Is Globalization Detrimental to CO<Subscript>2</Subscript> Emissions in Japan? New Threshold Analysis

Using annual data from 1970 to 2014, this paper examines the effects of globalization on CO₂ emissions in Japan while accounting for economic growth and energy consumption as potential determinants of carbon emissions. The structural breaks and asymmetries arising due to policy shifts require attention, and hence, an asymmetric threshold version of the ARDL model is utilized. The results show the presence of threshold asymmetric cointegration between variables. Threshold-based positive and negative shocks arising from globalization increase carbon emissions, while the impact of the latter is more profound. Energy consumption (economic growth) also has a significant positive effect on carbon emissions. Globalization, economic growth, and energy consumption significantly increase carbon emissions in the short run. We suggest that policy makers in Japan consider globalization and energy consumption as policy tools in formulating their policies regarding protecting sustainable environmental quality in the long run. Otherwise, the Japanese economy may continue to face environmental consequences such as undesirable climate change and massive warming at the micro and macro levels as a result of potential shocks arising from globalization and energy consumption.     

Mean Vehicle Speed Distributions for the Spatiotemporal Estimation of Exhaust Emissions

Link Emissions Models estimate traffic-related air pollution emissions at the individual road link level and inform governmental policies for air quality management. The current South Australian Link Emissions Model (CLEM) assumes constant spatiotemporal traffic flow at a single fixed mean speed, a potential limitation as the variability of exhaust emissions with vehicle speed has been established in the literature.We extend CLEM to eliminate the assumption of constant traffic flow, through the derivation of mean Australian vehicle speed distributions for different road types. Specifically, we successfully model the vehicle speed profile data from the second National In-Service Emissions study using Nearest Neighbour Kernel Density Estimation. We propose a mean speed Distribution Link Emissions Model (DLEM) for exhaust emission estimation based on the derived mean speed distributions. DLEM is an augmented, enhanced version of CLEM, accommodating a range of vehicle speeds and road types. The performance of the extended model, DLEM, is analysed in comparison to the current model, CLEM, through a case study analysis of vehicle exhaust emissions on a typical arterial road in Adelaide, South Australia. Results indicate use of DLEM and, by extension, mean vehicle speed distributions, has a strong impact on emission estimation. In particular, the fixed speed model, CLEM, may be substantially underestimating exhaust emissions of carbon monoxide, non-methane volatile organic compounds and particulate matter less than 2.5 μm in diameter. These are common exhaust pollutants that have been extensively linked with adverse health effects including respiratory morbidity and premature mortality.     

Emission-based static traffic assignment models

Tailpipe emissions in the road transportation system are a major source of air pollution and greenhouse gases. One of the possible approaches is to influence drivers’ routing decisions such that the emissions and fuel consumption is minimized. In order to evaluate such condition, we develop environmental traffic assignment (E-TA) models based on user equilibrium (UE) and system optimal (SO) behavioral principles. Extending the traditional travel time-based UE and SO principles to E-TA is not straightforward because, unlike travel time, the rate of emissions increases with the increase in vehicle speed beyond a certain point. The results of various TA models show a network-wide traffic control strategy in which vehicles are routed according to SO-based E-TA, can reduce system-wide emissions. However, a system in which drivers make routing decisions to minimize their own emissions (E-UE system) results in a paradoxical situation of increased individual as well as system-wide emissions.     

An Evaluation of Microscopic Emission Models for Traffic Pollution Simulation Using On-board Measurement

As a result of the continuously increasing numbers of motor vehicles in metropolitan areas worldwide, road traffic emission levels have been recognized as a challenge during the planning and management of transportation. Experiments were conducted to collect on-road emission data using portable emission measurement systems in two Chinese cities in order to estimate real traffic emissions and energy consumption levels and to build computational models for operational transport environment projects. In total, dynamic pollutant emissions and fuel consumption levels from dozens of light duty vehicles, primarily from four different vehicle classes, were measured at a second-by-second level. Using the collected data, several microscopic emission models including CMEM, VT-Micro, EMIT, and POLY were evaluated and compared through calibration and validation procedures. Non-linear optimization methods are applied for the calibration of the CMEM and EMIT models. Numerical results show that the models can realize performance levels close to the CMEM model in most cases. The VT-Micro model shows advantages in its unanimous performance and ability to describe low emission profiles while the EMIT model has a clear physics basis and a simple model structure. Both of them can be applied when extensive emission computation is required in estimating environmental impacts resulting from dynamic road traffic.     

