The document discusses models for forecasting population growth and their implications for environmental impacts. It introduces the concept of exponential and logistic growth models.
- Exponential growth assumes a constant growth rate leading to ever-increasing population over time. Logistic growth incorporates the finite carrying capacity of the environment, causing population growth to level off as it approaches this limit.
- Logistic growth produces an S-shaped curve, initially exponential but then slowing to reach an equilibrium population equal to the environment's carrying capacity. This model more realistically represents constraints on indefinite growth.
Population ecology is the study of populations in relation to their environment. Key concepts include population size, density, growth rates, and limiting factors. Population growth can be exponential in unlimited environments but becomes logistic as resources are depleted. The logistic model describes sigmoid growth with a carrying capacity K, where population growth rate is highest at N=K/2. Natality, mortality, immigration, and emigration influence population size. Estimating population density can involve direct counts, sampling methods like quadrats, or capture-recapture of marked individuals.
Biodiversity refers to genetic, species, and ecosystem variation within a specific area or planet. It is measured by both species richness, the number of species, and evenness, the number of individuals belonging to each species. Several factors influence biodiversity patterns, including geological history, climate, resource availability, and competition. The tropics generally have the highest species diversity due to high productivity, complex habitat structures, and survival of fragments during past climate changes. Both random processes and necessity, through competition and interaction laws, along with historical factors shape current species diversity patterns in a given location.
Population ecology examines populations as units of study. A population has characteristics like density, size, age structure, and dispersion. The four basic population parameters that affect density are natality, mortality, immigration, and emigration. Techniques to estimate population density include using quadrats, capture-recapture methods, and calculating relative density with tools like traps or roadside counts. Life tables can describe mortality schedules by tracking age-specific cohort survival. Population growth rates depend on birth and death rates, and can be modeled exponentially or logistically depending on environmental constraints.
Population ecology examines how populations change over time based on birth, death, immigration, and emigration rates. Key concepts include:
- Populations have a density that can be influenced by density-dependent and density-independent factors.
- Natality is the birth rate and mortality is the death rate. These determine a population's growth rate.
- Populations can exhibit exponential or logistic growth patterns depending on available resources/carrying capacity.
- Reproductive strategies like r/K selection influence life history traits and population dynamics.
- Competition between species occupying the same niche frequently leads to competitive exclusion of one species.
This document discusses key concepts in population ecology, including population size, density, distribution, growth patterns, biotic potential, carrying capacity, r-selected and K-selected species, environmental resistance, and predator-prey cycles. It provides examples and explanations of exponential and logistic growth curves, and compares characteristics of r-selected and K-selected species.
This document provides an overview of ecosystems, including:
1. The definition of an ecosystem as the structural and functional unit of ecology encompassing the interaction between biotic and abiotic components.
2. The key characteristics, structure, and functions of ecosystems, such as energy flow, nutrient cycling, and trophic levels.
3. Details on primary productivity, decomposition, and the flow of energy through food chains and webs within ecosystems.
The document is intended for educational purposes and provides information compiled from various sources on the basic concepts of ecosystems.
Stochastic modelling and its applicationsKartavya Jain
Stochastic processes and modelling have various applications in telecommunications. Token rings, continuous-time Markov chains, and fluid-flow models are used to model traffic flow and network performance. Aggregate dynamic stochastic models can model air traffic control by representing aircraft arrivals as Poisson processes. Disturbances like weather can be incorporated by altering flow rates. Wireless network models use search algorithms and location stochastic processes to track mobile users.
Population ecology is the study of populations in relation to their environment. Key concepts include population size, density, growth rates, and limiting factors. Population growth can be exponential in unlimited environments but becomes logistic as resources are depleted. The logistic model describes sigmoid growth with a carrying capacity K, where population growth rate is highest at N=K/2. Natality, mortality, immigration, and emigration influence population size. Estimating population density can involve direct counts, sampling methods like quadrats, or capture-recapture of marked individuals.
Biodiversity refers to genetic, species, and ecosystem variation within a specific area or planet. It is measured by both species richness, the number of species, and evenness, the number of individuals belonging to each species. Several factors influence biodiversity patterns, including geological history, climate, resource availability, and competition. The tropics generally have the highest species diversity due to high productivity, complex habitat structures, and survival of fragments during past climate changes. Both random processes and necessity, through competition and interaction laws, along with historical factors shape current species diversity patterns in a given location.
Population ecology examines populations as units of study. A population has characteristics like density, size, age structure, and dispersion. The four basic population parameters that affect density are natality, mortality, immigration, and emigration. Techniques to estimate population density include using quadrats, capture-recapture methods, and calculating relative density with tools like traps or roadside counts. Life tables can describe mortality schedules by tracking age-specific cohort survival. Population growth rates depend on birth and death rates, and can be modeled exponentially or logistically depending on environmental constraints.
Population ecology examines how populations change over time based on birth, death, immigration, and emigration rates. Key concepts include:
- Populations have a density that can be influenced by density-dependent and density-independent factors.
- Natality is the birth rate and mortality is the death rate. These determine a population's growth rate.
- Populations can exhibit exponential or logistic growth patterns depending on available resources/carrying capacity.
- Reproductive strategies like r/K selection influence life history traits and population dynamics.
- Competition between species occupying the same niche frequently leads to competitive exclusion of one species.
This document discusses key concepts in population ecology, including population size, density, distribution, growth patterns, biotic potential, carrying capacity, r-selected and K-selected species, environmental resistance, and predator-prey cycles. It provides examples and explanations of exponential and logistic growth curves, and compares characteristics of r-selected and K-selected species.
This document provides an overview of ecosystems, including:
1. The definition of an ecosystem as the structural and functional unit of ecology encompassing the interaction between biotic and abiotic components.
2. The key characteristics, structure, and functions of ecosystems, such as energy flow, nutrient cycling, and trophic levels.
3. Details on primary productivity, decomposition, and the flow of energy through food chains and webs within ecosystems.
The document is intended for educational purposes and provides information compiled from various sources on the basic concepts of ecosystems.
Stochastic modelling and its applicationsKartavya Jain
Stochastic processes and modelling have various applications in telecommunications. Token rings, continuous-time Markov chains, and fluid-flow models are used to model traffic flow and network performance. Aggregate dynamic stochastic models can model air traffic control by representing aircraft arrivals as Poisson processes. Disturbances like weather can be incorporated by altering flow rates. Wireless network models use search algorithms and location stochastic processes to track mobile users.
The document discusses various characteristics of populations including:
1) Population density which measures the number of individuals per unit area or volume.
2) Population growth rate which is the rate at which a population increases each year as a percentage of the initial population.
3) Birth and death rates which measure the number of live births and deaths per 1,000 people in a population each year.
This document discusses key concepts in population ecology, including the five characteristics of a population: geographic distribution, density, dispersion, growth rate, and age structure. It provides examples and explanations of each characteristic. Geographic distribution describes the area inhabited by a population. Density is the number of individuals per unit area. Dispersion describes the spatial distribution of individuals as either clumped, even, or random. Growth rate measures changes in population size over time. Age structure diagrams show population breakdown by age and sex.
This document discusses life tables and their importance. It provides a brief history of life tables, noting their development by John Graunt and Edmund Halley. Life tables are useful for estimating survival rates, mortality rates, and other vital statistics. They allow calculation of life expectancy and comparison of mortality between populations. The document then describes how to construct a life table and provides examples of their use in clinical medicine using the Kaplan-Meier method.
Population ecology is the science that studies changes in population size and composition, and identifies factors causing these changes. A population consists of all individuals of a species in a given area, and is characterized by its size, density, dispersion, births, deaths, and survivorship over time. Population dynamics examines how and why population size changes, influenced by density-dependent factors like resources and disease, and density-independent factors like weather. Populations typically follow an S-shaped logistic growth curve as they approach the carrying capacity of their environment.
This document discusses population growth models, including exponential and logistic growth. Exponential growth occurs when resources are unlimited and the population grows rapidly in a J-shaped curve. Logistic growth occurs when resources are limited, causing the population to initially grow rapidly then slow down, reaching an asymptote as the carrying capacity is approached. Key factors that influence population growth are birth rates, death rates, immigration, emigration, food availability, and competition for limited resources. Governments have implemented controls to limit unsustainable human population growth.
Exponential and Logistics Growth Curve - Environmental ScienceNayan Dagliya
This document discusses exponential and logistic population growth models. Exponential growth occurs when a population's growth rate remains constant regardless of population size, producing a J-shaped curve. Logistic growth accounts for environmental constraints, with the growth rate decreasing as the population approaches the carrying capacity, producing an S-shaped curve. Key factors like resources, space, food and shelter determine a population's carrying capacity.
The document discusses various characteristics of populations including size, density, growth, and dispersion. It describes population growth models as exponential or logistic depending on whether resources are unlimited or limited by the environment's carrying capacity. The strategies for population growth are also covered, distinguishing between r-selected and K-selected types that thrive in changing versus stable environments.
Multiple regression analysis allows researchers to examine the relationship between one dependent or outcome variable and two or more independent or predictor variables. It extends simple linear regression to model more complex relationships. Stepwise regression is a technique that automates the process of building regression models by sequentially adding or removing variables based on statistical criteria. It begins with no variables in the model and adds variables one at a time based on their contribution to the model until none improve it significantly.
This document discusses various statistical parameters used in pharmaceutical research and development. It describes parameters like measures of central tendency (mean, median, mode), dispersion (variance, standard deviation), coefficient of dispersion, residuals, factor analysis, absolute error, mean absolute error, and percentage error of estimate. Measures of central tendency provide a summary of the central or typical values in a data set. Dispersion measures provide a way to quantify how spread out the data is from the central value. Other parameters like residuals, errors, and factor analysis are used to analyze relationships in complex data.
This document presents a method for predicting stream flow distributions based on climatic and geomorphic data alone, without discharge measurements. It combines a physically-based stream flow model with water balance and geomorphic recession flow models. Key parameters of the stream flow model are estimated from rainfall, potential evapotranspiration, and digital elevation model data. The method was tested on calibration and test catchments. While offering a unique approach, the method has limitations including additional assumptions and reduced accuracy of parameter estimates and flow regime predictions.
