1,720,975 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Urbanization and Inequality/Poverty
No full textThe level of world urbanization has crossed the 50% mark, and nearly all future population growth is projected to occur in cities. Cities are disproportionately wealthy, but are associated with poverty, too. Addressing the dual challenges of urbanization and poverty is key to achieving sustainable development. This paper performs cross-sectional regressions, based on Kuznets, as a starting point for understanding the relationship between urbanization and poverty/inequality indicators. Increases in gross domestic product per capita unambiguously lowered poverty and narrowed rural-urban gaps. By contrast, levels of urbanization were either unrelated to poverty/inequality indicators and measures of rural-urban gaps, or had a nonlinear effect where, initially, increases in urbanization likewise led to improvements in those areas, while at higher levels of urbanization, increases in urbanization exacerbated poverty and rural-urban gaps
What Is the Temporal Path of the GDP Elasticity of Energy Consumption in OECD Countries? An Assessment of Previous Findings and New Evidence
No full textThis paper answers the question: what is the path of the GDP elasticity of economy-wide energy consumption for OECD countries over the period 1960–2019? To do so, this study first considers the arguments as to why this elasticity might change over time, and then reviews the previous evidence on whether this elasticity has changed over time. Lastly, the paper compiles and uses a new dataset to analyze whether the GDP elasticity of energy demand in OECD countries (i) has changed between the periods before and after the major energy crises (e.g., 1974–1985); and (ii) has been stable since 1986. Elasticity stability is analyzed via rolling window regressions using dynamic mean group cross-correlated errors. We argue that (i) the GDP elasticity for economy-wide energy consumption was around unity for OECD countries prior to the first energy crisis; and (ii) the reactions to the extreme oil price experiences that occurred over 1974–1985 led to a substantially lower GDP elasticity for economy-wide energy consumption of around 0.6 that has been stable at that level since the end of the second energy crisis (circa 1986). This demonstration of the path of the GDP elasticity is in contrast to some recent work that has suggested the GDP elasticity of energy has not changed (or changed very little) since the 1970s or even since the 1960s. Furthermore, this evidence that reactions to those extreme oil price experiences led to a step-function-like lowering of the GDP elasticity runs counter to other arguments that dematerialization, inverted-U-based development paths, or Kyoto Protocol ratification are responsible for continued declines in the GDP elasticity
The challenge of sustainability in a global system: documentation of a transdisciplinary, multi-country, dynamic simulation model
Full text linkSustainability models should consider aspects of the economy-environment-population nexus, be dynamic, and acknowledge the disparity among actors/countries. Lastly, sustainability models should not be programmed either to reject sustainability (e.g., an essential, nonrenewable input) or to affirm it (e.g., costless, endogenous technical change). We develop a simulation model to assess sustainable development on three levels: economic (by determining production, consumption, investment, direct foreign investment, technology transfer, and international trade), social (by calculating population change, migration flows, and welfare), and environmental (by calculating the difference between environmental pollution and upgrading expenditures). The model follows “representative” countries that differ in their initial endowments (i.e., natural resource endowment, physical and human capital, technology, and population), and thus in their development levels and prospects. In addition, we model free substitution in production, flexible economic structures, the ability to upgrade input factors via investment, and optimizing agents who possess a high degree of mobility and information, and who interact through and in response to market equlibiria.economic development, environment, population dynamics, simulation
Consumption-Driven Environmental Impact and Age Structure Change in OECD Countries
Full text linkThis 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.demography, environment, FMOLS panel cointegration, GHG emissions projections, IPAT, STIRPAT
Demographic dynamics and per capita environmental impact: using panel regressions and household decompositions to examine population and transport
Full text linkDemographic variables have tended to be ignored in many environment-development analyses. This paper examines how population changes (in aging, households, and urbanization/density) can help explain changes/differences in personal transport using both macro- and micro- level data. First, panel regressions are performed with IEA-OECD road sector energy use data (spanning 1960-2000) on spatial population measures, average household size, and age structure data. Then US household data is used to determine the extent compositional changes in the nature of households can explain changes in per capita driving. An Environmental Kuznets Curve for per capita road energy use was rejected—the coefficients on the GDP squared terms were insignificant and the implied turning points were well outside the sample range; instead, the relationship between wealth and road energy was found to be monotonic (log-linear). The ideas that more densely populated countries have less personal transport demands, the young drive more, and smaller households mean higher per capita driving were confirmed. The basic result from the household decompositions was that changes in demand were more important than compositional changes, however, during some periods the compositional change component was considerable. A few policy implications can be drawn from these analyses. First, the look at micro data implies that there is much potential for policy to affect transport behavior since the compositional component of change—more difficult for policy to alter—is smaller than the behavioral or demand component. However, the look at the macro data implies that spatial factors, like population density and urbanization—which also can be difficult to alter—are significant in influencing personal transport demand.OECD countries, energy consumption, environmental policy, household size, transport
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