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Drivers of environmental impact: A proposal for nonlinear scenario designing
Abstract
Drivers of environmental impact are commonly studied in the related literature through the IPAT and STIPAT models. The first is an accounting model and the second is a stochastic approach that enables both statistical tests of significance of the drivers and the consideration of a larger set of drivers. These methodologies, however, are unable to take account of the level of all drivers in a nonlinear structure, i.e., different impacts according to the level of the variable. This paper presents a global Ordered Logistic Model that estimates the probability of four ordinal categories of environmental impact (four defined categories of Ecological Footprint). The results further the analysis of environmental impact offering an additional understanding of what to expect in terms of environmental pressure when the current level of the drivers are changing. The study demonstrates the proposed methodology by offering some examples of scenario analysis based on the estimated model.
... In the 1990s, to that basic accounting model, Dietz and Rosa (1994) add a stochastic approach towards the relationship between the those variables, resulting in the STIRPAT 2 models. This type of model allows the addition of other variables, so that evidence was found to show the level of industrialization (York, Rosa & Dietz, 2003), urbanization (Fan, Liu, Wu & Wei, 2006;Liddle & Lung, 2010;Mattos & Filippi, 2014;York et al., 2003) and age structure (Liddle & Lung, 2010) are important environmental impact factors. ...
The recent literature has explored the ambiguous theoretical links between inequality and environmental impact. However, few studies have investigated those links in developing countries. This paper explores the role of income inequality in the per capita emission of CO2 in Latin America. We estimate the impact of income inequality on carbon dioxide emissions, while simultaneously considering national income levels. The results suggest quadratic relationships between environmental impact and both gross domestic product per capita and inequality. Income inequality influences CO2 emissions, but the direction depends on income level. The complexities of the results are analytically explored in the paper.
... A handful of studies concentrated on oil exporting countries (see, Iwata and Okada 2014;de Mattos and Filippi 2014;Brizga et al. 2013;Kick and Mckinney 2014;Fang and Miller 2013;Lamb et al. 2014;Hasanov et al. 2016) while others either focused on provinces or cities (see also, Zhang et al. 2018;Xie et al. 2017;Wang et al. 2017a, b;Zhou and Liu 2016;Fu et al. 2015;Liddle 2013;Jia et al. 2009). However, none of the studies reviewed (especially those from Nigeria) inculcated the square of urbanization as one of the explanatory variables which is capable of providing insights on how CO 2 emissions respond to both increase and decrease in urbanization rate. ...
This study tries to model urbanization, trade flow and energy consumption with regards to the environment in Nigeria relying on the STIRPAT model with data spanning 1980Q1–2016Q4. The Zivot and Andrews (J Bus Econ Stat 10(3):251–270, 1992) test affirm that the variables are trended but stationary after first difference. The ARDL bounds test and the Bayer and Hanck (J Time Ser Anal 34(1):83–95, 2013) cointegration tests confirm a long run relationship among the variables. Findings reveal that urbanization and energy consumption are the major drivers of CO2 emissions in both time periods, while trade performs the opposite. Also, a unidirectional causality exists from urbanization and its square to carbon emissions. This clearly indicates that urbanization causes environmental degradation. Therefore, policies to tackle energy poverty and also make it clean, minimize urban anomalies and enhance sustainable growth were suggested.
... Therefore, in this section, we review only those studies that are similar to our research here in terms of country considered and the methodology used. 4 As we have emphasized above, the majority of the existing studies, devoted to the oil-exporting economies of the Commonwealth of Independent States (CIS) of which Azerbaijan is a member country, are either cross-sectional ( York et al., 2003a;Knight, 2008;Kick and McKinney, 2014;Lamb et al., 2014;Mattos and Filippi, 2014) or panel ( Fang and Miller, 2013;Martínez-Zarzoso, 2009;York and Rosa, 2012;Brizga et al., 2013;Jorgensen, 2011;Lankao et al., 2008 Bulut et al. (2015) are time series studies and the rest ones are panel studies, which do not take into consideration countryspecific features. Nonetheless, we review all of them below as there are only three time series studies exploring the Azerbaijani energy/electricity consumption. ...
