Search

The increasing globalization and information communication associated with commerce are confronted with the risk experienced over the years in the various production sectors of the economies. The research studied the influence of ICTs capital goods exports, industrial value addition, transportation energy, and globalization on insurance & financial services in a panel of 29 selected European and Central Asian nations. The research spanned from 2012 to 2021 and used a Panel quantile regression model. It was shown that insurance and financial services increased dramatically between the 0.40th and 0.90th quantiles due to technological progress. In addition, for quantiles between 0.10th and 0.90th, industrial value added is positively correlated with increased insurance and financial services. Quantile 0.10t show a negative and significant association between transportation energy and insurance risks. In contrast, quantiles between 0.40th and 0.90th show a positive and significant relationship, indicating a U-shaped relationship between these two variables. The causality findings validated the technology-led industrialization and transportation energy, while a bidirectional association was identified between globalization and transportation energy across nations. The variance decomposition results suggested that industrial value added, globalization, and technological advancement would exert greater variance shocks on the insurance and financial services sector over time. The study concludes that the progression of technology contributes to the maintenance of insurance performance in the age of globalization. This performance might be enhanced by developing more effective transportation energy services and industrial production.

Due to increasing concern of the Indonesian government towards the ever increasing national level of carbon emissions and the increasing demand of oil, in 2014 the Indonesian National Energy Council released the National Energy Policy which set the target of an increased renewable energy portion to become 23% of the total national energy consumption while setting a limit to oil consumption to be at most 25%. As the road transportation sector consumes more than 60% of the national oil consumption, measures targeting this sector will be important in fulfilling the target. A model has been set up to evaluate the impact of the measures on oil consumption via two main avenues: the substitution of oil based fuels with alternative fuels (e.g. biofuel, compressed natural gas/CNG), the reduction of oil consumption via vehicle retirement and the acceleration of fuel economy improvement for vehicles. Results have shown that it is important that policies consider the presence of older vehicles in the population since there is presently no vehicle retirement policy or penalty for older vehicles resulting in a large number of old vehicles still being operational. Thus, introduction of new vehicles will have a limited effect on the average fuel efficiency. Implementation of a vehicle retirement policy to reduce the amount of older vehicles is shown to be very effective in stemming the increase of oil consumption. In regards to fuel substitution, oil substitution with ethanol is most impactful, as most motor vehicles in Indonesia rely on gasoline including the rapidly increasing motorcycle population. While oil consumption is also reduced by biodiesel substitution, impact is more limited due to a smaller diesel vehicle population with a lower annual increase of vehicle population. In addition, financial, social and regulatory barriers exist that impend implementation of alternative fuels and vehicle limitation schemes. We also evaluated possible barriers and avenues that can further promote these measures to meet the national energy policy target. • The impact of fuel substitution of Indonesian road transportation energy was simulated. • The avenues in order to meet the national energy policy target were evaluated. • Fuel substitution with ethanol is the most impactful for the Indonesian transportation energy sector. • Oil consumption can be also reduced by biodiesel substitution. • Regulatory barriers exist in the implementation of alternative fuels and vehicle limitation schemes. [ABSTRACT FROM AUTHOR]