Carbon monoxide and carbon dioxide concentrations in Santiago de Chile associated with traffic emissions

CO/CO₂ ratios have been measured in different locations of Santiago de Chile city. Measurements were carried out in a tunnel (prevailing emissions from cars with catalytic converter) and close to heavy traffic streets. Concentrations measured along the city traffic tunnel or temporal profiles of concentrations measured near heavy traffic streets allow an estimation of CO/CO₂ ratios emitted from mobile sources. Values obtained range from 0.0045 ± 0.0006 to 0.0100 ± 0.0004 and depend on the prevailing type of mobile sources. In particular, lowest values were found close to a street with heavy traffic dominated by diesel-powered public transportation, while the highest values were found at the city tunnel. Places located near streets of mixed mobile sources (public buses and cars) showed intermediate values. Average CO/CO₂ ratios are compatible with emission factors proposed for Santiago’s main mobile sources.     

Diurnal models of traffic-generated CO for Penang,Malaysia

Georgetown of Penang, an old city, is noted for its narrow streets. The existing traffic dispersal system is utterly inadequate to cope with the ever increasing number of cars and motorcycles on the road. The principal objective of this study is to build prediction models of CO to be employed as one of the planning tools in the future design of Penang urban traffic dispersal system. This study involves the monitoring of kerbside CO levels at selected sites and the fitting of hourly-averaged CO data to linear regression models incorporating the residual effect of CO emission due to traffic in the earlier periods and also different categories of vehicles. The best overall regression model appears to be the one based upon the total traffic count of motorcycles. This can be accounted for by the fact that the traffic counts of motorcycles and cars are highly correlated in most cases and that the emissions of CO from motorcycles are more readily detected as they travel closer to the kerb. The inclusion of residual CO in the models significantly improves the correlation coefficient from about 0.4 to about 0.7.     

Exposure of motorcycle,car and bus commuters to carbon monoxide on a main road in the Tel Aviv metropolitan area,Israel

Short-term personal exposure of passengers in different types of motor vehicles to carbon monoxide was investigated in an intensively used main road in Israel’s Tel Aviv metropolitan area. According to monitoring stations of the Ministry for Environmental Protection (MEP), concentrations of carbon monoxide (CO) along the road, at a height of 3 m above pedestrian level, in the Tel Aviv metropolitan area, are currently very low. However, these measurements do not reflect the actual exposure of commuters, which were the main objective of this study. Four vehicle types/travel modes were investigated: private cars with closed windows, private cars with open windows, motorcycles, and buses. The commuter CO average exposure was the accumulative exposure divided by the duration of the sampling taken along the route, for each type of vehicles. The results showed that commuters in cars with closed windows were exposed to the highest mean CO level, 27.2 ppm, for a period of 38 min; those in a car with open windows, to 19.7 ppm for 38 min; motorcycle riders, to 12.8 ppm, for 17 min; and bus users were exposed to the lowest mean pollution level, of only 3.6 ppm, for 25 min. Thus, CO values of 1 to 3 ppm, as measured at an MEP adjacent monitoring station, may indicate the exposure to CO pollution of area residents, but do not represent the actual exposure of commuters on the congested main road.     

Meat,Dairy and Climate Change: Assessing the Long-Term Mitigation Potential of Alternative Agri-Food Consumption Patterns in Canada

We use a newly developed model of the entire Canadian energy system (TIMES-Canada) to assess the climate change mitigation potential of different agri-food consumption patterns in Canada. For this, our model has been extended by disaggregating the agricultural demand sector into individual agri-food demands to allow for a more in-depth analysis. Besides a business-as-usual (baseline) scenario, we have constructed four different agri-food scenarios to assess the viability of reducing Canadian meat and dairy consumption in order to diminish Canada’s agricultural sector energy consumption and greenhouse gas (GHG) emissions. Our policy scenarios progressively restrict the consumption of different meat and dairy agricultural products until the year 2030. Our results suggest that the implementation of a meat and dairy consumption reduction policy would lead to a 10 to 40 % reduction in agricultural GHG emissions, depending on the severity of the scenario. This translates to a 1 to 3 % decrease in total Canadian GHG emissions by the year 2030. Besides these environmental benefits, health benefits associated with a reduction in meat and dairy consumption (as inferred from other studies) are presented as an additional source of motivation for implementing such a policy in Canada.     