In this study, we examined the impacts of population age groups of 15-64 and 65-above on residential electricity consumption in Azerbaijan within the STIRPAT framework. Unlike many prior studies of STIRPAT framework, we analyzed this impact, employing co-integration and error correction method in order to rule out possible spurious estimation results caused by non-stationary data used. Results from the Autoregressive Distributed Lags Bounds Testing approach, which is the preferred method among alternatives in the case of small samples, indicated that the affluence together with age groups have significant impact on the residential electricity consumption in Azerbaijan and the biggest effect comes from the age group of 15-64, which is the working age population. Another finding of the study is that if there is a socio-economic, or political shock to the system that initially affect residential electricity consumption and affluence, the whole shock will be absorbed by the system less than in one year. Findings of the study may be useful in making appropriate decisions in the fields of residential electricity consumption.
... In this section, we review those studies, relevant to our research in terms of countries considered and the methods applied. 8 The majority of the existing research considering the CIS oil-exporting economies are either cross-sectional (York et al., 2003a;Knight, 2008;Kick and McKinney, 2014;Lamb et al., 2014;Mattos and Filippi, 2014) or panel (Fang and Miller, 2013;Martínez-Zarzoso, 2009;York and Rosa, 2012;Brizga et al., 2013;Jorgenson, 2011;Lankao et al., 2008;Grunewald and Martínez-Zarzoso, 2009a,b;Prew, 2010;Iwata and Okada, 2014;Martínez-Zarzoso and Maruotti, 2011;Grunewald and Martínez-Zarzoso, 2011) studies investigating environmental issues but not energy use. 9 In this regard, only Shafiei (2013); Scarrow (2010); Fang et al. (2012) and Nouri et al. (2012) examine the energy use of the selected countries. ...
This study examines the impacts of the population age groups of 20–34, 35–49, 50–64 and 65–79 on the per capita energy use of the oil-exporting countries of Commonwealth Independent States: Azerbaijan, Kazakhstan and Russia employing the modified-STIRPAT framework. Considering that estimations using non-stationary data may yield spurious results, unlike many prior STIRPAT studies, we explore integration and cointegration properties of the data and then estimate long- and short-run elasticities as well as speed of adjustment coefficients. Since our time series analysis covers only 23 observations (1990-2012), as a robustness check, we also conduct panel data analysis by pooling the mentioned countries data with that for members of Organization of Petroleum Exporting Countries. We apply the Autoregressive Distributed Lags Bounds Testing approach in the time series analysis and Pooled Mean Group estimator in the panel analysis, both are superior in small samples. The findings from the time series analysis are supported by those from the panel data analysis. According to the results, there is cointegrated relationship among the variables. The age groups together with affluence and oil prices have statistically significant impacts on the per capita energy use in the selected countries. Moreover, we find the speed of adjustments exhibiting different magnitudes for different countries depending on which population age group is considered. The findings suggest that policymakers should pay special attention to the population age groups of 35–49 and 50–64, as they have a large effect on per capita energy use. Since these groups are the main part of the working age population, increase in their energy consumption is likely to lead to economic growth. Furthermore, the policymakers should take into consideration the finding that speed of adjustments towards an equilibrium path is quite high. It implies that any policy related shocks to the per capita energy use relationship could disappear within a year or even sooner.
... Yuxarıda da qeyd etdiyimiz kimi Rusiya da daxil olmaqla MDB-nin neft ixrac edən ölkələrinə həsr olunmuş tədqiqatlar ya ölkələr arası (66,32,31,36,44) və ya panel (15,43,68,3,35,20,57,28,42,21) tədqiatlardır və onlar enerji istehlakınadeyil, ətraf mühit məsələlərinə fokuslaşmışlar(Bu tədqiqatlarda asılı dəyişən olaraq ya CO2 emissiyası və ya ekoloji iz qəbul edilmişdir).Sadəcə, Scarrow (58), Liddle (2011), Fang və digərləri (15), Nouri və digərləri (48), Shafiei (59) və Həsənov və digərləri (23) enerji istehlakına təsir edən amilləri tədqiq etmişlər.Baxmayaraq ki, Həsənov və digərləri (23) xaric yuxarıda adı çəkilən digər tədqiqat işləri panel tədqiqatlardır və ölkələrin spesifik xüsusiyyətlərini nəzərə almırlar, biz yenə də onları aşağıda xülasə etmişik. ...