The transportation sector accounts for 61.5% of global oil consumption and is responsible for 29% of the world's total energy demand. Passenger transportation utilizes around 50%–60% of the energy used for transportation-related activities. Accurate prediction of future transportation energy consumption is essential for governments to make well-informed decisions regarding transportation infrastructure development and utilization, which supports the United Nations' Sustainable Development Goals (SDGs) and advances the shift to a net-zero carbon economy. With the expected increase in population, vehicles, and economic growth, it is essential to predict the energy demand to ensure sustainable urban transportation. This is crucial not only for economic prosperity but also for promoting human health and mitigating carbon emissions. Therefore, transportation energy demand prediction plays a vital role in designing sustainable future urban transportation and making informed energy investment and policy decisions. This study proposes a novel methodology and investigates for the application of machine learning stacking ensemble method with hyperparameter tuning and multicollinearity removal to predict transportation energy demand in Turkey based on historic data from 1975–2019. The dataset includes GDP, year, vehicle miles traveled, population, oil price, passenger miles traveled, and ton-miles traveled as features. A performance evaluation and comparison of 19 machine learning algorithms is first carried out to find the best candidate for the stacking ensemble models, including eXtreme Gradient Boosting algorithm. This performance comparison uses all features and also only two of them during the training phase, and it takes into consideration a 4-fold cross-validation. A combination of permutation importance and hierarchical clustering algorithm on the Spearman rank-order correlations is used for dimensionality reduction of the dataset. Extra Tree Regressor and ADABoost Regressor, which are both placed in the second level of the suggested models, are two meta-regressors that are proposed for stacking ensembles because they perform better compared to single machine learning algorithm. In total, eight stacking ensemble models – four for each of the meta-regressors – were developed and investigated considering all features and only two of them separately. Six metrics – R-squared, MSE, MAE, RMSE, RMSLE, and MAPE – are used to assess all models. The Extra Trees Regressor can be used as a meta-regressor in the best proposed stacking ensemble model to predict the energy demand for transportation. This model achieves an R-squared value of approximately 0.99 when all the features are taken into consideration. When only two features from the dataset are considered the same stacking ensemble model can achieve an accuracy of 0.98. These findings have the potential to contribute to the development of more accurate models and results, which can, in turn, lead to improved strategies for managing future transportation energy demand. Additionally, this research can support the advancement of alternative technologies that promote sustainable urban development, ultimately helping to move towards a net-zero carbon economy. • Novel methodology for predicting transportation energy using stacking ensemble. • Evaluation of 19 ML algorithms and development of 8 stacking ensemble models. • Vehicle-Km and Oil-Price significantly impact energy predictions. • Hierarchical clustering and permutation importance for multicollinearity removal. [ABSTRACT FROM AUTHOR]

Motivated by sustainable development in the manufacturing industry, many approaches have been proposed to reduce the machining energy consumption (MEC) of machine tools at the production stage. However, the design stage determines the majority of the MEC for a machined part, and has greater energy-saving potential than the other stages. The question is how to design an optimal diameter of a part to minimise its future MEC for external turning. If the MEC is reduced by increasing the final diameter based on the same blank, the weight of the part will increase. Consequently, the transportation energy consumption (TEC) of electric vehicles (e.g. automatic guided vehicles) for moving the parts will also increase. Based on this finding, herein we consider a trade-off between the reductions of MEC and TEC. A TEC model is developed and integrated with a modified MEC model to obtain a machine-vehicle system energy consumption (MVSEC) model. Thus, as a novel contribution of this study, an optimised trade-off is proposed between reductions of MEC and TEC by using an integrated optimal design of diameter and cutting parameters. In a case study, the optimal design parameters of a shaft sleeve to be processed by a machine tool (CK6153i) and an electric vehicle (ZX001) were found. The experiment results show that simulated annealing can provide high-quality solutions in a short computation time. The optimal solution achieved a 3.53% MVSEC (28.281 KJ) reduction for a case. Finally, the relationship between transportation distance and optimal diameter is analysed, and the effect of MVSEC optimisation on machining time is discussed. Image 108461 • Energy for machining and transportation is jointly optimised at the design stage. • A machine-vehicle system energy consumption model is developed. • The optimal diameter and cutting parameters are obtained by simulated annealing. • The relationship between transportation distance and optimal diameter is analysed. • The approach achieves a 3.53% energy consumption (28.281 KJ) reduction for a case. [ABSTRACT FROM AUTHOR]

