a country provides an insight into its economic state, as well as the level of health and education of its population. Developing countries usually have a higher fertility rate due to lack of access to birth control and contraception, and to women usually foregoing a higher education, or even any education at all, in favor of taking care of housework. Many families in poorer countries also need their children to help provide for the family by starting to work early and/or as caretakers for their parents in old age. In developed countries, fertility rates and birth rates are usually much lower, as birth control is easier to obtain and women often choose a career before becoming a mother. Additionally, if the number of women of child-bearing age declines, so does the fertility rate of a country. As can be seen above, countries like Hong Kong are a good example for women leaving the patriarchal structures and focusing on their own career instead of becoming a mother at a young age, causing a decline of the country’s fertility rate. A look at the fertility rate per woman worldwide by income group also shows that women with a low income tend to have more children than those with a high income. The United States are neither among the countries with the lowest, nor among those with the highest fertility rate, by the way. At 2.08 children per woman, the fertility rate in the US has been continuously slightly below the global average of about 2.4 children per woman over the last decade.

Statistical Area 2 2023 update

SA2 2023 is the first major update of the geography since it was first created in 2018. The update is to ensure SA2s are relevant and meet criteria before each five-yearly population and dwelling census. SA2 2023 contains 135 new SA2s. Updates were made to reflect real world change of population and dwelling growth mainly in urban areas, and to make some improvements to their delineation of communities of interest.

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Description

This dataset is the definitive version of the annually released statistical area 2 (SA2) boundaries as at 1 January 2023 as defined by Stats NZ. This version contains 2,395 SA2s (2,379 digitised and 16 with empty or null geometries (non-digitised)).

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SA2 is an output geography that provides higher aggregations of population data than can be provided at the statistical area 1 (SA1) level. The SA2 geography aims to reflect communities that interact together socially and economically. In populated areas, SA2s generally contain similar sized populations.

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The SA2 should:

form a contiguous cluster of one or more SA1s,

excluding exceptions below, allow the release of multivariate statistics with minimal data suppression,

capture a similar type of area, such as a high-density urban area, farmland, wilderness area, and water area,

be socially homogeneous and capture a community of interest. It may have, for example:

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• a shared road network,
• shared community facilities,
• shared historical or social links, or
• socio-economic similarity,

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form a nested hierarchy with statistical output geographies and administrative boundaries. It must:

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• be built from SA1s,
• either define or aggregate to define SA3s, urban areas, territorial authorities, and regional councils.

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SA2s in city council areas generally have a population of 2,000–4,000 residents while SA2s in district council areas generally have a population of 1,000–3,000 residents.

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In major urban areas, an SA2 or a group of SA2s often approximates a single suburb. In rural areas, rural settlements are included in their respective SA2 with the surrounding rural area.

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SA2s in urban areas where there is significant business and industrial activity, for example ports, airports, industrial, commercial, and retail areas, often have fewer than 1,000 residents. These SA2s are useful for analysing business demographics, labour markets, and commuting patterns.

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In rural areas, some SA2s have fewer than 1,000 residents because they are in conservation areas or contain sparse populations that cover a large area.

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To minimise suppression of population data, small islands with zero or low populations close to the mainland, and marinas are generally included in their adjacent land-based SA2.

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Zero or nominal population SA2s

To ensure that the SA2 geography covers all of New Zealand and aligns with New Zealand’s topography and local government boundaries, some SA2s have zero or nominal populations. These include:

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• SA2s where territorial authority boundaries straddle regional council boundaries. These SA2s each have fewer than 200 residents and are: Arahiwi, Tiroa, Rangataiki, Kaimanawa, Taharua, Te More, Ngamatea, Whangamomona, and Mara.
• SA2s created for single islands or groups of islands that are some distance from the mainland or to separate large unpopulated islands from urban areas
• SA2s that represent inland water, inlets or oceanic areas including: inland lakes larger than 50 square kilometres, harbours larger than 40 square kilometres, major ports, other non-contiguous inlets and harbours defined by territorial authority, and contiguous oceanic areas defined by regional council.
• SA2s for non-digitised oceanic areas, offshore oil rigs, islands, and the Ross Dependency. Each SA2 is represented by a single meshblock. The following 16 SA2s are held in non-digitised form (SA2 code; SA2 name):

400001; New Zealand Economic Zone, 400002; Oceanic Kermadec Islands, 400003; Kermadec Islands, 400004; Oceanic Oil Rig Taranaki, 400005; Oceanic Campbell Island, 400006; Campbell Island, 400007; Oceanic Oil Rig Southland, 400008; Oceanic Auckland Islands, 400009; Auckland Islands, 400010 ; Oceanic Bounty Islands, 400011; Bounty Islands, 400012; Oceanic Snares Islands, 400013; Snares Islands, 400014; Oceanic Antipodes Islands, 400015; Antipodes Islands, 400016; Ross Dependency.