In this study, we examined impacts of population age groups of 15-64 and 65-above on residential electricity
use in Russiain STIRPAT framework. Unlike many prior studies of STIRPAT framework, we analyzed the impacts
of employing cointegration and error correction method in order to rule out possible spurious estimation results
causing by non-stationarity of data used. Results from the Autoregressive Distributed Lags Bounds Testing approach,
which is the preferable method among alternatives in the case of small samples, indicated that the affluence has
significant impact on the residential electricity use in Russia, while population age group 15-64 has insignificant
but positive impact. Findings of the study may be useful in making appropriate decisions in the fields of residential electricity consumption.
We studied impacts of population groups of 15–64 and 65–above on residential electricity use in Kazakhstan in the STIRPAT framework. Unlike earlier studies for Kazakhstan in the STIRPAT framework, we applied time series cointegration and error correction methods. Results from the autoregressive distributed lags bounds testing approach indicate a significant impact of the age group of 15–64 on the residential electricity use in long-run, however, the age group of 65–above has only short-run effects and affluence has no effect. Another finding is that, 21% of short-run disequilibrium can be corrected towards long-run equilibrium during a year. Policymakers should consider the trend of the population group of 15–64 in their decision about the long-run stance of the residential electricity consumption. The trend suggests an implementation of energy conservative policy and increasing efficiency of its usage. Another policy implication is that household's electricity consumption is not income dependent maybe due to cheap electricity prices subsidised by the government. In the short-run, policy makers should consider the age group of 65–above among other factors in their implementations. Moreover, they should be careful in making any policy shock to the residential electricity consumption system, because convergence towards long-run equilibrium path takes about six years.
We studied impacts of population groups of 15-64 and 65-above on residential electricity use in Kazakhstan in the STIRPAT framework. Unlike earlier studies for Kazakhstan in the STIRPAT framework, we applied time series cointegration and error correction methods. Results from the autoregressive distributed lags bounds testing approach indicate a significant impact of the age group of 15-64 on the residential electricity use in long-run, however, the age group of 65-above has only short-run effects and affluence has no effect. Another finding is that, 21% of short-run disequilibrium can be corrected towards long-run equilibrium during a year. Policymakers should consider the trend of the population group of 15-64 in their decision about the long-run stance of the residential electricity consumption. The trend suggests an implementation of energy conservative policy and increasing efficiency of its usage. Another policy implication is that household's electricity consumption is not income dependent maybe due to cheap electricity prices subsidised by the government. In the short-run, policy makers should consider the age group of 65-above among other factors in their implementations. Moreover, they should be careful in making any policy shock to the residential electricity consumption system, because convergence towards long-run equilibrium path takes about six years.
Previous research has suggested a link between household dynamics (i.e., average household size and number of households) and environmental impacts at the national level. Building on this work, we empirically test the relationship between household dynamics and fuelwood consumption, which has been implicated in anthropogenic threats to biodiversity. We focus our analysis on developing countries (where fuelwood is an important energy source). Our results show that nations with smaller average households consume more fuelwood per capita. This finding indicates that the household economies of scale are, indeed, associated with the consumption of fuelwood. In addition, we found that number of households is a better predictor of total fuelwood consumption than average household size suggesting a greater relative contribution to consumption levels. Thus, insofar as declining average household sizes result in increased number of households and higher per capita consumption, this trend may be a signal of serious threats to biodiversity and resource conservation. We also found further support for the “energy ladder” hypothesis that economic development reduces demand for traditional fuels.
This study examines the regional and temporal differences in the statistical relationship between national-level carbon dioxide emissions and national-level population size. The authors analyze panel data from 1960 to 2005 for a diverse sample of nations, and employ descriptive statistics and rigorous panel regression modeling techniques. Initial descriptive analyses indicate that all regions experienced overall increases in carbon emissions and population size during the 45-year period of investigation, but with notable differences. For carbon emissions, the sample of countries in Asia experienced the largest percent increase, followed by countries in Latin America, Africa, and lastly the sample of relatively affluent countries in Europe, North America, and Oceania combined. For population size, the sample of countries in Africa experienced the largest percent increase, followed countries in Latin America, Asia, and the combined sample of countries in Europe, North America, and Oceania. Findings for two-way fixed effects panel regression elasticity models of national-level carbon emissions indicate that the estimated elasticity coefficient for population size is much smaller for nations in Africa than for nations in other regions of the world. Regarding potential temporal changes, from 1960 to 2005 the estimated elasticity coefficient for population size decreased by 25% for the sample of Africa countries, 14% for the sample of Asia countries, 6.5% for the sample of Latin America countries, but remained the same in size for the sample of countries in Europe, North America, and Oceania. Overall, while population size continues to be the primary driver of total national-level anthropogenic carbon dioxide emissions, the findings for this study highlight the need for future research and policies to recognize that the actual impacts of population size on national-level carbon emissions differ across both time and region.