This slideshow was created for the VCE Environmental Science Online Course, Unit 3: Biodiversity. It explains different methods of assessing biodiversity and discusses several indices for measurement.
Two species when occupy in same habitat accumulating same resource in same manner then competition is inevitable. The normal logistic growth is not expected. Lotka and Volterra proposed equation to describe the interspecific competition among the species. Either one of the species wins other is excluded or they co-exist in unstable or stable manner.
The document defines key terms related to metapopulation dynamics. It describes metapopulations as populations composed of spatially discrete local populations between which migration is limited. Local populations exist in habitat patches surrounded by unsuitable matrix. Metapopulations are characterized by local extinction and recolonization of vacant patches over time. If extinctions exceed recolonizations, the entire metapopulation may go regionally extinct. The document outlines different types of spatially dynamic populations and models used to study metapopulation dynamics.
The document summarizes the Lotka-Volterra predator-prey model. It describes how the model was independently developed by Lotka and Volterra to explain predator-prey population dynamics over time. The model uses differential equations that account for the growth of the prey population and the growth and death of the predator population. The model assumes exponential growth of prey in the absence of predators and proportional rates of predation and predator growth. The model predicts stable population cycles or equilibrium points where populations remain balanced. However, the model has limitations and does not reflect real-world population crashes or extinctions that can occur.
This document presents information on natality, related demographic terms, and measures of fertility. It defines natality as the birth rate, or number of births per 1000 people per year. It also discusses related concepts like mortality, fertility, and factors that affect birth rates. The document focuses on common measures used to quantify fertility, including the crude birth rate, general fertility rate, age-specific fertility rate, total fertility rate, gross reproduction rate, and net reproduction rate. It provides formulas and examples for calculating each measure and describes how they are used to analyze population changes over time.
This document defines key concepts in population ecology, including population, population ecology, population size, density, dispersion, structure, growth, pyramids, demography, and survivorship curves. It explains that population ecology studies how biotic and abiotic factors influence population density, dispersion, and size. It also describes different population structures, growth patterns, survivorship curves that reflect varying reproductive strategies and death rates at different ages among species.
This document discusses key concepts related to population growth and dynamics. It defines what a population is and describes demography as the statistical study of populations. Population size, density, and dispersion are identified as key features of populations. Crude density and ecological density are important indexes used to describe populations. Methods for estimating population densities include mark-recapture and minimum known alive techniques. The concepts of growth rate, sources and sinks, and meta-populations are also introduced. Population dispersion patterns like regular, random, clumped and regular clumped are described. Finally, the document discusses age structure and different types of age pyramids in populations.
The chi-square test is used to compare observed data with expected data. It was developed by Karl Pearson in 1900. The chi-square test calculates the sum of the squares of the differences between the observed and expected frequencies divided by the expected frequency. The chi-square value is then compared to a critical value to determine if there is a significant difference between the observed and expected results. The degrees of freedom, which determine the critical value, are calculated based on the number of rows and columns in a contingency table. The chi-square test can be used to test goodness of fit, independence of attributes, and other hypotheses.
This document provides examples of using differential equations to model physical systems involving flow processes. Example 2 models the flow of salt into and out of a tank, with the goal of finding the salt concentration over time. Example 3 models the flow of a toxic waste into a pond, with a time-varying inflow concentration, to determine the amount of waste in the pond over time. The examples illustrate setting up the initial value problem, finding the solution, and discussing assumptions and limitations of the mathematical model.
This document provides an overview of applying first order ordinary differential equations (ODEs) to mixing problems and real-life applications. It includes two examples of using first order ODEs to model mixing problems, such as determining the amount of salt in a tank over time. It also applies Torricelli's law to model the outflow of water from a leaking tank and calculates the time until the tank empties. The document demonstrates setting up the mathematical models as first order ODEs, finding the general and particular solutions, and interpreting the solutions in the context of the problems.
The document discusses various characteristics of populations including:
1) Population density which measures the number of individuals per unit area or volume.
2) Population growth rate which is the rate at which a population increases each year as a percentage of the initial population.
3) Birth and death rates which measure the number of live births and deaths per 1,000 people in a population each year.
This document discusses key concepts in population ecology, including the five characteristics of a population: geographic distribution, density, dispersion, growth rate, and age structure. It provides examples and explanations of each characteristic. Geographic distribution describes the area inhabited by a population. Density is the number of individuals per unit area. Dispersion describes the spatial distribution of individuals as either clumped, even, or random. Growth rate measures changes in population size over time. Age structure diagrams show population breakdown by age and sex.
This document discusses life tables and their importance. It provides a brief history of life tables, noting their development by John Graunt and Edmund Halley. Life tables are useful for estimating survival rates, mortality rates, and other vital statistics. They allow calculation of life expectancy and comparison of mortality between populations. The document then describes how to construct a life table and provides examples of their use in clinical medicine using the Kaplan-Meier method.
Population ecology is the science that studies changes in population size and composition, and identifies factors causing these changes. A population consists of all individuals of a species in a given area, and is characterized by its size, density, dispersion, births, deaths, and survivorship over time. Population dynamics examines how and why population size changes, influenced by density-dependent factors like resources and disease, and density-independent factors like weather. Populations typically follow an S-shaped logistic growth curve as they approach the carrying capacity of their environment.
This document discusses population growth models, including exponential and logistic growth. Exponential growth occurs when resources are unlimited and the population grows rapidly in a J-shaped curve. Logistic growth occurs when resources are limited, causing the population to initially grow rapidly then slow down, reaching an asymptote as the carrying capacity is approached. Key factors that influence population growth are birth rates, death rates, immigration, emigration, food availability, and competition for limited resources. Governments have implemented controls to limit unsustainable human population growth.
Exponential and Logistics Growth Curve - Environmental ScienceNayan Dagliya
This document discusses exponential and logistic population growth models. Exponential growth occurs when a population's growth rate remains constant regardless of population size, producing a J-shaped curve. Logistic growth accounts for environmental constraints, with the growth rate decreasing as the population approaches the carrying capacity, producing an S-shaped curve. Key factors like resources, space, food and shelter determine a population's carrying capacity.
The document discusses various characteristics of populations including size, density, growth, and dispersion. It describes population growth models as exponential or logistic depending on whether resources are unlimited or limited by the environment's carrying capacity. The strategies for population growth are also covered, distinguishing between r-selected and K-selected types that thrive in changing versus stable environments.
Multiple regression analysis allows researchers to examine the relationship between one dependent or outcome variable and two or more independent or predictor variables. It extends simple linear regression to model more complex relationships. Stepwise regression is a technique that automates the process of building regression models by sequentially adding or removing variables based on statistical criteria. It begins with no variables in the model and adds variables one at a time based on their contribution to the model until none improve it significantly.
This document discusses various statistical parameters used in pharmaceutical research and development. It describes parameters like measures of central tendency (mean, median, mode), dispersion (variance, standard deviation), coefficient of dispersion, residuals, factor analysis, absolute error, mean absolute error, and percentage error of estimate. Measures of central tendency provide a summary of the central or typical values in a data set. Dispersion measures provide a way to quantify how spread out the data is from the central value. Other parameters like residuals, errors, and factor analysis are used to analyze relationships in complex data.
This document presents a method for predicting stream flow distributions based on climatic and geomorphic data alone, without discharge measurements. It combines a physically-based stream flow model with water balance and geomorphic recession flow models. Key parameters of the stream flow model are estimated from rainfall, potential evapotranspiration, and digital elevation model data. The method was tested on calibration and test catchments. While offering a unique approach, the method has limitations including additional assumptions and reduced accuracy of parameter estimates and flow regime predictions.
This slideshow was created for the VCE Environmental Science Online Course, Unit 3: Biodiversity. It explains different methods of assessing biodiversity and discusses several indices for measurement.
Two species when occupy in same habitat accumulating same resource in same manner then competition is inevitable. The normal logistic growth is not expected. Lotka and Volterra proposed equation to describe the interspecific competition among the species. Either one of the species wins other is excluded or they co-exist in unstable or stable manner.
The document defines key terms related to metapopulation dynamics. It describes metapopulations as populations composed of spatially discrete local populations between which migration is limited. Local populations exist in habitat patches surrounded by unsuitable matrix. Metapopulations are characterized by local extinction and recolonization of vacant patches over time. If extinctions exceed recolonizations, the entire metapopulation may go regionally extinct. The document outlines different types of spatially dynamic populations and models used to study metapopulation dynamics.
The document summarizes the Lotka-Volterra predator-prey model. It describes how the model was independently developed by Lotka and Volterra to explain predator-prey population dynamics over time. The model uses differential equations that account for the growth of the prey population and the growth and death of the predator population. The model assumes exponential growth of prey in the absence of predators and proportional rates of predation and predator growth. The model predicts stable population cycles or equilibrium points where populations remain balanced. However, the model has limitations and does not reflect real-world population crashes or extinctions that can occur.
This document presents information on natality, related demographic terms, and measures of fertility. It defines natality as the birth rate, or number of births per 1000 people per year. It also discusses related concepts like mortality, fertility, and factors that affect birth rates. The document focuses on common measures used to quantify fertility, including the crude birth rate, general fertility rate, age-specific fertility rate, total fertility rate, gross reproduction rate, and net reproduction rate. It provides formulas and examples for calculating each measure and describes how they are used to analyze population changes over time.
This document defines key concepts in population ecology, including population, population ecology, population size, density, dispersion, structure, growth, pyramids, demography, and survivorship curves. It explains that population ecology studies how biotic and abiotic factors influence population density, dispersion, and size. It also describes different population structures, growth patterns, survivorship curves that reflect varying reproductive strategies and death rates at different ages among species.
This document discusses key concepts related to population growth and dynamics. It defines what a population is and describes demography as the statistical study of populations. Population size, density, and dispersion are identified as key features of populations. Crude density and ecological density are important indexes used to describe populations. Methods for estimating population densities include mark-recapture and minimum known alive techniques. The concepts of growth rate, sources and sinks, and meta-populations are also introduced. Population dispersion patterns like regular, random, clumped and regular clumped are described. Finally, the document discusses age structure and different types of age pyramids in populations.