Can buildings sector achieve the carbon mitigation ambitious goal: Case study for a low-carbon demonstration city in China?

China is committed to peaking its carbon emissions by 2030 and become a carbon-neutral society by 2060. The building sector that accounts for over one-third of the total carbon emissions is expected to face a great challenge in helping China achieve this goal. Shenzhen, as a low-carbon pilot city, whether its low-carbon work of urban buildings reaches the target is crucial. An attempt has been made in this study to assess the intensity of carbon emissions and associated reduction efficiency of urban buildings (operation stage) in Shenzhen by using the life cycle assessment method. The results show that the total carbon emissions generated from the buildings' operation stage have increased from 22 million metric tons (Mt) CO₂eq in 2005 to 42 (±13%) Mt. CO₂eq in 2019. Carbon emissions mainly result from the buildings' electricity use (79%), followed by refrigerant release emissions (12%). The energy conservation and carbon emissions reduction intensity in Shenzhen is at the middle level in China, and there is considerable space for improvement. According to scenario-based analysis, the carbon emission of the buildings sector can probably reach its peak by 2025 with the implementation of suitable policies – 5 years earlier than national target by 2030. Overall, this study makes a systemic analysis of the characteristics of urban buildings energy consumption and carbon emissions reduction, which can provide supportings for justifying the effectiveness of low-carbon activities in Shenzhen and beyond.     

The technological innovation of hybrid and plug-in electric vehicles for environment carbon pollution control

Plug-in Hybrid Electric Vehicles (PHEV) have been promoted by providing Vehicle-to-Grid (V2G) infrastructure as a possible solution to reduce greenhouse gas (GHG) and other emissions by utilizing energy instead of oil for effective environmental management. The promising solution for reducing air pollution in cities is commonly regarded as electric vehicles, which helps to optimize the environment management more effectively, as a key to future low carbon mobility. However, their environmental benefits rely on the temporal and spatial sense of real use, and challenges such as limited range complicated for the rollout of an Electric Vehicle (EVs). This paper investigates the environmental carbon pollution in cities and control preventions using Plug-in Hybrid Electric Vehicles (PHEV). Further, the Artificial intelligence model has been introduced, which defines optimal automobile designs and the assignment of vehicles to drivers across a variety of scenarios, including minimum net life cycle expense, GHG emissions, and oil usage for effective environmental management. By designing overspent vehicle power for corresponding output, weight, and cost impact, the life cycle costs and the emission of GHG are reduced utilizing high battery swinging and replacing batteries as needed. Moreover, energy consumption (EC) and pollution have been greatly influenced by the use of energy sources in the environment. The significant energy consumption and pollution variables resulted in a large proportion of coal-fired energy. The results show that the PHEV can achieve better fuel economy by combining the proposed model with an allowable deviation from the state of the charge.     

A dynamic analysis of the global warming potential associated with air conditioning at a city scale: an empirical study in Shenzhen,China

Heating, ventilation and air conditioning (HVAC) systems are a major source of energy consumption in buildings, directly and indirectly contributing to greenhouse gas (GHG) emissions. In the urban environment, and depending on local climatic conditions, air conditioning units attribute to these high energy demands. This study analyzes the use of residential air conditioning units and their associated global warming potential (GWP) between 2005 and 2030 for the city of Shenzhen, a fast-growing megacity located in Southern China. A life cycle assessment approach was adopted to quantify the GWP impacts which arise from both direct (refrigerant release) and indirect (energy consumption) sources, in combination with a materials flow analysis approach. The results show that the total GWP (expressed as carbon dioxide equivalents, CO₂ eq.) from residential air conditioning systems increased from 2.2 ± 0.2 to 5.1 ± 0.4 million tonnes (Mt) CO₂ eq. between 2005 and 2017, with energy consumption and refrigerant release contributing to 72.5% and 27.5% of the total demands, respectively. Immediate measures are required to restrict refrigerant release and reduce the energy consumption of air conditioning units, to help mitigate the predicted additional total emissions of 36.4 Mt. CO₂ eq. potentially released between 2018 and 2030. This amount equals to approximately New Zealand's national CO₂ emissions in 2017. The findings proposed in this study targets air conditioning units to reduce the GWP emissions in cities, and provide useful data references and insights for local authorities to incentivise measures for improving building energy efficiency management and performance.     