There is little doubt that humans are consuming Earth's natural resources at an unprecedented rate, and the consensus among the scientific community is that, unchecked, this will lead to ecological calamities such as global warming, habi-tat encroachment, biodiversity loss, and air, land, and water pollution (Tausnev 2005; Intergovernmental Panel on Climate Change 2007; United Nations Environment Programme 2007). Alterations to the organic composition of the environment occurring since the industrial revolution are unrivaled by those accumulated in any prior epoch in history (Ewing, Moore, Goldfinger, Oursler, Reed, and Wackernagel 2010; UNEP 2007). In addition to bur-geoning population growth, many attribute this to the staggering rate of fossil fuel extraction and combustion that supplies energy to machines for production and transportation purposes (Ewing et al. 2010; IPCC 2007). Monitoring and managing the magnitude of resource depletion is essential to sustainability, or the provision ABSTRACT: Environmental sociologists have identified various facets of anthropogenic ecological degradation such as deforestation, greenhouse gas emissions, natural resource depletion, and water and land pollution. Virtually all of these outcomes are encompassed by one important dimension—the introduction of "entropy" or disorder via processes of production, consumption, and waste creation that are critical to understanding current cleavages in sustainability profiles of nations. This article synthesizes thermodynamic laws with global political-economic interpretations on the environment. A cross-national structural equation model demonstrates the theoretical and empirical importance of entropy as it relates to natural resources and environmental degradation. Conclusions and implications for research in environmental sociology, particularly, and sociology, generally, are discussed.
In the early 1970s Ehrlich and Holdren devised a simple equation in dialogue with Commoner identifying three factors that created environmental impact. Thus, impact (I) was expressed as the product of (1) population, (P); (2) affluence, (A); and (3) technology, (T). This article tracks the various forms the IPAT equation has taken over 30 years as a means of examining an underlying shift among many environmentalists toward a more accepting view of the role technology can play in sustainable development. Although the IPAT equation was once used to determine which single variable was the most damaging to the environment, an industrial ecology view reverses this usage, recognizing that increases in population and affluence can, in many cases, be balanced by improvements to the environment offered by technological systems.
This paper examines two environmental impacts for which population has a substantial demonstrated influence: transport carbon emissions and residential electricity consumption. It takes as its starting point the STIRPAT framework and disaggregates population into four key age groups: 20-34, 35-49, 50-69, and 70 and older. Population age structure’s influence was significant and varied across cohorts, and its profile was different for two dependent variables. For transport, young adults (20-34) were intensive, whereas the other cohorts had negative coefficients. For residential electricity consumption, age structure had a U-shaped impact: the youngest and oldest had positive coefficients, while the middle cohorts had negative coefficients.
Complacency concerning this component of man's predicament is unjustified and counterproductive.
Learning actors' leverage for change along the journey to sustainability requires quantifying the component forces of environmental impact and integrating them. Population, income, consumers' behavior, and producers' efficiency jointly force impact. Here, we renovate the "IPAT Identity" to identify actors with the forces. Forcing impact I are P for population, A for income as gross domestic product (GDP) per capita, C for intensity of use as a good per GDP, and T for efficiency ratios as impact per good. In the "ImPACT Identity," parents modify P, workers modify A, consumers modify C, and producers modify T. Because annual percentage changes in component forces add to a change in national impact, actors' leverage is reflected transparently in consistent units of annual percentage changes that can be compared from force to force. Examples from energy and food, farming and manufacturing, and steel and water show that declining C, called dematerialization, can temper the sustainability challenge of growth (P x A), and that innovation or efficient technology that lowers T can counter rising consumption (P x A x C). Income elasticity can accommodate connections between income and other forces. From rates of change of forces, the identity can forecast impacts. Alternatively, by identifying the necessary change in forces to cause a projected impact, ImPACT can assay the likelihood and practicability of environmental targets and timetables. An annual 2-3% progress in consumption and technology over many decades and sectors provides a benchmark for sustainability.