The chi-square test is used to compare observed data with expected data. It was developed by Karl Pearson in 1900. The chi-square test calculates the sum of the squares of the differences between the observed and expected frequencies divided by the expected frequency. The chi-square value is then compared to a critical value to determine if there is a significant difference between the observed and expected results. The degrees of freedom, which determine the critical value, are calculated based on the number of rows and columns in a contingency table. The chi-square test can be used to test goodness of fit, independence of attributes, and other hypotheses.
This document provides examples of using differential equations to model physical systems involving flow processes. Example 2 models the flow of salt into and out of a tank, with the goal of finding the salt concentration over time. Example 3 models the flow of a toxic waste into a pond, with a time-varying inflow concentration, to determine the amount of waste in the pond over time. The examples illustrate setting up the initial value problem, finding the solution, and discussing assumptions and limitations of the mathematical model.
This document provides an overview of applying first order ordinary differential equations (ODEs) to mixing problems and real-life applications. It includes two examples of using first order ODEs to model mixing problems, such as determining the amount of salt in a tank over time. It also applies Torricelli's law to model the outflow of water from a leaking tank and calculates the time until the tank empties. The document demonstrates setting up the mathematical models as first order ODEs, finding the general and particular solutions, and interpreting the solutions in the context of the problems.
This document provides an overview of engineering mathematics II with a focus on first order ordinary differential equations (ODEs). It explains what first order ODEs are, how to solve separable and reducible first order ODEs, and provides examples of applying first order ODEs to model real-world scenarios like population growth, decay, and radioactive decay. The objectives are to explain first order ODEs, separable equations, and apply the concepts to real life applications.
Model of Differential Equation for Genetic Algorithm with Neural Network (GAN...Sarvesh Kumar
The work is carried on the application of differential equation (DE) and its computational technique of genetic algorithm and neural (GANN) in C#, which is frequently used in globalised world by human wings. Diagrammatical and flow chart presentation is the major concerned for easy undertaking of these two concepts with indication of its present and future application is the new initiative taken in this paper along with computational approaches in C#. Little observation has been also pointed during working, functioning and development process of above algorithm in C# under given boundary value condition of DE for genetic and neural. Operations of fitness function and Genetic operations were completed for behavioural transmission of chromosome.
The document discusses fundamental matrices and their properties. A fundamental matrix Ψ(t) is a matrix whose columns are fundamental solutions to the system x' = P(t)x. Ψ(t) satisfies the differential equation Ψ' = P(t)Ψ and is nonsingular. The general solution to the system can be written as x = Ψ(t)c, where c is a constant vector. For an initial value problem, the solution is x = Ψ(t)Ψ-1(t0)x0. The fundamental matrix Φ(t) corresponding to a set of fundamental solutions satisfying initial conditions is also discussed. Matrix exponential functions are introduced as the fundamental matrix
The document discusses differential equations and their use in mathematical modeling of physical phenomena. It provides examples of differential equations describing free fall with air resistance, mouse and owl populations, and water pollution. Direction fields are used to graphically analyze solution behaviors and equilibrium solutions for various differential equations.
Pursuing any development or neighborhood plan today involves
working with a myriad of actors beyond professional collaborators
during planning and design phases. These include direct abutters,
surrounding neighbors, elected offi cials, public agencies, opponents
(often), investors, financial institutions, and regulators, all billed as
“stakeholders.” Navigating the shoals created by cadres of stakeholders
is perhaps the greatest challenge to pursuing sophisticated
ideas about and goals for urbanism.
Consensus around goals that arenot very ambitious is, unfortunately, common. However, rather thanwallow in despair about the unpredictable nature of decentralized processes, urban designers must learn to be more effective collaborators,willing participants in true interdisciplinary endeavors, and advocatesfor ideas not always their own, ideas that have the potential
to rally others around higher expectations, not expedient solutions.
Such skills are not always available in a designer’s tool kit.
Some blame the messiness of democratized processes for producing mediocrity.
On the other hand, many can offer examples of substantial benefits to projects as a result of broader community participation.
Then, too, there is that maxim among seasoned urban designers, “To
envision takes talent, to implement takes genius.”
This document defines various microbiology terms related to sterilization, disinfection, and antimicrobial agents. It discusses physical agents like heat and radiation that are used for sterilization. It also covers chemical agents' modes of action and factors affecting their antimicrobial activity. The document highlights the historical development of antibiotics and mechanisms of antibiotic resistance development. It compares antimicrobial activity in vivo and in vitro.
The document discusses matrices and their applications. It begins by defining what a matrix is and some basic matrix operations like addition, scalar multiplication, and transpose. It then discusses matrix multiplication and how it can be used to represent systems of linear equations. The document lists several applications of matrices, including representing graphs, transformations in computer graphics, solving systems of linear equations, cryptography, and secret communication methods like steganography. It provides some high-level details about using matrices for secret codes and hiding messages in digital files like images and audio.
The document discusses various units of measurement for length, volume, mass, and temperature in both the metric and imperial systems. It provides examples to convert between units and explains how to measure quantities using tools like rulers, graduated cylinders, balances, and thermometers. Key metric units include meters, centimeters, millimeters, liters, milliliters, grams, and degrees Celsius.
The document discusses various topics related to hydronic system design including:
- Common hydronic system types like primary-secondary and variable flow systems
- Key considerations for piping design like pump sizing, pressure drops, and expansion tank placement
- Examples of specific system designs for chilled water, boiler water, and complex multi-building systems
- Benefits of variable speed pumps for energy efficiency and system controllability
An Empirical Study of the Environmental Kuznets Curve for Environment Quality...ijceronline
This paper attempts to examine the determinants of environmental degradation within the framework of Environment Kuznets Curve (EKC) hypothesis using China's city-level panel data from 2003 to 2012. The population agglomeration as well as three types of cities such as municipalities, sub-provincial city and prefecture-level city are considered in our paper. Our empirical results with the whole sample data verified the theory of the EKC hypothesis, which shows a reverse "U" shape between economic growth and environmental pollution. In addition, the effect of population on environmental pollution is quite different among the various types of cities. The results of this study can serve as a useful reference for policy makers in terms of achieving economic and environmental sustainability.
1) The document discusses the role of demographic dividend in China's economic development from 1978 to 2013. It builds an economic model to test the relationship between economic growth, age structure, labor force, education and other factors.
2) Regression analysis based on the model finds that changes in age structure and capital stock had a positive effect on economic growth, while previous GDP per capita negatively impacted current growth. However, changes in education levels did not significantly influence growth.
3) Additional regressions introducing factors like international trade, urbanization and legal progress found some of these played a more important role in China's development than the demographic dividend alone. Forecasts are made for China's annual growth from 2014 to 2050 based on
1) The document discusses the role of demographic dividend in China's economic development from 1978 to 2013. It builds an economic model to test the relationship between economic growth, age structure, labor force, education and other factors.
2) Regression analysis based on the model finds that changes in age structure and capital stock had a positive effect on economic growth, while previous GDP per capita negatively impacted current growth. However, changes in education levels did not significantly influence growth.
3) Additional regressions introducing factors like international trade, urbanization and legal progress found some of these played a more important role in China's development than the demographic dividend alone. Forecasts are made for China's annual growth from 2014 to 2050 based on
This document discusses various methods for population forecasting that can be used by urban planners, including arithmetic increase, geometric increase, incremental increase, graphical, and logistic curve methods. It provides examples of applying these methods to forecast populations for the cities of Ahmedabad, Surat, and Vadodara in India for the years 2011, 2021, and 2031 based on past census data. Accurate population forecasting is important for urban planning purposes such as developing housing, employment, water and sanitation infrastructure, transportation, and recreational facilities for future population needs.
This document discusses how rapid urbanization in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) has impacted natural habitats and key ecosystem services over the period of 2000-2018. The author analyzes spatial and temporal data on land use/cover, population density, GDP, vegetation indices and other biophysical factors to assess changes. The results show that urbanization generally led to declines in ecosystem services, with negative spatial correlations between urbanization and services. However, relationships varied in urban and rural areas, and an overall trend of weak decoupling was observed as urbanization increased. The findings provide insights for sustainable urban planning and ecosystem protection in megaregions experiencing rapid development.
This document provides a climate change vulnerability assessment for Pakse, Lao People's Democratic Republic. It analyzes the city's exposure, sensitivity, and adaptive capacity to climate change. Key findings include:
- Pakse experiences a tropical climate and heavy seasonal rainfall that is projected to intensify, with wet seasons getting wetter and dry seasons drier. Flooding is already a frequent problem.
- The population and infrastructure are highly sensitive to climate impacts like flooding and drought. Livelihoods dependent on agriculture and tourism are also at risk.
- Adaptive capacity is limited by institutional, financial, and community-level constraints, though some flood defenses have been implemented.
Global demographic trends and future carbon emissions o neill et al_pnas_2010...Adnan Ahmed
This document summarizes a study that analyzed the implications of future demographic trends on global carbon emissions through 2100. Using an energy-economic model called PET, the study found that:
1) Slowing population growth could provide 16-29% of the emissions reductions suggested to avoid dangerous climate change by 2050.
2) Aging populations can substantially reduce emissions in some regions by up to 20% due to lower labor participation rates, while urbanization can increase emissions over 25% from higher productivity.
3) At a global level, the effects of changes in population composition like aging and urbanization are offsetting, but urbanization is a dominant driver of increased emissions in developing countries like China and India.
Population growth and increases in carbon dioxide emissions both rose significantly from 1980 to 2009. While a positive correlation exists between the two, statistical analysis shows the relationship is weak. The chi-square test results fail to reject the null hypothesis that population growth and carbon emissions are independent. Therefore, while human activities like energy use that produce carbon emissions rise with growing populations, population growth alone does not sufficiently explain increasing carbon dioxide levels according to this analysis. Other factors must also influence annual carbon emission amounts.
This document provides an introduction to the concept of an urban nexus. It discusses how cities are major centers of population and economic activity but also significant contributors to resource consumption and environmental impacts. The document then explores the concept of a water-energy-food nexus and how this relates to urban areas. It reviews different definitions and perspectives on an urban nexus. The overall aim is to develop a conceptual framework for understanding the urban nexus and how it can align with global agendas around sustainable development and urban issues.