This paper investigates the benefits of exploiting weather conditions for energy optimal driving. Optimization approaches are introduced that are based on the fact that weather-dependent speed profiles can save transportation energy, especially for electric transportation systems. Optimization models for both minimum transportation energy with travel time constraints and minimum travel time with transportation energy constraints are introduced and the resulting speed profiles are analyzed. Infinite dimensional optimization models are proposed for exact problem descriptions and approximate discretized convex models are derived for highly efficient solutions. The optimization tasks are formulated as a deterministic as well as a robust optimization problem, where weather conditions such as wind speed and rolling resistance effects are assumed to be known exactly or within uncertainty bounds. Simulations illustrate the utility of the proposed optimization models and demonstrate achievable efficiency improvements. [ABSTRACT FROM AUTHOR]

It is well-known that societal energy consumption and pollutant emissions from transport are influenced not only by technical efficiency, mode choice and the carbon/pollutant content of energy but also by lifestyle choices and socio-cultural factors. However, only a few attempts have been made to integrate all of these insights into systems models of future transport energy demand or even scenario analysis. This paper addresses this gap in research and practice by presenting the development and use of quantitative scenarios using an integrated transport-energy-environment systems model to explore four contrasting futures for Scotland that compare transport-related 'lifestyle' changes and socio-cultural factors against a transition pathway focussing on transport electrification and the phasing out of conventionally fuelled vehicles using a socio-technical approach. We found that radical demand and supply strategies can have important synergies and trade-offs between reducing life cycle greenhouse gas and air quality emissions. Lifestyle change alone can have a comparable and earlier effect on transport carbon and air quality emissions than a transition to EVs with no lifestyle change. Yet, the detailed modelling of four contrasting futures suggests that both strategies have limits to meeting legislated carbon budgets, which may only be achieved with a combined strategy of radical change in travel patterns, mode and vehicle choice, vehicle occupancy and on-road driving behaviour with high electrification and phasing out of conventional petrol and diesel road vehicles. The newfound urgency of 'cleaning up our act' since the Paris Agreement and Dieselgate scandal suggests that we cannot just wait for the 'technology fix'. [ABSTRACT FROM AUTHOR]

Acknowledging the importance of sectoral energy utilization, the present study explores the impact of industrial, commercial, and agricultural energy consumption and economic progress of Malaysia on environmental degradation as measured by CO2 emissions. Applying the functionality of Cobb-Douglas, the authors utilized the long time series data from the period of 1978 to 2016. The outcomes of the empirical investigations using ARDL bound testing approach ascertain the presence of long-run association between GDP growth, capital, labor along with the sectoral energy utilizations of the country. Furthermore, the study also finds empirical evidences of positive significant associations of all energy consumptions with economic growth. Lastly, long run relationship among environmental degradation and energy consumption and economic growth along with financial development, trade openness, capital stocks, and urbanization as control variables. In addition, FMOLS results confirm that economic growth and energy consumption have a positive and significant impact on environmental degradation. This study brings a valid policy recommendation for the Malaysia Government to invest more in all sector of energy for the progress in the Malaysian economic growth. [ABSTRACT FROM AUTHOR]

The association of energy utilization with economic progress has always proven to be crucial in modern business. Acknowledging the importance of sectoral energy utilization, the present study explores the environmental impact of industrial, commercial, transportation& agricultural energy consumption on economic progress of Malaysia. Applying the functionality of Cobb-Douglas, the authors utilized the long time series data from the period of 1978 to 2016. The outcomes of the empirical investigations using ARDL bound testing approach ascertain the presence of long-run association between GDP growth, capital, labor along with the sectoral energy utilizations of the country. Furthermore, the study also finds empirical evidences of positive significant associations of all energy consumptions with economic growth. Lastly, the results of Granger-causality also confirm the existence of bi-directional causal association between all sectoral energy consumptions and Malaysian economic progress. This study brings a valid policy recommendation for the Malaysia Government to invest more in all sector of energy for the progress in the Malaysian economic growth. [ABSTRACT FROM AUTHOR]