We examine the reduced-form relationship between per capita income and various environmental indicators. Our study covers
four types of indicators: urban air pollution, the state of the oxygen regime in river basins, fecal contamination of river
basins, and contamination of river basins by heavy metals. We find no evidence that environmental quality deteriorates steadily
with economic growth. Rather, for most indicators, economic growth brings an initial phase of deterioration followed by a
subsequent phase of improvement. The turning points for the different pollutants vary, but in most cases they come before
a country reaches a per capita income of $8000.
This paper considers the influence of uncertainty with respect to GDP and population total and age structure on long-run environmental impact projections. A simple model, based on the stochastic version of the IPAT equation, is used to generate projections of two environmental impacts for which population has a demonstrated influence carbon emissions from transport and residential electricity consumption. The paper examines the sensitivity of those long-run projections to uncertainty regarding possible future GDP and population growth/change. The following are demonstrated: 1) if the medium path for either population or GDP is to be believed/trusted, then uncertainty with respect to the growth/change of the other factor is unimportant; 2) uncertainty with respect to population projections has a greater effect on environmental impact projections than does uncertainty with respect to GDP projections despite that the degree of uncertainty associated with population is smaller than the uncertainty associated with aggregate economic growth.
Models are increasingly being relied upon to inform and support natural resource management. They are incorporating an ever broader range of disciplines and now often confront people without strong quantitative or model-building backgrounds. These trends imply a need for wider awareness of what constitutes good model-development practice, including reporting of models to users and sceptical review of models by users. To this end the paper outlines ten basic steps of good, disciplined model practice. The aim is to develop purposeful, credible models from data and prior knowledge, in consort with end-users, with every stage open to critical review and revision. Best practice entails identifying clearly the clients and objectives of the modelling exercise; documenting the nature (quantity, quality, limitations) of the data used to construct and test the model; providing a strong rationale for the choice of model family and features (encompassing review of alternative approaches); justifying the techniques used to calibrate the model; serious analysis, testing and discussion of model performance; and making a resultant statement of model assumptions, utility, accuracy, limitations, and scope for improvement. In natural resource management applications, these steps will be a learning process, even a partnership, between model developers, clients and other interested parties.
Ground-level air pollution has serious effects on the natural environment and human health, but it has not received the same attention in the sociological literature as the greenhouse gases polluting the upper atmosphere. To address questions about the effects of social structural forces on environmental impacts, we analyze cross-national time-series data (1990–2000) to assess influences on the emission of ground-level air pollutants: sulfur dioxide, nitrogen oxides, carbon monoxide, and non-methane volatile organic compounds. Drawing on human ecological theory, we move beyond previous analyses by assessing demographic effects on pollution emissions in a nuanced way by dividing population into the number of households and average household size. We found that the number of households has a greater effect on SO2 emissions than average household size. This suggests that the effect of population on the environment is not simply due to its size and growth, but also to its distribution across households. The difference we found has important implications, since the global growth rate in the number of households is greater than the growth rate in population. Furthermore, while the population growth rate in less developed nations is over four times that in developed nations, the household growth rate is only double. This finding suggests that developed nations will contribute more to air pollution in the coming years than would be assumed based on population growth alone.
This paper analyzes urban population’s and affluence’s (GDP per capita’s) influence on environmental impact in developed and developing countries by taking as its starting point the STIRPAT framework. In addition to considering environmental impacts particularly influenced by population and affluence (carbon emissions from transport and residential electricity consumption), the paper determines whether and, if so, how those environmental impact relationships vary across development levels by analyzing panels consisting of poor, middle, and rich countries. The development-based panels approach is an improvement on the GDP per capita polynomial model used in the Environmental Kuznets Curve and other literatures for several reasons: (i) it allows one to determine whether the elasticity of all variables considered varies according to development; (ii) it is arguably a more accurate description of the development process; (iii) it avoids potentially spurious regressions involving nonlinear transformations of nonstationary variables (GDP per capita squared); and (iv) unlike the polynomial model, it allows for the possibility that elasticities are significantly different across development levels but still positive — precisely the relationship expected for the environmental impacts considered here. Whether or not the elasticity for affluence was greater than that for population was a function of both the choice of dependent variable and the makeup of the panel (all countries, poor, middle, or rich). Furthermore, the estimated elasticities varied, in a nonlinear fashion, according to the development process: U-shaped, inverted U-shaped, and monotonic patterns were revealed, again, depending on the dependent variable.