Human reproduction planning is the practice of intentionally controlling the rate of growth of a human population. Historically, human population planning has been implemented with the goal of increasing the rate of human population growth. However, in the period from the 1950s to the 1980s, concerns about global population growth and its effects on poverty, environmental degradation and political stability led to efforts to reduce human population growth rates. More recently, some countries, such as China, Iran, and Spain, have begun efforts to increase their birth rates once again. While population planning can involve measures that improve people's lives by giving them greater control of their reproduction, a few programs, most notably the Chinese government's "one-child policy and two-child policy", have resorted to coercive measures.
Heterogeneity and scale of sustainable development in citiesJonathan Dunnemann
"Rapid worldwide urbanization is at once the main cause and, potentially, the main solution to global sustainable development challenges.Thegrowthofcitiesistypicallyassociatedwithincreases insocioeconomic productivity, but it alsocreates stronginequalities."
Development Potential: The Joint Influence of High Population Growth and a ...Anna McCreery
This document summarizes a study examining how population growth and economic strength influence a country's potential for future economic growth. A preliminary analysis found that higher population growth, a lower percentage of the population dependent on agriculture, and stronger trade balances were associated with higher GDP per capita. Countries were then classified based on population growth rates and economic conditions. Four maps were created from GIS data to visualize geographic patterns between these variables and analyze spatial autocorrelation between countries' attributes.
Population Dynamics Lab ReportUse the following formula to c.docxharrisonhoward80223
Population Dynamics
Lab Report
Use the following formula to complete the charts below: pf = pi * ert
Where:
pf = final population
pi = initial population
e = a physical constant whose value is 2.7183
r = rate of growth
t = time (doubling time)
Change the rate of growth into a decimal by dividing by 100.
Use either your calculator that has an ex function or the calculator found on the following website: http://www.math.com/students/calculators/source/scientific.htm
Example:
pi = 5.2 X 109 (initial population of 5.2 billion people in developing countries)
t = 39 years (from table 1)
r = 1.8% (from table 1)
r = 1.8% = 0.018
Multiply r and t 0.018 * 39 = 0.702
Pf = 5.2 * (e0.702)
On calculator, enter 0.702, then INV, then ex
Pf = 5.2 * (2.02)
Pf = 10.49 or 10.5 X 109
Or 10.5 billion people
Table 1: Growth Rates and Doubling Times for Various Countries
Region
Growth Rate (%)
Doubling Time (years)
World
1.4
50
Developed Countries
0.4
175
Developing Countries
1.8
39
Africa
2.5
28
Asia
1.6
44
United States
1.0
70
Mexico
1.7
41
Europe
0.2
350
Russia
0.3
233
Oceania
1.5
47
Exercise One:
Part A: Using information from table 1, fill in the chart below and then calculate the final population for each.
Part B: Using information from table 1, fill in Part B of the chart but use the developed countries’ doubling time.
Region
r (%)
dt (years)
Pi (X 109)
Pf (X 109)
A
Developing
4.7
Developed
1.2
United States
0.303
Mexico
0.107
Africa
0.048
B
Developing
**
4.7
**Use doubling time of developed countries
Exercise Two:
Calculate the final population for developed nations where (r) starts at 0.6 and decreases by 0.1 percent every ten years until (r) = 0.0 percent (ZPG). The final population becomes the initial population for the next ten year period.
r (%)
t (years)
Pi (X 109)
Pf (X 109)
0.6
10
1.2
0.5
10
0.4
10
0.3
10
0.2
10
0.1
10
0.0
10
Calculate the final population for developing nations where (r) starts at 2.0 percent and decreases by 0.4 percent every ten years until (r) = 0.0 percent (ZPG). Remember, the final population becomes the initial population for the next ten years.
r (%)
t (years)
Pi (X 109)
Pf (X 109)
2.0
10
4.7
1.6
10
1.2
10
0.8
10
0.4
10
0.2
10
0.0
10
Using information from exercise one, answer the following questions.
1. Which country/region (do not consider the first three lines of information) has the highest growth rate? The lowest? How do you account for this difference?
2. Why do some countries/regions have a shorter or lower doubling time?
3. What would happen to the final population of developing countries if their growth rate is maintained over a developed countries doubling time?
Using information from exercise two, answer the following questions:
1. How do the final populations of developed regions and developing regions compare when zero population growth is reached?
2. Why were the growth rates used in this exercise differen.
Population Forecasting Methods
Population Forecasting consists of mathematical models which are used to analyse changes in population numbers.
There are several factors affecting changes in population:
Increase due to births
Decrease due to deaths
Increase/Decrease due to migration
Increase due to annexation
All the above data can be obtained from the census population records.
Population forecasting is an integral part of design. It is essential to take into account the population at the end of the design period.
Fundamental to planning (Assumptions and estimates used in determining water, sewage flow have a permanent effect on planning decisions and outcomes)
Premature and excessive investments in works
System failure and hence increasing customer complaints
Environmental impact
Essential to service provider so as to know the spare capacity of the system
Identification of weak links of system, Ability to accept new/unexpected demands
BY BAJKANI UWAIS {MUET
This document analyzes quality of life indicators for G20 countries using statistical analysis methods. It introduces 8 quality of life indicators such as CO2 emissions, health expenditure, and education spending. A correlation matrix shows moderate correlations between some indicators. Regression, factor analysis, and cluster analysis are used to investigate relationships between indicators and group countries based on similarities in quality of life. The analysis finds countries can be grouped according to their quality of life profiles.
This document analyzes quality of life indicators for G20 countries using statistical analysis methods. It introduces 8 quality of life indicators such as CO2 emissions, health expenditure, and education spending. A correlation matrix shows some moderate correlations between indicators. Regression, factor analysis, and cluster analysis are used to investigate relationships between indicators and group countries based on similarities in quality of life. The analysis finds some countries have high quality of life due to factors like education levels and environmental protection.
The document discusses exponential population growth and projections. It notes that the world's population is growing at 1.11% annually, corresponding to a doubling time of about 60 years. Using this rate of growth, the document projects that global population could reach 8 trillion by 2600 CE and 8 quadrillion by 3200 CE, representing a millionfold increase over today's population. It cautions that growth in economic activity, resource use, and energy consumption follows similar exponential curves, calling into question the concept of indefinite "sustainable growth." Recommended additional resources on mathematical aspects of growth are provided.
The document discusses water demand forecasting and population forecasting methods. It describes calculating total annual water demand, average daily flow rates, and per capita demand. It also outlines factors that affect per capita demand and reasons for selecting a design period. The document then discusses various population forecasting methods like arithmetic increase, geometric increase, incremental increase, and graphical methods. It provides formulas and explanations for different curve fitting techniques to extrapolate population projections, including linear, geometric, parabolic, modified exponential, Gompertz, and logistic curves.
Project #4 Urban Population Dynamics This project will acquaint y.pdfanandinternational01
Project #4: Urban Population Dynamics This project will acquaint you with population
modeling and how linear algebra tools may be used to study it. Background Kolman, pages
305-307. Population modeling is useful from many different perspectives: planners at the city,
state, and national level who look at human populations and need forecasts of populations in
order to do planning for future needs. These future needs include housing, schools, care for the
elderly, jobs, and utilities such as electricity,water and transportation. businesses do population
planning so as to predict how the portions of the population that use their product will be
changing. Ecologists use population models to study ecological systems, especially those where
endangered species are involved so as to try to find measures that will restore the population.
medical researchers treat microorganisms and viruses as populations and seek to understand the
dynamics of their populations; especially why some thrive in certain environments but don\'t in
others. In human situations, it is normal to take intervals of 10 years as the census is taken every
10 years. Thus the age groups would be 0-9,10-19,11-20 etc , so 8 or 9 age categories would
probably be appropriate. The survival fractions would then show the fraction of \"newborns\" (0-
9) who survive to age 10, the fraction of 10 to 19 year olds who survive to 20 etc. This type of
data is compiled, for example, by actuaries working for insurance companies for life and medical
insurance purposes. The basic equations we begin with are (1) x(k+1) = Ax(k) k=0,1,2,. . . and
x(0) given with solution found iteratively to be (2) x(k) = Akx(0) (see Kolman for details of the
structure of A, which is 7 x 7 in this case). Your Project Suppose we are studying the
population dynamics of Los Angeles for the purpose of making a planning proposal to the city
which will form the basis for predicting school, transportation, housing, water, and electrical
needs for the years from 2000 on. As above, we take the unit of time to be 10 years, and take 7
age groups: 0-9,10-19,...,50-59,60+. Suppose further that the population distribution as of 1990
(the last census) is (3.1, 2.8, 2.0, 2.5, 2.0, 1.8, 2.9) (x105 ) and that the Leslie matrix,A, for this
model appears as Part One: Interpret carefully each of the nonzero terms in the matrix. In
addition, indicate what factors you think might change those numbers (they might be social,
economical, political or environmental). Part Two: Predict: what the population distribution
will look like in 2000, 2010, 2020 and 2030 what the total population will be in each of those
years by what fraction the total population changed each year Additionally, what does your
software tell you the largest, positive eigenvalue of A is? Part Three: Decide if you believe the
population is going to zero, becoming stable, or is unstable in the long run. Be sure and describe
in your write up how you arrived at your conclusion. If.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
Climate Impact of Software Testing at Nordic Testing DaysKari Kakkonen
My slides at Nordic Testing Days 6.6.2024
Climate impact / sustainability of software testing discussed on the talk. ICT and testing must carry their part of global responsibility to help with the climat warming. We can minimize the carbon footprint but we can also have a carbon handprint, a positive impact on the climate. Quality characteristics can be added with sustainability, and then measured continuously. Test environments can be used less, and in smaller scale and on demand. Test techniques can be used in optimizing or minimizing number of tests. Test automation can be used to speed up testing.
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.
Unlocking Productivity: Leveraging the Potential of Copilot in Microsoft 365, a presentation by Christoforos Vlachos, Senior Solutions Manager – Modern Workplace, Uni Systems
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
UiPath Test Automation using UiPath Test Suite series, part 5DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 5. In this session, we will cover CI/CD with devops.