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SA2 numbering and naming

Each SA2 is a single geographic entity with a name and a numeric code. The name refers to a geographic feature or a recognised place name or suburb. In some instances where place names are the same or very similar, the SA2s are differentiated by their territorial authority name, for example, Gladstone (Carterton District) and Gladstone (Invercargill City).

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SA2 codes have six digits. North Island SA2 codes start with a 1 or 2, South Island SA2 codes start with a 3 and non-digitised SA2 codes start with a 4. They are numbered approximately north to south within their respective territorial authorities. To ensure the north–south code pattern is maintained, the SA2 codes were given 00 for the last two digits when the geography was created in 2018. When SA2 names or boundaries change only the last two digits of the code will change.

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For more information please refer to the Statistical standard for geographic areas 2023.

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Generalised version

This generalised version has been simplified for rapid drawing and is designed for thematic or web mapping purposes.

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Macrons

Names are provided with and without tohutō/macrons. The column name for those without macrons is suffixed ‘ascii’.

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Digital data

Digital boundary data became freely available on 1 July 2007.

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To download geographic classifications in table formats such as CSV please use Ariā

According to Cognitive Market Research, The Global Ground Granulated Blast Furnace Slag (GGBFS) Market size is USD 0.058 billion in 2023 and will expand at a compound annual growth rate (CAGR) of 5.80% from 2023 to 2030.

The demand for Ground Granulated Blast Furnace Slag (GGBFS) Market is rising due to the sustainable construction practices.
Rising demand for urbanization and population growth trends is higher in the Ground Granulated Blast Furnace Slag (GGBFS) Market.
The concrete road & flyover category held the highest Ground Granulated Blast Furnace Slag (GGBFS) Market revenue share in 2023.
Asia Pacific ground granulated blast furnace slag will continue to lead, whereas the North America Ground Granulated Blast Furnace Slag (GGBFS) Market will experience the most substantial growth until 2030.

Increasing Demand for Sustainable Construction Practices to Provide Viable Market Output

The increasing demand for sustainable construction practices is a pivotal driver shaping the market dynamics for materials like Ground Granulated Blast Furnace Slag (GGBFS). With a growing global awareness of environmental concerns, the construction industry is undergoing a paradigm shift towards eco-friendly and sustainable solutions. GGBFS, as a byproduct of the steel manufacturing process, aligns with these sustainability goals by repurposing an industrial waste into a valuable construction material. Its use significantly reduces the carbon footprint of construction projects, making it an attractive choice for environmentally conscious builders and developers. As regulatory bodies and industry standards increasingly emphasize sustainable construction, the demand for GGBFS is expected to witness a notable surge, further solidifying its position as a key contributor to green and resilient building practices globally.

For instance, in October 2023 Tata Steel Limited to enter into agreement with Tata Power Renewable Energy Ltd. (TPREL) to source 379 MW of Renewable Power, a milestone towards achieving Net Zero. By partnering with Tata Power Renewable Energy Ltd., Tata Steel aims to secure a substantial amount of renewable power, a crucial milestone in its journey toward achieving Net Zero. This initiative reflects the company's recognition of the importance of transitioning to renewable energy sources to mitigate the environmental impact of its operations.

(Source:www.tatasteel.com/media/newsroom/press-releases/india/2023/tata-steel-limited-to-enter-into-agreement-with-tata-power-renewable-energy-ltd-tprel-to-source-379-mw-of-renewable-power-a-milestone-towards-achieving-net-zero/)

Urbanization and Population Growth to Propel Market Growth

The relentless forces of urbanization and population growth are pivotal drivers propelling the growth of the Ground Granulated Blast Furnace Slag (GGBFS) market. As the global population continues to surge, accompanied by rapid urbanization trends, there is an escalating demand for robust and sustainable infrastructure. GGBFS plays a crucial role in meeting this demand by offering enhanced performance and durability in construction applications. The burgeoning need for housing, commercial spaces, and transportation infrastructure in urban areas fuels the demand for construction materials, with GGBFS standing out as an eco-friendly solution. Its utilization in concrete not only contributes to the structural integrity of buildings and infrastructure but also aligns with the imperative for environmentally conscious construction practices. This symbiotic relationship between urbanization, population growth, and the demand for sustainable construction materials positions GGBFS as a key player in shaping the infrastructural landscape of burgeoning urban centers around the world.