This comparative analysis shows that population size and affluence are the principal drivers of anthropogenic environmental stressors, while other widely postulated drivers (eg urbanization, economic structure, age distribution) have little effect. Similarly, increased education and life expectancy do not increase environmental stressors, suggesting that some aspects of human well-being can be improved with minimal environmental impact. Projecting to 2015, we suggest that increases in population and affluence will likely expand human impact on the environment by over one-third. Countering these driving forces would require increases in the efficiency of resource use of about 2% per year.
We analyze data for fourteen foundational European Union Nations covering the period 1960–2000 to estimate the effects of demographic and economic factors on energy consumption. We find that population size and age structure have clear effects on energy consumption. Economic development and urbanization also contribute substantially to changes in energy consumption. We use the resultant model to project energy consumption for the year 2025 based on demographic and economic projections to assess the implications of various demographic scenarios. The projections suggest that the expected decline of population growth in Europe will help curtail expansion in energy consumption.
In the literature on the application of STIRPAT to environmental impacts of population and affluence, the parameter estimates differ from study to study. One example is the effect of population size on CO2 emissions, which is concluded to be very close to 1 in some studies (e.g., York et al., 2003) while far from 1 in others (e.g., Shi, 2003). What can explain these differences in results? In the present paper, I offer an alternative model equivalent to STIRPAT, which explicitly specifies the different role of technology (T) in STIRPAT from IPAT. By the alternative model, I conclude that different functional forms of STIRPAT can be one explanation for the difference among estimates in the studies on environmental impacts of population and affluence. The alternative model can also help to determine which factors to be added in STIRPAT.
Growing evidence demonstrating clear threats to the sustainabilily of the ecosystems supporting human societies has given rise to a variety of sociological theories of human-environment interactions. These environmental impact theories fall into three general perspectives: human ecology, modernization, and political economy. These theories, however, have not been empirically tested in a common analytic framework. Here, a framework that relies on ecological principles is adopted and modified. Using a revised stochastic formulation of that framework and the most comprehensive measure of environmental impact to date-the ecological footprint-the factors driving the environmental impacts of societies are assessed. The overall findings support the claims of human ecologists, partially support the claims of political economists, and contradict the claims of modernization theorists. Basic material conditions, such as population, economic production, urbanization, and geographical factors all affect the environment and explain the vast majority of cross-national variation in environmental impact. Factors derived from neo-liberal modernization theory, such as political freedom, civil liberties, and state environmentalism have no effect on impacts. Taken together, these findings suggest societies cannot be sanguine about achieving sustainability via a continuation of current trends in economic growth and institutional change.
The Ecological Footprint (EF) equation provides useful accounting to analyze the relationship between human activities and
the environment. Knowledge of the specific forces driving EF is not fully understood but the STIRPAT model provides a simple
framework for decomposing the impact of human activities on environment. We applied the EF model in Sichuan Province, China
to assess the impact of human activities. The per capita EF increased by 2 fold in the 14 years between 1995 and 2008, but
ecological capacity decreased in the same period, suggesting that the biologically productive area of Sichuan Province is
inadequate to sustain human activities. According to the refined STIRPAT model, the hypothesized driving forces of EF include
population size (P), GDP per capita (A1), quadratic term of GDP per capita (A2), percentage of GDP from industry (T1) and urbanization rate (T2). However, the multi-collinearity among these drivers could be a substantial problem which may reveal negative effect in
the final results. Application of the Ridge Regression (RR) method to fit the STIRPAT model had the advantage of being able
to avoid the collinearity among independent variables. The results showed that population is the principal driving force of
EF variation in Sichuan Province and that urbanization and industrialization also have a positive association with the EF.
Analysis of affluence elasticity (EEA) showed that the relationship between EF and economic growth was not curvilinear, suggesting that variation of EF does not
follow an Environmental Kuznets Curve relative to economic growth in Sichuan Province.
KeywordsEcological footprint–STIRTPAT model–Ridge Regression–Major driving forces–Sichuan Province–China
We focus on three environmental impacts particularly influenced by population age-structure—carbon emissions from transport
and residential energy and electricity consumption—as well as aggregate carbon emissions for a panel of developed countries,
and take as our starting point the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) framework.
Among our contributions is to further disaggregate population into three particularly key age groups: 20–34, 35–49, and 50–64,
and by doing so demonstrate that population’s environmental impact differs considerably across age groups, with the older
age groups (ones typically associated with larger households) actually exerting a negative influence. Furthermore, those age-specific
population influences are different (in absolute and relative terms) for the different environmental impacts we analyze. Also,
we find that urbanization, in developed countries, best measures access to a country’s power grid, and thus, is positively
associated with energy consumption in the residential sector. Finally, we suggest some modeling and methodological improvements
to the STIRPAT framework.