Topics covered:
CI/CD with in UiPath
End-to-end overview of CI/CD pipeline with Azure devops
Speaker:
Lyndsey Byblow, Test Suite Sales Engineer @ UiPath, Inc.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
Pushing the limits of ePRTC: 100ns holdover for 100 daysAdtran
At WSTS 2024, Alon Stern explored the topic of parametric holdover and explained how recent research findings can be implemented in real-world PNT networks to achieve 100 nanoseconds of accuracy for up to 100 days.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Building Production Ready Search Pipelines with Spark and MilvusZilliz
Spark is the widely used ETL tool for processing, indexing and ingesting data to serving stack for search. Milvus is the production-ready open-source vector database. In this talk we will show how to use Spark to process unstructured data to extract vector representations, and push the vectors to Milvus vector database for search serving.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
In his public lecture, Christian Timmerer provides insights into the fascinating history of video streaming, starting from its humble beginnings before YouTube to the groundbreaking technologies that now dominate platforms like Netflix and ORF ON. Timmerer also presents provocative contributions of his own that have significantly influenced the industry. He concludes by looking at future challenges and invites the audience to join in a discussion.
1. INTRODUCTION TO ENGINEERING
AND THE ENVIRONMENT
Edward S. Rubin
Cnrlzegie Mrllnn University
with Cliff I. Davidson
and other contributors
Boston Burr Ridge, lL Dubuque, lA Madison, WI New York
San Francisco St. Louis Bangkok Bogota Caracas Kuala Lumpur
Lisbon London Madrid MexicoCity Milan Montreal NewDelhi
Santiago Seoul Singapore Sydney Taipei Toronto
2. -
CHAPTER 15 Environmental Forecasting
Population
growth
Changes ill Changes in
j growth environme~ltal
i~npacta
Technologq
change
Figure 15.1 Basic elements of environmental forecasting, illustrating the three main "drivers" of
population growth, economic activity, and technological change: Future changes
in environmental impacts also can feed back to and affect these processes.
reflect our current understanding of how the world works based on principles of
physics, biology, and chemistry. Many of these science-based models of environ-
mental processes also are dynamic, meaning they can predict how factors like pol-
lutant concentrations will change over time in response to a specified input or stim-
ulus such as an increase or decrease in emissions from human activities.
The study of environmental engineering and science focuses mainly on develop-
ing the science-based process models just described. Such models are essential for
predicting the environmental conscqucnces of changes in anthropogenic emissions,
both now and in the future. In contrast, the study of population growth, economic
activity, and technological change lies primarily in fields of the social sciences, where
mathematical models also are used for prediction. Figure 15.1 illustrates some of the
links between these social science models (reflecting human behavior) and the phys-
ical science models of environmental processes. Both types of models are important
for environmental forecasting. The social science models emphasized in this chapter
provide a broader perspective on the factors affecting environmental futures.
15.4 POPULATION GROWTH MODELS
According to the United Nations, the world's population reached 6 billion people on
October 12, 1999. The trend in world population growth through the end of the 20th
century A.D. is shown in Figure 15.2. Over the past 100 years, the world population has
quadrupled. Although it took thousands of years for the population to grow to 1 bil-
lion, the last billion people arrived in only 13 years! Several billion new neighbors are
expected to join us on the planet over the next several decades. Because population
growth is a major determinant of environmental impacts, we look first at how such
growth can be expressed in mathematical tenns and used for environmental modeling.
3. PART 4 Topics in Environmental Policy Analysis
7.000
6 billion on
1000 500 o 500 1000 1500 2000
BC RC
Year (AD)
Figure 15.2 World population growth, 1000 B.C. to 2000
A . D . (Source: Based on USDOC, 19990)
Depending on the time frame and geographic region of concern, one could envi-
sion a broad array of population trajectories, as illustrated in Figure 15.3. These include
populations that grow quickly or slowly, as well as populations that stabilize, or that
decline over time. The scope and purpose of an environmental analysis play a large role
in defining the importance and type of population prqjections needed.
Each curve or trajectory in Figure 15.3 can be represented by an equation or math-
ematical model. Indeed, virtually any path that can be envisioned can be represented
numerically in an environmental forecast or scenario. The next sections present a set
of population growth models that span a range of complexity. The parameters govern-
ing the behavior of each model represent the variables of an environmental forecast or
scenario. The value of key variables is often guided by analysis of historical data, but
it is up to the analyst to specify how these parameters might change in the future.
1
15.4.1 Annual Growth Rate Model #
1
One of the simplest and most common ways to quantify the growth of a population
growth mte, r; expressed either as a percentage or as
is to assume a corzstont ariril~al I
a fraction. For example, if a population grows at a rate of 2 percent per year, next
year's population will be 1.02 times greater than this year's population, and the fol-
lowing year it will be 1.02 times greater than that (an overall increase of 2.04 per-
4. CHAPTER 15 Environmental Forecasting
Present
Time - Future
Figure 15.3 Possible trajectories of population change.
cent). If the annual growth rate, r, is expressed as a fraction (such as 0.02), a general
expression for the total population, after t years is
P = Po (1 + r)' [15.1]
where Pu is the initial population at the time t = 0.
This equation has the identical form as the compound annual interest equation
used in engineering economics to calculate monetary growth (see Chapter 13j. The key
characteristic of this equation is a nonlinear increase in the total quantity over time (be
it population, money, or any other quantity that grows at a constant annual rate). Fig-
ure 15.4 illustrates thls trend for three different rates of annual population growth. The
higher the annual growth rate, the more dramatic the rise in population over time.
0
0 10 20 30 40 50
Time (years)
Figure 15.4 Population increase for three annual growth rates based on a
compound annual growth model.
5. 642 PART 4 Topics in Environmental Policy Analysis
Example 15.1
Population growth of an urban area. The current population of an urban area is 1 million
people. The region has experienced rapid growth at an annual rate of 7 percentlyear. City plan-
ners and environmental officials anticipate that this annual growth rate will continue for the
next 10 years. If so, what would the population be 10 years from now'?
Solution:
Assuming a constant annual growth rate, use Equation (15.11 with PC,= 1 million, r = 0.07.
and t = 10 years:
Thus the population would double in 10 years at a 7 percentlyear rate of growth.
The implication of a compound annual growth model is an ever-rising popula-
tion. This type of model is frequently used in environmental forecasts or scenarios
to estimate future environmental emissions from human activity. The simplest types
of projections use population figures together with per capita measures of environ-
mental impact, as in the next example.
Example 15.2
Projected growth in municipal solid waste. Pleasantville is a city of 100,000 people that cur-
rently collects 8 X lo7 kg (80,000 metric tons) of municipal solid waste (MSW) each year.
The waste is disposed of in a sanitary landfill the city owns. Based on recent trends, the city's
population is projected to grow at a rate of 3 percentlyear over the next 15 years. Assuming
that per capita waste production remains constant over this period. how much additior~nl waste
will the city have to collect and dispose of annually 15 years from now?
Solution:
First use Equation (15.1) to calculate the future population of the city 15 years from now,
based on the 3 percentlyear annual growth rate:
P = 100,000(1.03)~~ 155,800 people
=
The number of additional people is therefore
Added population = 155,800 - 100,000 = 55,800 people
The current annual waste generation per capita is
8 X 107kg
MSWlperson = -- = 800 kglperson-yr
100.000 people
Assuming this rate remains constant. the total additional waste generated 15 years from now
would be
Additional waste = (55,800 people) X (800kglperson-yr)
= 4.5 X lo7 kg/yr
= 45,000 metric tons
6. CHAPTER 15 Environmental Forecasting
Estimates of this sort may be used to anticipate the magnitude of future environ-
mental problcms. such as the need for additional landfill area or alternative methods
of waste disposal. Because the future is always uncertain, a good analysis also
employs a range of assumptions for key parameters that affect the outcomes of inter-
est. In this case, both the annual population growth rate and the amount of waste gen-
erated per person should be treated as uncertain. Similarly. different growth rates
might apply to diflerent time periods, yielding a nulltiperiod growth model. Problems
at the end of the chapter include examples that illustrate these types of projections.
15.4.2 Exponential Growth Model
The annual growth models just described assunle that population increases occur in
annual spurts at the end of each year. a process known as compound annual growth.
This works well for compound annual interest added to a bank account, but a more
realistic model for population increases would be continuol~s.This can be modeled
by shortening the time period for compound growth from annual to continuous. The
growth:
result is an alternative model of pure e.xporler~/icrl
P = Po e'" (15.2)
This equation is based on the assumption that at any point in time the rate of
change in population is proportional to the total population at that moment. Mathe-
matically, this can be written as
where the prvportionality constant, r is the growth rate expressed as a fraction of thc
current population. The solution to this differential equation is Equation 115.2).
where Po is the original population at time r = 0.
Figure 15.5 compares the exponential growth model of Equation (15.2) with the
coinpound annual growth model of Equation (15.1). As you can see. the two models
givc very sirnilar results for low values of growth rates and time periods. But as r a n d t
increase, thcre is greater divergence, with the exponential model growing more rapidly.
Example 15.3
Exponential versus compound annual growth. In Example 15.2 the population of Plcasantville
was assumcd to grow at a compound annual rate of 3 percentlyear for 15 years. Suppose instead
that an exponential growth model had been used based on the same 3 pcrcent growth rate. How
would ihis change the estimate of Plea~antville's
population 15 years from now.?
Solution:
Use Equation (15.2) with P:, = 100.000 peuple, r = O.Oj/yr, and t = 15 years:
This compares to 155,800 people using the cu~npounii annual growth model. The diffcl-cnce
in this case is less than 1 percent. or an additional 1,000 people, assuming exponential growth.
7. PART 4 Topics in Environmental Policy Analysis
Exponential growth
Compound annual growth
Figure 15.5 Comparison of two simple growth models.
Increases in the world's population over the past 5,000 years resemble an expo-
j
nential growth function.' Over the past 100 years the rate of increase has been
approximately 1.3 percentlyear. We know. however, that exponential growth cannot
continue indefinitely. In an environment with finite space and finite resources to sup-
port the needs of a population, growth eventually is curtailed. The next section pre-
sents a mathematical model that exhibits such characteristics.
15.4.3 Logistic Growth Model
Biologists have found that the population growth of many living organisms tends to
follow an S-shaped curve like the one sketched in Figure 15.6, a shape known as sig-
moidal. Initially the population begins to grow exponentially, but over time the
growth rate gradually slows until it finally reaches zero. At that point the population
stabilizes at the limit labeled P,,,, in Figure 15.6. This limit is known as the carry-
ing capcrcih of the environment. It defines an equilibrium condition in which the
total demands of the population for food, water, waste disposal, and natural resources
are in balance with the capability of the environment to supply those needs. That bal-
ance defines a stable level of population with no further growth. The result is known
as a logistic growth curve, represented by the sketch in Figure 15.6.