Highlights • An initiative to build 50,000 DC fast stations raises 2050 electrified mileage by 8%. • US initiatives will have sharp returns for GHG reductions through ∼30,000 stations. • An influx of DC fast stations can more effective electrify miles than public level 2. • The above holds even if only 1 DC fast station is built for every 10 level 2 stations. • Infrastructure can reduce GHGs, even if some of the expenses are borne by EV drivers. Abstract This work uses market analysis and simulation to explore the potential of public charging infrastructure to spur US battery electric vehicle (BEV) sales, increase national electrified mileage, and lower greenhouse gas (GHG) emissions. By employing both scenario and parametric analysis for policy driven injection of public charging stations we find the following: (1) For large deployments of public chargers, DC fast chargers are more effective than level 2 chargers at increasing BEV sales, increasing electrified mileage, and lowering GHG emissions, even if only one DC fast charging station can be built for every ten level 2 charging stations. (2) A national initiative to build DC fast charging infrastructure will see diminishing returns on investment at approximately 30,000 stations. (3) Some infrastructure deployment costs can be defrayed by passing them back to electric vehicle consumers, but once those costs to the consumer reach the equivalent of approximately 12 ¢ /kWh for all miles driven, almost all gains to BEV sales and GHG emissions reductions from infrastructure construction are lost. [ABSTRACT FROM AUTHOR]

The United States transportation sector consumes 5 billion barrels of petroleum annually to move people and freight around the country by car, truck, train, ship and aircraft, emitting significant greenhouse gases in the process. Making the transportation system more sustainable by reducing these emissions and increasing the efficiency of this multimodal system can be achieved through several vehicle-centric strategies. We focus here on one of these strategies – reducing vehicle mass – and on collecting and developing a set of physics-based expressions to describe the effect of vehicle mass reduction on fuel consumption across transportation modes in the U.S. These expressions allow analysts to estimate fuel savings resulting from vehicle mass reductions (termed fuel reduction value, FRV), across modes, without resorting to specialized software or extensive modeling efforts, and to evaluate greenhouse gas emission and cost implications of these fuel savings. We describe how FRV differs from fuel intensity (FI) and how to properly use both of these metrics, and we provide a method to adjust FI based on mass changes and FRV. Based on this work, we estimate that a 10% vehicle mass reduction (assuming constant payload mass) results in a 2% improvement in fuel consumption for trains and light, medium, and heavy trucks, 4% for buses, and 7% for aircraft. When a 10% vehicle mass reduction is offset by an increase in an equivalent mass of payload, fuel intensity (fuel used per unit mass of payload) increases from 6% to 23%, with the largest increase being for aircraft. [ABSTRACT FROM AUTHOR]

Energy consumption and greenhouse gas (GHG) emissions of China’s road transport sector have been increasing rapidly in recent years. Previous studies on the future trends trend to focus on the national picture and cannot offer regional insights. We build a novel bottom-up model to estimate the future energy demand and GHG emissions of China’s road transport at a provincial level, considering local economic development, population and policies. Detailed technical characteristics of the future vehicle fleets are analyzed in several up-to date scenarios. The results indicate that China’s vehicle stock will keep increasing to 543 million by 2050. The total direct petroleum demand and associated GHG emissions will peak at 508 million tonnes of oil equivalent (Mtoe) and 1500 million tonnes CO 2 equivalent (Mt CO 2,e ) around 2030 in the Reference scenario. Natural gas vehicle diffusion has a large impact on petroleum demand reduction in the short term, with decreases of 41–46 Mtoe in 2050. Compared to the Reference case, battery electric and fuel cell vehicles will reduce petroleum demand by 94–157 and 28–54 Mtoe in 2050, respectively. When combined with decarbonization of future power supply, battery electric vehicles can play a significant role in reducing Well-to-Wheels GHG emissions in 2050 with 295–449 Mt CO 2,e more reductions. The spatial distributions of future vehicle stock, energy demand and GHG emissions vary among provinces and show a generally downward trend from east to west. Policy recommendations are made in terms of the development of alternative fuels and vehicle technologies considering provincial differences, expansion of natural gas vehicle market and acceleration of electric vehicle market penetration. [ABSTRACT FROM AUTHOR]

