7,940 research outputs found

    Does public transit improvement affect commuting behavior in Beijing, China? : A spatial multilevel approach

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    Developing countries like China have experienced substantial city transformations over the past decade. City transformations are characterized by transportation innovations that allow individuals to access to speedy commuting modes for work activities and offer potential influences on commuting behavior. This paper examines the potential effects of subway system expansion in Beijing on commuting behavior. Our methodological design controls for spatial effects by employing Bayesian multilevel binary logistic models with spatial random effects. Using cross-sectional individual surveys in Beijing, the results suggest that there is a significant rise in subway commuting trips while non-motorized and bus commuting trips are reduced with the new subway expansion. Model comparison results show evidence about the presence of spatial effects in influencing the role of built environment characteristics to play in the commuting behavior analysis

    Geospatial Big Data analytics to model the long-term sustainable transition of residential heating worldwide

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    Geospatial big data analytics has received much attention in recent years for the assessment of energy data. Globally, spatial datasets relevant to the energy field are growing rapidly every year. This research has analysed large gridded datasets of outdoor temperature, end-use energy demand, end-use energy density, population and Gros Domestic Product to end with usable inputs for energy models. These measures have been recognised as a means of informing infrastructure investment decisions with a view to reaching sustainable transition of the residential sector. However, existing assessments are currently limited by a lack of data clarifying the spatio-temporal variations within end-use energy demand. This paper presents a novel Geographical Information Systems (GIS)-based methodology that uses existing GIS data to spatially and temporally assess the global energy demands in the residential sector with an emphasis on space heating. Here, we have implemented an Unsupervised Machine Learning (UML)-based approach to assess large raster datasets of 165 countries, covering 99.6% of worldwide energy users. The UML approach defines lower and upper limits (thresholds) for each raster by applying GIS-based clustering techniques. This is done by binning global high-resolution maps into re-classified raster data according to the same characteristics defined by the thresholds to estimate intranational zones with a range of attributes. The spatial attributes arise from the spatial intersection of re-classified layers. In the new zones, the energy demand is estimated, so-called energy demand zones (EDZs), capturing complexity and heterogeneity of the residential sector. EDZs are then used in energy systems modelling to assess a sustainable scenario for the long-term transition of space heating technology and it is compared with a reference scenario. This long-term heating transition is spatially resolved in zones with a range of spatial characteristics to enhance the assessment of decarbonisation pathways for technology deployment in the residential sector so that global climate targets can be more realistic met

    Simulating the deep decarbonisation of residential heating for limiting global warming to 1.5C

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    Whole-economy scenarios for limiting global warming to 1.5C suggest that direct carbon emissions in the buildings sector should decrease to almost zero by 2050, but leave unanswered the question how this could be achieved by real-world policies. We take a modelling-based approach for simulating which policy measures could induce an almost-complete decarbonisation of residential heating, the by far largest source of direct emissions in residential buildings. Under which assumptions is it possible, and how long would it take? Policy effectiveness highly depends on behavioural decision- making by households, especially in a context of deep decarbonisation and rapid transformation. We therefore use the non-equilibrium bottom-up model FTT:Heat to simulate policies for a transition towards low-carbon heating in a context of inertia and bounded rationality, focusing on the uptake of heating technologies. Results indicate that the near-zero decarbonisation is achievable by 2050, but requires substantial policy efforts. Policy mixes are projected to be more effective and robust for driving the market of efficient low-carbon technologies, compared to the reliance on a carbon tax as the only policy instrument. In combination with subsidies for renewables, near-complete decarbonisation could be achieved with a residential carbon tax of 50-200Euro/tCO2. The policy-induced technology transition would increase average heating costs faced by households initially, but could also lead to cost reductions in most world regions in the medium term. Model projections illustrate the uncertainty that is attached to household behaviour for prematurely replacing heating systems

    Lock-in effects of road expansion on CO2 emissions : results from a core-periphery model of Beijing

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    In the urban planning literature, it is frequently explicitly asserted or strongly implied that ongoing urban sprawl and decentralization can lead to development patterns that are unsustainable in the long run. One manifestation of such an outcome is that if extensive road investments occur, urban sprawl and decentralization are advanced and locked-in, making subsequent investments in public transit less effective in reducing vehicle kilometers traveled by car, gasoline use and carbon dioxide emissions. Using a simple core-periphery model of Beijing, the authors numerically assess this effect. The analysis confirms that improving the transit travel time in Beijing’s core would reduce the city’s overall carbon dioxide emissions, whereas the opposite would be the case if peripheral road capacity were expanded. This effect is robust to perturbations in the model’s calibrated parameters. In particular, the effect persists for a wide range of assumptions about how location choice depends on travel time and a wide range of assumptions about other aspects of consumer preferences.Transport Economics Policy&Planning,Roads&Highways,Energy and Environment,Environment and Energy Efficiency,Economic Theory&Research,Urban Transport

    The Consumption-Based Carbon Emissions in the Jing-Jin-Ji Urban Agglomeration Over China's Economic Transition