The leveling-off phenomenon in a logistic growth model represents a resistance
to further growth as the population nears the carrying capacity of the environment.
Mathematically, this can be represented by adding an "environmental resistance"
term to the simple exponential growth model of Equation (15.3):
1 1 Of course, over shorter periods and on smaller geographic scales, the population varies more erratically,
especially due to catastrophes such as wars or famine.
8. CHAPTER 15 Environmcntal Porecastirlg
Population
I Time
Figure 15.6 A logistic growth curve showing the characteristic S-shaped profile. Po is the
initial population and t, is the time needed to reach half of the carrying
capacity, P,,.
Now instead o l population growth being proportional only to the current popu-
lation, it dcpcnds also on the size of the current population relative to the carrying
capacity. P,,,,,,. When thc current population is small relative to the cax~yingcapac-
ity, the negative (resistance) tcrm in Equation (15.4) is also small, and we have an
exponential grouath model as beforc. But as P gets larger and approaches PI,,,,, the
environmental resistance increases and thc term in brackets approaches zero. The
population growth rate (dPldt) also then goes to zcro. Between these two extremes
the trend in total population transitions from an upward-bound curve to a horizontal
asymptote. producing the S-shaped curve of a logistic growth modcl. Mathemati-
cally, the solution to Equation (15.4) is
'The growth rate, r; in this equation represents a composite growth rate aver the
sigmoidal shape of the logistic curve, so the value of r differs from that of the sim-
ple exponential growth model shown earlier. The two rates can be related if we
deline an initial exponential growth rate. r,, associated with an initial population. Po,
at ti~ile = 0. Then it can be shown that
r
Similarly, we can show that the constant t,, in Equation (15.5) represents the
time at which the population rcaches hulfthe carrying capacity (that is, the midpoint
of the growth curve). Thus
a t t = t,,,, P = ! P r,ial
? (157 )
9. PART 4 Topics in Environmental Policy Analysis
By manipulating Equation (1 5.5) we can further show that t,, and r are related by
A numerical example best illustrates the use of a logistic growth model for pop-
ulation projections.
Example 15.4
Estimating the world population in 2100. Assume that world population growth can be
described by a logistic zrowth model with a carrying capacity of 20 billion people. Estimate
the global population in 2100 based on a current population of 6 billion in 2000 with an expo-
nential growth rate of 1.5 percent. How would your answer differ if the carrying capacity were
15 billion people?
nz
Solution: iz,
The desired population can be found using Equation (15.5) with t = 100 years. But first we ar,
must find the logistic growth rate. K and the midpoint time, t,,,. From Equation (15.6). an
trj
trj
Im
rat
From Equation (15.8).
be1
Substituting these results into Equation (15.5) gives Th
the
p,,,,, - 20
P =
1 + r - r ,
-
I ~ 15.7 billion
+ e - ~ , ~ ? ~ . ' i ~ ~ o - 3=. 5 ) aP
This is the projected population 100 years from now, assuming a carrying capacity of 20
billion people. The value of t,, = 39.5 years indicates that a population of 10 billion people
(half the carrying capacity) will be reached about 40 years from now. Repeating the calcula- Estir
tion for a global carrying capacity of 15 billion (instead of 20 billion), the estimated popula- a crI
tion in 2100 would be 13.4 billion people. By either estimate the world's population would a nc
more than double over the next 100 years based on these assumptions. perc
Solu
Bec;
prob
Logistic growth models are appealing because they reflect the type of long-term
growth patterns that have actually been observed for n~icroorganisms,insects, and
other life forms. A logistic model further offers a simple way of representing a long-
term limit to growth and a gradual stabilization of the population. For human popu- !
lations, however, logistic models have been less successful in predicting carrying
capacity and growth rates in the past. Rather. the value of carrying capacity seems to
be a moving target that changes over time. The unique human capability for techno- 2
i
On a
logical innovation has led to developments such as modern medicine and fertilizers -
10. CHAPTER I5 Environ~nent;~l
Forecasting
for food production that continue to alter the apparent limit to global population.
Thus a sinlple lvgistic model affords only a rough approximation based on assump-
tions about key parameters. Because of the availability of detailed data on actual
population characteristics, other types of models are more commonly used to project
future population growth, as discussed next.
15.4.4 Demographic Models
Dernographj is the study of the characteristics of human populations, including their
size, age, gender. geographic distribution, and other statistics. The wealth of data on
population characteristics allows much more detailed models to be developed for
population projections in lieu of the models discussed so far.
Population statistics are most commonly collected. analyzed, and reported on a
national basis by individual countries, private organizations, and international organ-
izations like the United Nations. The basic data needed for population projections
are currcnt rates of births and deaths. The difference between these two rates gives
an approximate mcasure of the vverall population growth rate. In addition, a coun-
If
try may gain or lose population via ~nigration. more people regularly enter a coun-
try than leave, there is a net incrcase in the overall population growth rate. If the net
immigration rate is negative (more people leaving than entering): the overall growth
rate is reduced. In general, we can write
Growth rate = (Birth rate) - (Death rate) + (Immigration rate) (15.9)
The rate4 in Equation (1 5.9) are typically quantified in terms of the annual num-
bers of births, deaths, and immigrants per 1,000 people in the overall population.
These overall statistics are referred to as the crude rates, which means they apply to
the population as a whole. as oppoqed to specific segments of the population such as
a particular age group.
Example 15.5
Estimating populationgrowth rate. A country with a total population of 50 million people has
a crude birth rate of 20 births per year per 1.000 people, a crude death rate of 9 per 1.000, and
a net immigration rate of I per 1.000. What is the net population growth rate expressed as a
percentage of thc total populatiuri?
Solution:
Bccause we are interested only in rates. the absolute size of the population does not enter this
problem. Using Equation (15.9). we have
Growth rate = Birth rate - Death rate + Immigration rate
= 20 - 9 + 1 [per 1,000 people/yr)
= 12 per 1.000 people/yr
On a percentage basis this annual growth rate is 1.2 percent of the population.
11. PART 4 Topics in Environmental Policy A~lalysis
Age Structure of a Population Data on overall birth and death rates, plus imnligra-
tion statistics, allow us to construct an overall picture of population dynamics. Even
more useful than the crude rates for the o ~ e r a l population are the age-sl~er~fic
l rrrtes
by gender. Combining such data with information on the oge sti.zrc,tirlr of a popula-
tion allows much more accurate projections of near-term population trends.
Figure 15.7 shows two exanlples of age-specific population distributions, illus-
trating the number of males and females in the population for various age intervals."
For clarity of presentation an age interval of five years is used in these figures, although
a one-year intenal is commonly used in population statistics. Notice the bulge in the
United States in the middle years: this represents the baby boom cohort born after World
War 11. As this population ages; there will be an increasing percentage of people in the
higher age brackets. and the age distribution profile will flatten. In contrast, the shape
of the world population shows a predominantly younger population. This implies a sub-
stantial growth in future population as younger people enter their reproductive years.
Fertility Rates A key factor in population projections is the total fei-tilitj rate of
women in the population. A composite of the age-specific birth rates in a given year,
this approximates the average number of children born to each woman during her life-
time. The higher the total fertility rate, the larger the future population is likely to be.
The rP~~lrrcer~lei~t rate is the average number of live births needed to
fertility
replace each female in the current population with one female in the next generation.
L
i
In modern industrialized countries this number is about 2.1, reflecting the slightly i
higher proportion of males that are born each year (it's not exactly 50-50, as seen by
the higher number of males in the younger population in Figure 15.7) plus the num-
ber of females who die before childbirth. An important factor here is the number of
newborns who do not survive the first year of life. In poorer societies with little
access to the benefits of modern medicine and child care. the infant morrcrlih rntcJ
historically has been high. Successful efforts to reduce infant mortality thus can sig-
nificantly impact the replacement fertility rate and the total number of newborns
1
i
who survive and contribute to the future population.
Fertility rates that exceed the replacement rate create a pop~ilatioiziiloinerltuin
that leads to a sustained increase in population. The highest fertility rates in the
world today are found among developing countries, whose populations are growing
most rapidly (for example, fertility rates are above 7 in some African nations). In
contrast. fertility rates in many industrialized countries (Western Europe) are cur-
rently about l .5. well below the replacement rate. At this level the overall population
will gradually decline over several decades (barring an increase in immigration).
Projecting Future Population A simple example illustrates how population growth
can be modeled using statistical data on age structure and age-specific birth rates and
death rates. In order to simplify the arithmetic, the following example uses hypo-
thetical data for a 10-year period rather than I-year intervals.
* Most textbooks display the number of males and females in each age group side by side in the form of a
population tree centered about the vertical axis. The graphical presentation in Figure 15.7, however, shows
more clearly the differences in male and female populations in each age group.
12. I UFemales 1
Total = 273M
(mid-1999)
U.S.population (millions)
I
Males
I 3 Female3
Total = 5.996M
(mld- 1999)
0 100 200 300 400
World population (millions)
Figure 15.7 Age distribution of the U.S. and world populations in 1999. The U.S. population has a
bulge in the middle age group, whereas the world population is markedly younger. The
figure also shows that females outnumber males aher age 30 in the U.S. population and
aher age 55 in the world population. (Source: Based on USDOC, 1999b and 1999c)
649
13. 650 PART 4 Topics in En~ironmentalPolicy Analysis
A population projection based on age-specific data. Table 15.1 shows the current age distri-
bution for a hypothetical population of 500 million people. The age-specific birth rates and
death rates also are shoa.11 based on data for the previous 10 years. Assuming these rates also
apply for the next decade, calculate the total population and percentage of people in each age
E
group expected 10 years froin now. Assume immigration is negligible.
p
P
C Table 15.1 Populat~onstat~st~cs Example
for 15 6
t
Births per Deaths per
Age Group Current Population 1,000 People 1,000 People
(Years) (Rlillions) (During 10 Years) (During 10 Years)
0-5 100 0 20
10-19 95 200 30
20-29 90 600 30
30-39 80 300 40
4040 60 100 50
50-59 40 0 100
60-69 20 0 300
70-79 10 0 500
80-89 4 0 700
50-09 1 0 1,000
Total 500 million
Solution:
-
=
-
-
2r
-
First calculate the total number of births across all 'Ige groups oer the 10-year period. This
- uill determ~nethe total number of children In the 0-9 ~Ige group in the next time period a
-
%
-.