Assessment of the energy associated with the material production, transportation and construction of buildings provides an opportunity for reducing the use of energy and improving sustainability. Building Information Modeling (BIM) provides a platform to incorporate sustainability information in the design of buildings. However, interoperability of BIM with Life Cycle Assessment (LCA) tools needs further investigation. Previous research in this area has either partially employed BIM; data was exported from the main BIM authoring tool and then auxiliary tools were utilized to evaluate the model, thereby causing a disconnect between the model and analysis resulting in non-interoperable systems, or has ignored the importance of retaining the LCA results within the BIM environment. To address this issue, this paper presents a framework to estimate the embodied energy content within the native BIM environment. The implementation of this framework is illustrated by the development of a tool for estimation of material embodied energy, transportation energy and construction energy. A prototype of the tool has been implemented on a case project to establish the workability of the framework. [ABSTRACT FROM AUTHOR]

This paper investigates a long-term planning response to the climate-vulnerability of transportation energy infrastructure in the borough of Manhattan, NY. The proposed model, a two-stage stochastic optimization, features a hybrid utility-regret function with increasing relative and decreasing absolute risk aversion. Modeling results suggest (1) investment in early- and late-stage resilience-enhancing solutions as a complementary approach with significant weight on immediate actions, and (2) a decentralized supply chain formation through an early-stage deployment of reservoir tanks within the case study area. [ABSTRACT FROM AUTHOR]

An integrated alternative planning can control climate change drivers and mitigate or neutralize the adverse impacts of the changing climate on the transportation energy sector. In this article, we introduced an infrastructure of alternative fuel as a synergistic approach to climate-adaptation and -mitigation, and advanced a quantitative method to simulate the dependency of travel behavior on fuel availability when the infrastructure of transportation energy is stressed or under attack. Our approach allows both commuters traveling behavior and properties of transportation energy system to interact. Featuring a multi-stage mathematical program, the proposed model was used to study the resilience of New York City's transportation energy sector in the face of a range of climatic extremes. We found the impact of the changing climate on energy sector is major and worsening over time. The modeling results also reveal spatial distribution of vulnerable elements in both transportation and fueling infrastructures, the system's overall resilience in time of disaster, and the daily-commuters impact of vulnerable energy infrastructure. The modeling results are crucial to successful integrated planning response to climate-adaptation and -mitigation. [ABSTRACT FROM AUTHOR]

In the transportation sector, the fuel consumption model is a fundamental tool for vehicles’ energy consumption and emission analysis. Over the past decades, vehicle-specific power (VSP) has been enormously adopted in a number of studies to estimate vehicles’ instantaneous driving power. Then, the relationship between the driving power and fuel consumption is established as a fuel consumption model based on statistical approaches. This study proposes a new methodology to improve the conventional energy consumption modeling methods for hybrid vehicles. The content is organized into a two-paper series. Part I captures the driving power equation development and the coefficient calibration for a specific vehicle model or fleet. Part II focuses on hybrid vehicles’ energy consumption modeling, and utilizes the equation obtained in Part I to estimate the driving power. Also, this paper has discovered that driving power is not the only primary factor that influences hybrid vehicles’ energy consumption. This study introduces a new approach by applying the fundamental of hybrid powertrain operation to reduce the errors and drawbacks of the conventional modeling methods. This study employs a new driving power estimation equation calibrated for the third generation Toyota Prius from Part I. Then, the Traction Force-Speed Based Fuel Consumption Model (TFS model) is proposed. The combination of these two processes provides a significant improvement in fuel consumption prediction error compared to the conventional VSP prediction method. The absolute maximum error was reduced from 57% to 23%, and more than 90% of the predictions fell inside the 95% confidential interval. These validation results were conducted based on real-world driving data. Furthermore, the results show that the proposed model captures the efficiency variation of the hybrid powertrain well due to the multi-operation mode transition throughout the variation of the driving conditions. This study also provides a supporting analysis indicating that the driving mode transition in hybrid vehicles significantly affects the energy consumption. Thus, it is necessary to consider these unique characteristics to the modeling process.