For instance, in January 2022, JFE Steel to Integrate JFE Mineral, Mizushima Ferroalloy and JFE Material. This strategic decision may lead to improved coordination, cost-effectiveness, and a more unified approach to business activities within the JFE Group.

(Source:www.jfe-mineral.co.jp/e_mineral/news/announcement/20220126.html)

Market Dynamics of Ground Granulated Blast Furnace Slag GGBFS

Initial Costs and Perceptions to Restrict Market Growth

The Ground Granulated Blast Furnace Slag (GGBFS) market faces constraints associated with initial costs and perceptions that could impede its growth trajectory. Despite its long-term economic and environme...

Some of the SNK rasters intentionally do not align or have the same extent. These rasters were not snapped to a common raster per the authors discretion. Please review selected rasters prior to use. These varying alignments are a result of the use of differing source data sets and all products derived from them. We recommend that users snap or align rasters as best suits their own projects. - The first set of files represents projections of the number of historical (1901-1981) standard deviations (SD) above the historical mean for each of three future decades (2020-2029, 2050-2059, 2060-2069) temperature and precipitation levels.

The second set of files represents projections of the proportion of years in a future decade when monthly temperature or precipitation levels are at least two historical SDs above the historical mean.

Temperature and precipitation are monthly means and totals, respectively.

The spatial extent is clipped to a Seward REA boundary bounding box.

In the first set of files, each file, referred to as SDclasses, consists of ordered categorical (factor) data, with three unique classes (factor levels), coded 0, 1 and 2. Within each file, raster grid cells categorized as 0 represent those where the future decadal mean temperature or precipitation value does not exceed one historical SD above the historical mean. Cells categorized as 1 represent those where future decadal values exceed the historical mean by at least one but less than two historical SDs. Cells categorized as 2 represent those where future decadal values exceed the historical mean by at least two historical SDs.

In the second set of files, each file, referred to as annProp, consists of numerical data. Within each file, raster grid cell values are proportions, ranging from zero to one, representing the proportion of years in a future decade when monthly mean temperature or monthly total precipitation are at least two historical SD above the historical mean. Proportions are calculated on five GCMs and then averaged rather than calculated on the five-model composite directly.

Overview:

The historical monthly mean is calculated for each month as the 1901-1981 interannual mean, i.e., the mean of 82 annual monthly values.

The historical SD is calculated for each month as the 1901-1981 interannual SD, i.e., the SD of 82 annual monthly values.

2x2 km spatial resolution downscaled CRU 3.1 data is used as the historical baseline.

A five-model composite (average) of the Alaska top five AR4 2x2 km spatial resolution downscaled global circulation models (GCMs), using the A2 emissions scenario, is used for the future decadal datasets. This 5 Model Average is referred to by the acronym 5modelavg.

For a description of the model selection process, please see Walsh et al. 2008. Global Climate Model Performance over Alaska and Greenland. Journal of Climate. v. 21 pp. 6156-6174.

Emmission scenarios in brief:

The Intergovernmental Panel on Climate Change (IPCC) Special Report on Emissions Scenarios (SRES) created a range of scenarios to explore alternative development pathways, covering a wide range of demographic, economic and technological driving forces and resulting greenhouse gas emissions. The B1 scenario describes a convergent world, a global population that peaks in mid-century, with rapid changes in economic structures toward a service and information economy. The Scenario A1B assumes a world of very rapid economic growth, a global population that peaks in mid-century, rapid introduction of new and more efficient technologies, and a balance between fossil fuels and other energy sources. The A2 scenario describes a very heterogeneous world with high population growth, slow economic development and slow technological change.

These files are bias corrected and downscaled via the delta method using PRISM (http:prism.oregonstate.edu) 1961-1990 2km data as baseline climate. Absolute anomalies are utilized for temperature variables. Proportional anomalies are utilized for precipitation variables. Please see http:www.snap.uaf.edumethods.php for a description of the downscaling process.