.t
dec'lde from non The reproductive ages In t h ~ s example are the four groups between I 0 ,~nd I
=8 49 bears old. For s~mplicity, r'ites are based on the t o t ~ l
the populat~onof each age group
rather than on the number of females. Thus
200 births
Births to people aged 10-1 9 = 95 X 10"eople X - = 19 X 10' births
1.000 people
Similarly.
Births to people aged 20-29 = (90 X 10') (l!i:o) = 54 X 106
--
Births to people aged 30-39 = (80 X 10") = 32 X 10"
1
Summing over a11 age groups, the total number of children born over the next 10 years
would be 19 i 54 + 32 + 6 = 1 l I million. This would be the population in the 0-9 age group
I 10 years from now (that is, children who have not yet reached their 10th birthday ). Note that
14. CHAPTER 15 Environmental Forecasting
'table 15.2 Summary of present and future population for Example 15.6
Population (in Millions) Percentage of Total
Age Group
(Years) Current In 10 Years Current In 10 Years
0-9
10-19
20-29
30-3 9
4049
50-59
60-69
7U-79
SO-89
90-99
Total
the infant mortality rate (for age 0 to 1 year) is not given separately in this example but rather
is accounted for in the overall death rate for the 0- to 9-year-old population.
The remainder of the future population tree is quantified using the age-specific death rates
given in Table 15.1. For instance, Table 15.1 shows that the I00 million children currently in the
0-9 age group will experience an average death rate of 20 per 1,000 (2.0 percent). This means
that 98 percent of the 0-9 cohort will sunive and subsequently enter the 10-19 age group. Thus
Future population Current population
aged ) (
= aged 0-9 X (Fraction surviving)
Similar calculations apply to all other age groups. Notice that for the oldest group (90-99
years) the 10-year death rate in this example is 100 percent. meaning that no one in this age
group survives past the 99th year.
Table 15.2 summarizes the results of these calculations. The table shows how each age
group in the current population moves into the next age group 10 years from now. Also shown
is the percentage of the total population in each age group now and in the future.
The result is a population of 578 million people 10 years from noM1.This represents a 16 per-
cent increase over the current population. The percentage distributions also show an aging of the
population. with nearly 10 percent ovcr age 60 in the future compared to 7 percent currently.
The preceding example used a 10-year age interval to sirnplify the computations.
Also, population migration was assumed to be negligible. A more refined analysis.
done on a computer, would use one-year age categories and time steps, along with
gender-specific and age-specific population figures, birth rates, and death rates. Exam-
ples of such projections appear in Figure 15.8(a),which shows the U.S. population pro-
jected to the year 3050, as estimated by the U.S. Census Bureau for three scenarios
(labeled low, middle, and high series). Figure 15.8(b) shows how key parameters are
15. PART 4 Topics in Environmental Policy Analysis
assumed to change over time in the middle series Census Bureau projection. Differ-
ent assumptions produced the higher and lower population projections.
Figure 15.8(c) shows the 2050 population distribution resulting from the middle
series projections. Note that the profile is much more uniform than the 1999 profile in
Figure 15.7(a). These projections imply a higher percentage of older people in the pop-
ulation than today: In the oldest groups the population over 80 will have roughly tripled.
These results have important implications for public policy and the economy. For exam-
ple, demand is likely to increase for health care services (as opposed to schools) and a
smaller percentage of the population will be in the workforce. At the same time more
1,200 -
----- Low series
- - - High series /
/
/
/
/
/
Actual Forecast 0/
0
Year
(a)
Year
(b)
Figure 15.8 U.S. population projections to 2050, showing [a] the low, middle, and high
estimates of the U.S. Census Bureau, (b) key assumptions behind the middle
(best estimate) projection, and [c) the best-estimate age distribution in 2050.
(Source: Based on USDOC, 1999b and 1999c)
16. CHAPTER 15 Environmental Forecasting
.niddle
-, )lilt in
? 2 pop-
:
:sipled.
, ;Yam-
:
. and a
..: {iiore
0 5 10 15 20
U.S. pop~~lation
(millions)
Figure 15.8 (continued) (c)
senior citizens will be collecting Social Security benefits. The importance of population
projections thus extends to a broad range of issues besides environmental impact$.
The procedure used in Example 15.6 to calculate the total future population
based on age-specific birthrates and death rates can be expressed in general mathe-
matical terms. A more detailed mathematical model also would divide the popula-
tion into males and females and include separate data on the age-specific popula-
tions, fertility rates, and death rates by gender.
The writing of such equations is a bit tedious but essential for programming
advanced models. A taste of this appears as an exercise for students in the problems
at the end of this chapter.
Limitations of Demographic Models Even when using detailed population data in
sophisticated demographic models, we must make assumnprions about how far into
the future the current birth rates and death rates will prevail. In some cases mathe-
matical models (including logistic models) have been proposed to estimate future
changes in these parameters. But for the most part, assumptions about future fertil-
ity rates, death rates. and immigration patterns remain a matter of judgment because
the future remains uncertain. Assumptions about demographic parameters are often
linked to assumptions about future economic development and standards of living.
For instance. fertility rates and infant mortality rates are substantially lower among
wealthier populations than among poorer regions of the world.
17. PART 4 Topics i n Environmental Policy A n a l y s i ~
Demographic models are thus limited in their ability to jb~*ecrut long-term
changes in population. On the other hand. they can be very useful for analyzing sce-
izarios of future population trends under different conditions. The detailed population
data of a demographic model also allow a richer set of "what if" questions to be
asked, such as the effects of future changes in fertility rates and infant mortality rates.
Such scenarios can reveal how policy actions might influence future population
trends and hence environmental impacts. Demographic models also provide informa-
tion on the size and age structure of the available labor force of the future. which pro-
vides an important link to economic projections. as we shall discuss shortly.
At the same time! demographic models are not always necessary or suitable for
all types of environmental projections. Rather, in many situations the results of
demographic projections can be used to estimate the parameter values needed for
simpler models. Such a case is illustrated in the next example.
Example 15.7
Estimating growth rates from population data. A demographic analysis projects the total pop-
ulation of a region to grow from 500 inillion to 772 million over the next 30 years. If this pop-
ulation increase were to be approximated by a simple exponential growth model. what is the
implied annual groath rate'?
Solution:
The exponential growth model was given earlier by Equation (15.2). In this case both the ini-
tial and final populations are known. and we are solving for the growth rate. r; over a period
of 30 years. Thus
In most environmental forecast5 or scenarios. the size of the future population is
only one determinant of future env~ronmental impacts. A second. closely related factor
is the standard of living or affluence of a population. We look next at how the effects of
economic development can be reflected in an analysis of environmental futures.
15.5 ECONOMIC GROWTH MODELS
Suppose you wanted to estimate the total mass of pollutant emissions from automo-
biles 25 years from now. One key factor in that analysis would be the number of cars
in the future. That could depend on the future size of the popillation (the more peo-
ple, the more cars), but it would also depend on how affordable a car is for the aver-
age citizen. In general, the more affluent the society. the more vehicles per capita as
18. PART 4 Topics in Environmental Policy Analysis
CO2 reduct~on:
- 1 = Stabilize at 1990 levels
2 = 20% reduction below 1990
- 3 = 50% reduction below 1990
-
Year
-
Fiaure 15.13 Macroeconomic model ~redictions U.S. GDP loss from a tax on carbon
of
emissions to control global warming. These average results across a large set
of models predict that GDP losses will increase as C 0 2 emissions are reduced.
[Source: IPCC, 1 996)
environmental analysis, considerable care and judgment are required to exercise and
interpret the results of such models.
15.6 TECHNOLOGICAL CHANGE
We have already touched on the importance of technological change to economic
growth. Here we look at some of the more direct ways that technology change can
affect environmental forecasts or scenarios. Consider again, for example, the prob-
lem of estimating air pollutant emissions from automobiles 25 years from now. Or
even 10 years from now. Not only will the number of cars on the road have changed
(due to changes in population and standards of living), bul vehicle designs also will
have changed. Ten years from now a portion of the U.S. auto fleet is expected to be
electric cars powered by batteries or fuel cells, which emit no air pollution directly.
The design of conventional gasoline-powered vehicles also will have improved to
emit fewer air pollutants than today's cars. Average energy consumption also mighl
change significantly. Vehicles of the future might use more energy than today (from
a continuing trend to large sports utility vehicles), or they might require less energy
(from a transition to smaller, fuel-efficient vehicles).
These are examples of technological changes that can influence an environ-
mental forecast or scenario. In this section we examine some of the ways that tech-
nological change can be considered analytically. The emphasis will be on relatively
simple approaches that can be used easily in environmental analysis.
19. CHAPTER 15 Environmental Forecasting 667
15.6.1 Types of Technology Change
Several types of technology changes can be important for environmental analysis:
Irnprnvernerzts to a current tecl~nologjdesign. Incremental changes can reduce
the environmental impacts of a current technology, typically via improvcmcnts
in energy efficiency or a reduction in pollutant emission rates. An example
would be an automobile with an improved catalyst or engine design emitting
fewer hydrocarbons and nitrogen oxides per mile of travel.
Substitz~tinn f an alternative technology. Replacing a current tcchnology with
o
a different design often can provide the same basic service with reduced envi-
ronmental emissions-for instance. replacing a gasoline-powered car with an
electric vehicle, or an existing coal-fired power plant with an advanced pas-
powered or wind-powered plant. However. the direct and indirect emJ~ronmcn-
'
tal impacts of the alternative technology mcst be carefully evaluated relative to
the current technology design.