Compact cities have been attributed to lower per capita energy use. However, the complexity of relationships between the elements that constitute energy consumption in the urban system is poorly understood. Little or no research exist on the relation between energy costs of building taller, and transportation and infrastructure energy benefits of building denser. This study provides a theoretical assessment of how energy use is related to urban density in a densely populated area, to aid the development of sustainable cities and land-use planning. The paper builds a holistic parametric model to estimate the total urban energy use for space heating, embodied building energy, transportation energy, and road infrastructure energy, and how these relate to urban density. It does so by varying building height and other urban characteristics related to density, with the aim of identifying the most influential parameters with regard to energy consumption. The possibility of an optimal building height and urban density is also investigated. A much denser and taller city structure than what is normal in cities today appears to be optimal for low urban energy use. The most influential urban density indicators are found to be the dwelling service level (m 2 /cap) and the building design lifetime. Transportation energy becomes increasingly important with a rise in population. Results indicate that depending on population and building lifetime there exists an optimal building height in the range of 7-27 stories. Climate is found to significantly influence the energy results. These preliminary findings are indicative of general trends, but further research and development of the model are needed to reduce uncertainties. [ABSTRACT FROM AUTHOR]

For the first time, low-cost open-source 3-D printing provides the potential for distributed manufacturing at the household scale of customized, high-value, and complex products. To explore the potential of this type of ultra-distributed manufacturing, which has been shown to reduce environmental impact compared to conventional manufacturing, this paper presents a case study of a 3-D printable parametric design for recreational vehicle (RV) solar photovoltaic (PV) racking systems. The design is a four-corner mounting device with the ability to customize the tilt angle and height of the standoff. This enables performance optimization of the PV system for a given latitude, which is variable as RVs are geographically mobile. The open-source 3-D printable designs are fabricated and analyzed for print time, print electricity consumption, mechanical properties, and economic costs. The preliminary results show distributed manufacturing of the case study product results in an order of magnitude reduction in economic cost for equivalent products. In addition, these cost savings are maintained while improving the functionality of the racking system. The additional electrical output for a case study RV PV system with improved tilt angle functionality in three representative locations in the U.S. was found to be on average over 20% higher than that for conventional mass-manufactured racking systems. The preliminary results make it clear that distributed manufacturing - even at the household level - with open-source 3-D printers is technically viable and economically beneficial. Further research is needed to expand the results of this preliminary study to other types of products. [ABSTRACT FROM AUTHOR]

This paper examined the impact of transportation energy policies on traffic safety through policy simulations. Considering the changes in the vehicle miles traveled (VMT) and in vehicle stock composition as a result of policy changes, the impacts of these changes on traffic accidents were examined in terms of the number of traffic accidents, traffic fatalities, and total accident costs. The main focus was on the following policy alternatives: Fuel tax, mileage based a VMT tax, Pay-as-you-drive ( PAYD) and Pay-at-the-pump ( PATP) insurance premium policy, and the Corporate Average Fuel Economy ( CAFE) standards regulations. By integrating three interrelated economic demand decisions fully (size of the vehicle stock, use of the vehicle stock, and energy efficiency), the short-run, long-run and dynamic effects of a policy change can be predicted. The results showed that the share of light trucks will keep increasing in the future in all policy alternatives and that fuel consumption will decrease compared to the baseline in every scenario except for the VMT tax policy. The results also show that the fatality rates per vehicle miles traveled will decrease, but the CAFE policy will result in more fatalities and higher fatality rates compared to the baseline scenario. The results may provide guidance as to what would reduce the energy dependency while reducing the undesirable side effects related to traffic safety. The outcomes of this research will provide a set of specific results comparing policy scenarios in a consistent manner. The results will provide guidance as to whether the policy option would improve energy dependency while reducing the undesirable side effects, such as the problems related to the environment and the safety of motor vehicle travel. [ABSTRACT FROM AUTHOR]