File naming scheme:

[variable]_[metric]_[groupModel]_[timeFrame].[fileFormat]

[variable] pr, tas [metric] SDclasses, annProp [groupModel] 5modelAvg [timeFrame] decade_month [fileFormat] tif

examples:

pr_SDclasses_5modelAvg_2020s_01.tif

This file represents a spatially explicit map of the number of January total precipitation historical SDs above the January total precipitation historical mean level, for projected 2020-2029 decadal mean January total precipitation, where cell values are binned in classes less than one, at least one, less than two, and greater than two, labeled as 0, 1, and 2, respectively.

tas_annProp_5modelAVg_2060s_06.tif

This file represents a spatially explicit map of the proportion of years in the period 2060-2069 when June mean temperature projections are at least two historical SDs above the June mean temperature historical mean level, where cell values are proportions ranging from zero to one.

tas = near-surface air temperature

pr = precipitation including both liquid and solid phases

Snapshot img

Civil Engineering Market Size 2024-2028

The civil engineering market size is forecast to increase by USD 2.57 billion at a CAGR of 3.9% between 2023 and 2028.

The market is experiencing significant growth, driven by the surge in construction activities in developing countries. This trend is expected to continue as infrastructure development remains a priority for many governments. Another key factor fueling market growth is the adoption of intelligent processing in civil engineering projects. This includes the use of technologies such as Building Information Modeling (BIM) and Geographic Information Systems (GIS) to improve project efficiency and accuracy. 
However, the market is also facing challenges, including the decline in construction activities in some regions due to economic downturns and natural disasters. Despite these challenges, the future of the market looks promising, with continued investment in infrastructure development and the ongoing integration of advanced technologies.

What will be the Size of the Civil Engineering Market During the Forecast Period?

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The civil engineering services market encompasses a broad range of construction activities, including social infrastructure, residential, offices, educational institutes, luxury hotels, restaurants, transport buildings, online retail warehousing, and various types of infrastructure projects such as roads, bridges, railroads, airports, and ports. This market is driven by various factors, including population growth, urbanization, and the increasing demand for sustainable and energy-efficient structures. 
Digitalization plays a significant role In the civil engineering sector, with the adoption of digital civil engineering, smart grids, urban transportation systems, industrial automation, parking systems, and IT services. Additionally, there is a growing trend towards the development of zero-energy buildings, insulated buildings, double skin facades, PV panels, and e-permit systems.
Inspection technology and integrated 3D modeling are also becoming increasingly important In the civil engineering industry, enabling more accurate and efficient design and construction processes. The market is expected to continue growing, driven by the increasing demand for infrastructure development and the ongoing digital transformation of the industry.

How is this Civil Engineering Industry segmented and which is the largest segment?

The civil engineering industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.

Application

  Real estate
  Infrastructure
  Industrial


Geography

  APAC

    China
    India


  North America

    Canada
    US


  Europe

    Germany


  Middle East and Africa



  South America

By Application Insights

The real estate segment is estimated to witness significant growth during the forecast period. The real estate market encompasses the development, acquisition, and sale of property, land, and buildings. Global urbanization and infrastructure investment growth have significantly impacted this sector. In particular, the Asia Pacific region has seen rapid expansion in various sectors, such as commercial construction, with India leading the charge. Notably, international real estate development is projected to present opportunities for countries like India, as demonstrated by the October 2021 MoU between the Jammu and Kashmir administration and the Dubai government, focusing on industrial parks, IT towers, and super-specialty hospitals. Civil engineering services play a crucial role in real estate development, with a focus on social infrastructure, residential, construction activities, offices, educational institutes, hotels, restaurants, transport buildings, online retail warehousing, immigration, housing, and construction.

Innovations in green building products, energy efficiency, sustainable construction materials, such as cross-laminated timber, and digital technology are transforming the industry. Key areas of growth include infrastructure, oil and gas, energy and power, aviation, public spending, non-residential construction, healthcare centers, infrastructure projects, and digital civil engineering. Civil engineering firms provide essential services, including rail structures, tunnels, bridges, maintenance services, renovation activities, and energy-efficient products. The real estate segment also includes industrial real estate and housing development, with a shift towards flexible infrastructure, roads, railroads, airports, ports, single-family houses, and home remodeling. The industry is embracing advanced simulation tools, drone technology, and carbon emissions reduction initiatives, such as net-zero energy buildings, pre-f