New classes o f teclznologq. This extension of the previous case encompasses
technologies that offer a whole new way of doing things. For example, [he
automobile prov~deda new mode of pcrsonal transportation as an alternative
to bicycles or horsc-drawn buggies. Airplanes were a later example of an
cntirely new mode of transportation technology. The future environmental
impacts of new classes of technology are inherently more difficult to cvaluate
than those of the technologies we know. Moreover, some indlrect environmen-
tal impacts may be totally unforcscen (such a5 the extensive urban sprawl pro-
moted by thc growth of automobiles).
Clzange irl tecl~nology utiliiatinn. Engineers are primarily concerned with the
design of technology, but the deployrrlerlt and utilization of technology deter-
mine its aggregate environ~~lental impact. Environmental forecasts or scenarios
must therefore cons~der how technology utilization might change in the future.
Technological innovation has changed the face of personal transportation in the 20th century. Imagine how things might
look 50 or 100 years from now.
20. PART 4 Topics in Environmental Policy Analysis
For instance, will the future use of an automobile (average distance driven per
year) be the same or greater than today? Or might advances in air transporta-
tion or a growth in electronic commerce and telecommuting reduce the aver-
age usage of automobiles in the future?
The answers to such questions, and the importance of technological change.
depend on the time frame of interest and the scope or objectives of the analysis. The
further out in time we go, the more important these issues are likely to become. Next
we discuss a few simple ways of incorporating technology change into environmen-
tal analysis.
15.6.2 Scenarios of Alternative Technologies
Perhaps the most direct way of modeling technological change is to postulate a tran-
sition from current technology to some improved or alternative technology. For
instance, to characterize future emissions from automobiles, we could ask what might
happen if x percent of future automobiles y years from now were battery-powered
electric vehicles. What impact would this have on total air pollutant emissions and
urban smog? A scenario of this type does not try to forecast the actual number of
electric vehicles in the future. Rather. it asks a hypothetical question to assess the
potential air quality benefits of an alternative technology.
The mathematical model in this case would quantitatively characterize the alter-
native technology. Key attributes of a technology for environmental analysis might
include the types and quantities of air emissions, water pollutants, and solid wastes
that are emitted: the fuel or energy consumption required for operation; and the nat-
ural resource requirements and materials needed for construction and operation of the
technology. The environmental analysis also should capture any important indirect
impacts of concern, such as emissions from the manufacturing or disposal of a new
technology. Chapter 7 discussed this type of life cycle approach to environmental
analysis, which applies to future technologies as well as to present-day systems.
Example 15.1 1
Future C 0 2 reductions from a new technology. Coal-burning power plants in many develop-
ing countries have an average efficiency of about 30 percent and emit approximately 1.1 kg
of carbon dioxide (COz) for each kW-hr of electricity generated. C 0 2 is a greenhouse gas that
contributes to global warming. The amount of C 0 2 released is directly proportional to the
amount of coal burned. What if all future coal plants in these countries utilized advanced coal
gasification combined cycle technology with an efficiency of 50 percent? How much would
the CO, emission rate be reduced compared to plants uaing current technology?
Solution:
Recall that efficiency (p) is defined as the useful energy output of a process (in this case,
electricity from the power plant) divided by the energy input (in this case, the fuel energy in
coal). Thus
Electrical energy output
Efficiency (p) = ---
Coal energy input
21. CHAPTER 15 Environmental Forecaring
Per The higher efticiencq of the advanced power plant technology means that less coal
ra- energy input is needed to achieve a gi~en
electrical output. Thus
er-
mge,
Because C 0 2 emissions are proportional to the amount of coal burned, wc also havt:
The
Yext
men-
Thus the advanced plant uould emit 10 percent less CO, than the current plant design. Its C 0 2
emission rate uould be
Iran-
For
:~isht
; c.l-?d
,
Scenarios like this can provide a simple way of estimating the potential envi-
.~nd
ronmental benefits of an advanced technology. IT the results look interesting. a more
.-,: , > f
sophisticated analysis would be needed to assess the feasibility of actually achieving
4uch a result.
15.6.3 Rates of Technology Adoption
An important question in environmental forecasts is how long it takes for a new or
improved technology to achieve widespread use. Chapter 6, for exa~nple.discussed
the design of more energy-efficient refrigerators that eliminate the CFCs (chloroflu-
orocarhonsl responsible for stratospheric ozone depletion. Such refrigerators first
came on the markct in the mid-1990s. But how long will it take until all U.S. house-
holds have these improved refrigerators? The answer is important for predicting
at~rlosphericCFC levels, as well as energy-related environmental impacts.
'Phe speed with which a new technology is adopted depends on many factors.
Three of the most important are its price. its useful lifetime, and the number of com-
peting options. High prices and many competing options inhibit the adoption of a
new technology. So does a long useful lifelime because existing technologies are not
quickly replaced. A number of methods are used to model the rate of adoption of new
tcchnology-some complex, others relatively simple. Three of these methods are
highlighted here.
Specified Rate of Change The most direct method of introducing a new technology
is to ~pecify rate of adoption or diffusion into the economy. In general. that rate
its
will depend on the growth of new markets for the technology, plus the opportunity
to replace cxisting technologies at the end of their useful lives. The expected useful
lifetime is thus an important parameter controlling the rate of adoption of a new
technology. Table 15.6 shows the typical life of several technologies relevant to envi-
ronmental projections.
22. PART 4 Topics in Environmental Policy Analysis
Table 15.6 Typical technology lifetimes.
Technology Typical Lifetime (yearsy
Light bulbs 1-2
Personal computers 3-8
Automobiles 10-15
Refrigerators 15-20
Petrochemical plants 2040
Power plants 30-50
Buildings 50-100
" Some internal components or subsystems may be replaced more frequently.
For the case of the household refrigerator, the expected useful life is about 18
years. If we assume a constant rate of replacement, this means that 1118th (5.6 per-
cent) of all current refrigerators must be replaced each year for the next 18 years.
This defines the maximum rate of introduction for the improved refrigerator design
discussed earlier-at least in the replacement market. The growth of new markets
offers additional opportunities for adopting the improved technology; the size of this
market depends mainly on population and economic growth.
Example 15.12
Adoption of CFC-free refrigerators. Assume that all 120 million household refrigerators in
the United States in 1995 are replaced with improved CFC-free refrigerators at the end of an
18-year lifetime. Assume further that population and economic growth increase the demand
for new refrigerators by 2.0 million units per year over the next 10 years, and that all of these
units are CFC-free. Estimate the percentage of all household refrigerators that are CFC-free
in 2005.
Solution:
Since we are not given the age distribution of existing refrigerators. assume that each year
1118th of all existing refrigerators die and are replaced with CFC-free models. Thus
1
Replacement units/yr = - (120 M) = 6.67 million units/yr
18
In addition, there is new demand of 2.0 million unitslyear from population and economic
growth. Thus
Total new units/yr = Replacement units/yr + New demand/yr
= 6.67 M + 2.0 M = 8.67 M units/yr
So after 10 years (in 2005) the total number of CFC-free units will be 8.67 X 10 = 86.7 M.
The total number of refrigerators altogether will be
Total units in 2005 = 120 M (as of 1995) + 20 M (new growth) = 140 M
23. CHAPTER 15 Environmental Forecasting
The fraction that are CFC-free in 2005 will be
2005 fraction of - 86.7 M
CFC-free unils ) - 140 M
= 0.62 or 62%
The fraction of CFC-free units will continue to grou for another eight years until it
reaches 100 percent.
Specified Market Share In Example 15.12 the only new refrigerator technology
available after 1995 was the CFC-free design. In this case environmental laws actu-
ally prohibit the continued use of CFCs in new units. In general, though, a new or
improved technology must compete with alternative options in the marketplace. In
that case the adoption rate of a new technology depends not only on the size of the
market, but also on its market share.
One way to model the diffusion of a new technology is to s p e r i ' the market
bout 18 share at different points in time. For instance, one could assume that 10 percent of
I 5.6 per- all new cars sold in 2005 will be electric vehicles. A more detailed specification
18 years. might take the shape of a logistic curve like the ones illustrated in Figure 15.14. This
.rr design type of S-shaped curve is frequently used to model the gradual diffusion of a new
markets technology into the marketplace. The mathematical form of a logistic model was
7 5 of this presented earlier in Section 15.4.3, where it was used to approximate population
growth. Here we use it to represent the growth in market share of a new technology.
All we have to do is to redefine the variable P as percentage market share rather than
population. So if we define P(r) as the percentage share of the market at time t, and
P,,, as the maximunl market share (up to 100 percent). then
:-rators in
2nd of an
- 2 demand
:I1 of these
CFC-free
Slow growth
each year to 100%
Rapid growth
to 70%
Moderate growth
to 40%
I * Time
0
Figure 15.14 Logistic models of growth in technology market share for three scenarios
involving different growth rates and final market share.
24. PART 4 Topics in Environmental Policy Analysis
As before, t,,, is the time needed to reach half the maximum value. and r is a
composite growth rate given by
7"
where r, is the initial growth rate. In this case the characteristic time constants and
growth rate for a logistic model would depend on the technology of interest.
Example 1 5.13
A logistic growth model for new technology adoption. An auto industry analyst believes it will
take 15 years for electric vehicles (EVs) to gain a 50 percent share of near auto sales once the
initial share reaches 10 percent. Based on a logistic growth curve and an initial growth rate of
5 percentlyear, how long would it take for EVs to gain 90 percent of the new car market?
Solution:
Assume the maximum possible market share is 100 percent. The logistic growth rate. r; from
Equation (15.12) is
We want to find the t ~ m e t, at which the market share reaches 90 percent. Thus we use
,
Equation (15.11) and solve for t based on P(r) = 90 and t,,, = 15 years. Rearranging Equation
(15.1 I) gives
,-0 0556(1- 15) = 0 11 I
t = 55 years
Remember that this means 55 years from the time the market share reaches 10 percent. (We
are not told in this problem how lonp it will take to achieve that initial market share. That
requires a separate analysis.)
Consumer Choice Models Instead of directly specifying the market share or adop-
tion rate of a new technology, some forecasting models introduce new technolo-
gies based on consumer preferences. Usually this is based on economic criteria. In
such models the capital and operating costs of a new technology are specified
along with those of all competing technologies. The model (typically a computer t
program) then selects the cheapest option. The cost of a new technology may i
change over time. A limit also may be imposed on its maximum market share to 1
reflect the role of noneconomic factors in technology choice decisions. In some