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    Since the 2008 financial crisis, China has been undergoing an economic transition consisting of prioritizing green economic and sustainable development instead of rapid growth driven by large-scale investment. However, there is still a lack of fine print on how subregional effort can contribute to national or full supply chain mitigation plans, especially downscaling to the city level. To bridge this knowledge gap, we selected Jing-Jin-Ji urban agglomeration, one of the economic centers but also featured by intensive emission for decades, to analyze the emission variance and driving forces from 2012 to 2015 as a case study. Based on the consumption accounting framework, the carbon emissions of Jing-Jin-Ji have decreased by 11.7 Mt CO2 in total over the study period, and most cities showed the similar descending trend. The driving forces show that the emission intensity and production structure have largely reduced Jing-Jin-Ji's total due to measurements of economic transition. For instance, Beijing has decreased by 28.7 Mt of emission reduction which led by declined emission intensity. By contrast, per capita demands and growth of its population were the primary forces to increase emissions. To conclude, although the mitigation achievement is undeniable, we should also note that the economic transition has not changed the uneven pattern of selected urban agglomeration so far

    Constraining a Historical Black Carbon Emission Inventory of the United States for 1960–2000

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    We present an observationally constrained United States black carbon emission inventory with explicit representation of activity and technology between 1960 and 2000. We compare measured coefficient of haze data in California and New Jersey between 1965 and 2000 with predicted concentration trends and attribute discrepancies between observations and predicted concentrations among several sources based on seasonal and weekly patterns in observations. Emission factors for sources with distinct fuel trends are then estimated by comparing fuel and concentration trends and further substantiated by in‐depth examination of emission measurements. We recommend (1) increasing emission factors for preregulation vehicles by 80–250%; (2) increasing emission factors for residential heating stoves and boilers by 70% to 200% for 1980s and before; (3) explicitly representing naturally aspired off‐road engines for 1980s and before; and (4) explicitly representing certified wood stoves after 1985. We also evaluate other possible sources for discrepancy between model and measurement, including bias in modeled meteorology, subgrid spatial heterogeneity of concentrations, and inconsistencies in reported fuel consumption. The updated U.S. emissions are higher than the a priori estimate by 80% between 1960 and 1980, totaling 690 Gg/year in 1960 and 620 Gg/year in 1970 (excluding open burning). The revised inventory shows a strongly decreasing trend that was present in the observations but missing in the a priori inventory.Key PointsSystematic evaluation of long‐term U.S. black carbon observations identifies a small number of poorly estimated emission sourcesUpdated black carbon emission is higher than the previous estimate by 80% for 1960–1980, showing a decreasing trend as found in observationEmission factors for preregulation vehicles, off‐road engines, and residential heating stoves in 1980 and before should be increasedPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149266/1/jgrd55339_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149266/2/jgrd55339.pd

    The Consumption-Based Carbon Emissions in the Jing-Jin-Ji Urban Agglomeration Over China's Economic Transition

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    Abstract Since the 2008 financial crisis, China has been undergoing an economic transition consisting of prioritizing green economic and sustainable development instead of rapid growth driven by large‐scale investment. However, there is still a lack of fine print on how subregional effort can contribute to national or full supply chain mitigation plans, especially downscaling to the city level. To bridge this knowledge gap, we selected Jing‐Jin‐Ji urban agglomeration, one of the economic centers but also featured by intensive emission for decades, to analyze the emission variance and driving forces from 2012 to 2015 as a case study. Based on the consumption accounting framework, the carbon emissions of Jing‐Jin‐Ji have decreased by 11.7 Mt CO2 in total over the study period, and most cities showed the similar descending trend. The driving forces show that the emission intensity and production structure have largely reduced Jing‐Jin‐Ji's total due to measurements of economic transition. For instance, Beijing has decreased by 28.7 Mt of emission reduction which led by declined emission intensity. By contrast, per capita demands and growth of its population were the primary forces to increase emissions. To conclude, although the mitigation achievement is undeniable, we should also note that the economic transition has not changed the uneven pattern of selected urban agglomeration so far

    Sensitivity and Uncertainty Analyses of an Urban Forest Structure and Function Model

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    Urban forest models can quantify forest structure and benefits, and are frequently employed in decision-making. This dissertation first reviewed case studies of urban forest modeling practices over the past two-decades, compared the similarities and differences among different models, and summarized the current trends and gaps in the field of urban forest modeling. One gap is the lack of uncertainty assessments for model output. To address this gap, this dissertation performed sensitivity and uncertainty analyses for a popular urban forest model, i-Tree Eco. Based on a case study in New York City, the sensitivity analyses found that the most important input variables are genus for isoprene and monoterpene emissions, DBH for carbon estimators, and leaf area index, temperature, and photosynthetically active radiation for dry deposition estimators. The uncertainty analyses addressed uncertainties associated with the entire i-Tree Eco modeling process, from input data collection, to the characterization of urban tree structure, to the subsequent estimators of the ecosystem services of urban trees. Uncertainty magnitudes were quantified by employing bootstrap and Monte Carlo simulations, and the three sources of uncertainty, input, model, and sampling, were aggregated to derive an estimator of total uncertainty. Through case studies in 16 cities across the United States, the average magnitude of total uncertainty across the 16 cities was 12.4% for leaf area, 12.4% for leaf biomass, 13.5% for carbon storage, 11.1% for carbon sequestration, 40.7% for isoprene emissions, and 25.0% for monoterpene emissions. For leaf and carbon estimators, the total uncertainty is primarily driven by sampling uncertainty, while the magnitudes of sampling, input and model uncertainty are similar across the 16 study cities. In contrast, input, sampling, and model uncertainties all contribute similarly to the total uncertainty for isoprene and monoterpene emission estimators, and there are larger variations in these three sources of uncertainty across the 16 study cities. To reduce overall uncertainty, future studies should develop more accurate urban-, local-, and species-specific allometric relationships, improve the spatial representation of meteorological variables, develop more extensive and accurate local-scale measurements to calibrate and verify models, and improve sampling strategies
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