KeywordsSTIRPAT-Population structural change and environment-Carbon dioxide emissions-Demography and climate change-Panel data
The Environmental Kuznets Curve (EKC) hypothesis postulates an inverted-U-shaped relationship between different pollutants and per capita income, i.e., environmental pressure increases up to a certain level as income goes up; after that, it decreases. An EKC actually reveals how a technically specified measurement of environmental quality changes as the fortunes of a country change. A sizeable literature on EKC has grown in recent period. The common point of all the studies is the assertion that the environmental quality deteriorates at the early stages of economic development/growth and subsequently improves at the later stages. In other words, environmental pressure increases faster than income at early stages of development and slows down relative to GDP growth at higher income levels. This paper reviews some theoretical developments and empirical studies dealing with EKC phenomenon. Possible explanations for this EKC are seen in (i) the progress of economic development, from clean agrarian economy to polluting industrial economy to clean service economy; (ii) tendency of people with higher income having higher preference for environmental quality, etc. Evidence of the existence of the EKC has been questioned from several corners. Only some air quality indicators, especially local pollutants, show the evidence of an EKC. However, an EKC is empirically observed, till there is no agreement in the literature on the income level at which environmental degradation starts declining. This paper provides an overview of the EKC literature, background history, conceptual insights, policy and the conceptual and methodological critique.
This paper presents a critical history of the environmental Kuznets curve (EKC). The EKC proposes that indicators of environmental degradation first rise, and then fall with increasing income per capita. Recent evidence shows however, that developing countries are addressing environmental issues, sometimes adopting developed country standards with a short time lag and sometimes performing better than some wealthy countries, and that the EKC results have a very flimsy statistical foundation. A new generation of decomposition and efficient frontier models can help disentangle the true relations between development and the environment and may lead to the demise of the classic EKC.
In assessing and forecasting the impact of population change on carbon dioxide emissions, most previous studies have assumed a unitary elasticity of emissions with respect to population change, i.e. that a 1% increase in population results in a 1% increase in emissions. This study finds that global population change over the last two decades is more than proportionally associated with growth in carbon dioxide emissions, and that the impact of population change on emissions is much more pronounced in developing countries than in developed countries. The empirical findings are based on a data for 93 countries over the period 1975–1996.
The proportional odds model for ordinal logistic regression provides a useful extension of the binary logistic model to situations where the response variable takes on values in a set of ordered categories. The model may be represented by a series of logistic regressions for dependent binary variables, with common regression parameters reflecting the proportional odds assumption. Key to the valid application of the model is the assessment of the proportionality assumption. An approach is described arising from comparisons of the separate (correlated) fits to the binary logistic models underlying the overall model. Based on asymptotic distributional results, formal goodness-of-fit measures are constructed to supplement informal comparisons of the different fits. A number of proposals, including application of bootstrap simulation, are discussed and illustrated with a data example.
We developed a stochastic version of the Impact = Population x Affluence x Technology (IPAT) model to estimate the effects of population, affluence, and technology on national CO2 emissions. Our results suggest that, for population, there are diseconomies of scale for the largest nations that are not consistent with the assumption of direct proportionality (log-linear effects) common to most previous research. In contrast, the effects of affluence on CO2 emissions appear to reach a maximum at about $10,000 in per- capita gross domestic product and to decline at higher levels of affluence. These results confirm the general value of the IPAT model as a starting point for understanding the anthropogenic driving forces of global change and suggest that population and economic growth anticipated over the next decade will exacerbate greenhouse gas emissions.
Modelling the geographic distribution of urban population densities has been attempted in several ways. Recently a controversy emerged in the Journal of Urban Economics regarding whether or not calibrations of these models render unbiased parameter estimates. Three sources of bias were dealt with in these discussions, namely (1) model specification error, (2) the estimation procedure employed, and (3) the definition of areal unit observation size. Additional sources of bias overlooked in this controversy include the presence of multiple centers in a city, and the existence of externalities. This paper explores these additional sources, from both a conceptual and an empirical point of view.