Properly determining the driving range is critical for accurately predicting the sales and social benefits of battery electric vehicles (BEVs). This study proposes a framework for optimizing the driving range by minimizing the sum of battery price, electricity cost, and range limitation cost--referred to as the "range-related cost"--as a measurement of range anxiety. The objective function is linked to policy-relevant parameters, including battery cost and price markup, battery utilization, charging infrastructure availability, vehicle efficiency, electricity and gasoline prices, household vehicle ownership, daily driving patterns, discount rate, and perceived vehicle lifetime. Qualitative discussion of the framework and its empirical application to a sample (N = 361664) representing new car drivers in the United States is included. The quantitative results strongly suggest that ranges of less than 100 miles are likely to be more popular in the BEV market for a long period of time. The average optimal range among U.S. drivers is found to be largely inelastic. Still, battery cost reduction significantly drives BEV demand toward longer ranges, whereas improvement in the charging infrastructure is found to significantly drive BEV demand toward shorter ranges. The bias of a single-range assumption and the effects of range optimization and diversification in reducing such biases are both found to be significant. [ABSTRACT FROM AUTHOR]

Transportation provides access to economic opportunities, empowers individuals and adds value to their communities. However, the average household in the United States spends 17 % of their income on transportation expenditures and almost 4 % on fuel costs. In response to both budgetary and social implications of facing burdens to meet transportation energy needs, we propose a new data-driven framework to quantify transportation energy vulnerability. We measure exposure, sensitivity, and adaptive capacity to transportation energy burdens and present spatial patterns of transportation energy vulnerability across the United States census tracts. We examine the sensitivity of the vulnerability outcomes to the composite score's functional form and evaluate the impact of electric vehicle adoption on transportation fuel and electricity consumption. Generating the composite vulnerability score as an additive index is robust when estimating the impact of adaptive capacity interventions, but the multiplicative index performs better when exposure and sensitivity metrics are priorities. The market growth of electric vehicles reduces spatial disparities in energy vulnerability but has diminishing returns over time. We present the geography of vulnerability in Los Angeles, Chicago, and New York and find that populations in urban areas in Chicago and New York experience less transportation energy vulnerability. On the other hand, city center and rural regions' populations face a greater transportation energy burden in Los Angeles. • Quantifying exposure, sensitivity, and adaptive capacity to transport energy burden. • Additive and multiplicative transportation energy vulnerability composite scores. • Electric vehicles reduce spatial disparities in transportation energy vulnerability. • Limited and underfunded transit leads to low adaptive capacity to transport energy burden. [ABSTRACT FROM AUTHOR]

Abstract: The growth of desktop 3-D printers is driving an interest in recycled 3-D printer filament to reduce costs of distributed production. Life cycle analysis studies were performed on the recycling of high density polyethylene into filament suitable for additive layer manufacturing with 3-D printers. The conventional centralized recycling system for high population density and low population density rural locations was compared to the proposed in home, distributed recycling system. This system would involve shredding and then producing filament with an open-source plastic extruder from post-consumer plastics and then printing the extruded filament into usable, value-added parts and products with 3-D printers such as the open-source self replicating rapid prototyper, or RepRap. The embodied energy and carbon dioxide emissions were calculated for high density polyethylene recycling using SimaPro 7.2 and the database EcoInvent v2.0. The results showed that distributed recycling uses less embodied energy than the best-case scenario used for centralized recycling. For centralized recycling in a low-density population case study involving substantial embodied energy use for transportation and collection these savings for distributed recycling were found to extend to over 80%. If the distributed process is applied to the U.S. high density polyethylene currently recycled, more than 100 million MJ of energy could be conserved per annum along with the concomitant significant reductions in greenhouse gas emissions. It is concluded that with the open-source 3-D printing network expanding rapidly the potential for widespread adoption of in-home recycling of post-consumer plastic represents a novel path to a future of distributed manufacturing appropriate for both the developed and developing world with lower environmental impacts than the current system. [Copyright &y& Elsevier]