An understanding of the empirical relationship between income and environmental quality is evolving through recent studies investigating the Errvirmrmental Kuznets Curve (EKC). The EKC represents an inverted-U relationship between income and environmental degradation. However, studies may employ different methods, evaluate different environmental indicators, and use different data, resulting in a broad spectrum of findings and leading to sometimes conflicting interpretations. The purpose of this paper is to synthesize the results of existing EKC findings by conducting a statistical meta-analysis, and tn predict new income turning points (ITP), Results indicate how both methodological choices and pollutant types affect ITPs. (JEL Q20).
This paper examines the relationship between per capita income and a wide range of environmental indicators using cross-country panel sets. The manner in which this has been done overcomes several of the weaknesses asscociated with the estimation of environmental Kuznets curves (EKCs). outlined by Stern et al. (1996). Results suggest that meaningful EKCs exist only for local air pollutants whilst indicators with a more global, or indirect, impact either increase monotonically with income or else have predicted turning points at high per capita income levels with large standard errors unless they have been subjected to a multilateral policy initiative. Two other findings are also made: that concentration of local pollutants in urban areas peak at a lower per capita income level than total emissions per capita; and that transport-generated local air pollutants peak at a higher per capita income level than total emissions per capita. Given these findings, suggestions are made regarding the necessary future direction of environmental policy.
Assessing proportionality in the proportional odds model for ordered logistic regression A meta-analysis of environmental Kuznets curve studies
- R Brant
- Arlington
- T A Cavlovic
Brant, R., Dec 1990. Assessing proportionality in the proportional odds model for ordered logistic regression. Biometrics 46, 1171e1178. Arlington. Cavlovic, T.A., et al., 2000. A meta-analysis of environmental Kuznets curve studies. Agric. Resour. Econ. Rev. 29 (1), 32e42.
The IPAT equation and its variants: changing views of tech-nology and environmental impact The environmental Kuznets curve: an empirical analysis. Environ. Develop. Econ. 2, 401e416. Commoner, B., 1972. The environmental cost of economic growth
- M R T A Chertow
- A J Rayner
- J M Bates
Chertow, M.R., 2001. The IPAT equation and its variants: changing views of tech-nology and environmental impact. J. Indust. Ecol. 4 (4), 13e29. Cole, T.A., Rayner, A.J., Bates, J.M., 1997. The environmental Kuznets curve: an empirical analysis. Environ. Develop. Econ. 2, 401e416. Commoner, B., 1972. The environmental cost of economic growth. In: Ridker, R.G. (Ed.), Population, Resources and the Environment. U.S. Government Printing Office, Washington, DC, pp. 339e363.
The interaction of population growth and environ-mental quality
- M Cooper
- C Griffiths
Cooper, M., Griffiths, C., 1994. The interaction of population growth and environ-mental quality. Am. Econ. Rev. 84 (2). Papers and Proceedings of the Hundred and Sixth Annual Meeting of the American Economic Association.
Effects of population and affluence on CO2 emis-sions Rethinking the environmental impacts of population, affluence and technology Driving the human ecological footprint
- T Dietz
- E A Rosa
Dietz, T., Rosa, E.A., 7 Jan 1997. Effects of population and affluence on CO2 emis-sions. Proc. Natl. Acad. Sci. U.S.A. 94 (1), 175e179. Dietz, T., Rosa, E.A., 1994. Rethinking the environmental impacts of population, affluence and technology. Hum. Ecol. Rev. 1, 277e300. Dietz, T., Rosa, E.A., York, R., 2007. Driving the human ecological footprint. Front. Ecol. Environ. Washington DC 5 (1), 13e18.
Economic growth and the environment Modelling urban population density in a multi-centered city
- G M Grossman
- A B Krueger
Grossman, G.M., Krueger, A.B., May 1995. Economic growth and the environment. Q. J. Econ. Cambridge 110 (2), 353e377. Griffith, D.A., 1981. Modelling urban population density in a multi-centered city. J. Urban Econ. 9, 298e300.
Demographic trends and energy consumption in European Union Nations 36, 855e872. Fig. 8. Scenario of probability of environmental impact. Population density (pop_denskm) (all other drivers hold constant on their global means) Source: elaborated by the authors
- R York
York, R., 2007. Demographic trends and energy consumption in European Union Nations, 1960-2025. Soc. Sci. Res. 36, 855e872. Fig. 8. Scenario of probability of environmental impact. Population density (pop_denskm) (all other drivers hold constant on their global means). Source: elaborated by the authors. E.J. de Mattos, E.E. Filippi / Environmental Modelling & Software 62 (2014) 22e32