Influencing factors of carbon emissions in transportation industry based on CD function and LMDI decomposition model: China as an example

Globally, the transportation industry is one of the leading fields that generate the largest share of greenhouse gas emissions. While undergoing rapid development, countries worldwide aim to solve the problems involved in high energy consumption. Taking China as an example, this paper studies the main factors of carbon emissions in the transport sector and analyses the decoupling states between carbon emission and economic growth, making energy efficiency policies accordingly. In order to better demonstrate the dependence of the economy on the carbon emissions in China's transportation industry comprehensively, combined with the CD production function, this paper develops the decomposition and decoupling technology based on the LMDI approach. Additionally, it quantifies seven effects: energy emission intensity effect, energy structure effect, energy intensity effect, transportation intensity effect, technology state effect, labor input effect and capital input effect. The results show three major points: (1) From 2001 to 2018, the cumulative carbon emissions of China's transportation industry increased by 633.46 million tons, in which the capital input effect is the key factor driving carbon emissions, accounting for 157.70% of the total cumulative increased emissions, followed by energy structure effect at 10.39%. The labor input effect accounted for the smallest proportion at 2.26%. In this case, the technology state effect is the primary factor in restraining carbon emissions. During the study period, it reduced carbon emissions by 292.27 million tons, accounting for 46.14%. To a certain extent, energy intensity effect, transportation intensity effect and energy emission intensity inhibited carbon emissions, representing 16.67%, 5.32% and 2.22%, respectively. (2) During the research period, two decoupling states existed between carbon emissions and economic growth in China's transportation industry, specifically weak decoupling and expansive coupling. (3) The analysis of decomposition and decoupling state of influencing factors of carbon emissions shows that, on the one hand, factors promoting carbon emissions (capital input effect, energy structure effect and labor input effect) hinder the decoupling process. On the other hand, factors restraining carbon emissions (technology state effect, transportation intensity effect, energy intensity effect and energy emission intensity effect) accelerate the decoupling process. The research findings provide a new perspective for achieving carbon emission reduction in the transportation industry and curbing energy consumption growth.     

The Impact of Road Traffic in Madrid Community by Using a Nested Gaussian-Mesoscale Air Quality Model (GAMA)

In this contribution we show the integration of a mesoscale air quality model OPANA with the ISCST3 Gaussian model (EPA) in order to analyze the impact of different emission sources and particularly the traffic emission into the different gridboxes which define the OPANA Eulerian structure. The application is done over the Madrid (Spain) regional area with 80 × 100 km and gridboxes of about 5 km. Thousands of Gaussian runs over interested gridboxes are executed in order to simulate the traffic emissions from each gridbox. Each mobile unit is represented by a Gaussian point emitter. Input meteorological variables for the ISCST3 are taken from the OPANA mesoscale air quality model. Results shows that it is possible to model the impact of traffic emissions over each gridbox. A short comparison with air quality monitoring in each gridbox is also shown.     

Estimating transportation carbon efficiency (TCE) across the Belt and Road Initiative countries: An integrated approach of modified three-stage epsilon-based measurement model

Transportation systems are vital links for intercountry. However, the transportation industry is associated with high energy consumption and carbon emissions. In this paper, the transportation carbon efficiency (TCE) across the Belt and Road Initiative (BRI) countries during 2005–2017 is estimated by modifying a three-stage epsilon-based measurement model, and the carbon emission reduction potential (CERP) is identified. Based on the results, countries are classified into four categories by comparing a country's TCE and CERP with the average of all BRI countries. The results show that the average TCE of BRI countries is only 0.341, while their average CERP is 0.750, which is tremendous. It also shows that the higher the income levels, the more prone countries are to have a higher TCE. By considering the differences among the countries' environmental factors, TCEs, and the current state or trends of the CERPs, customized low-carbon policies are proposed to increase the TCE and reduce emissions.     

Estimating the mitigation potential of the Chinese service sector using embodied carbon emissions accounting

It is universally recognized that direct carbon emissions based on energy consumption and industrial production lead to carbon leakage and inequality. This paper employs input–output analysis (IOA) and the hypothetical extraction method (HEM) to establish an embodied carbon analysis framework to resolve the above externalities. As a typical downstream consumption industry, the service sector has had very little work examining its embodied carbon transfer structure and related climate policies. In this paper, carbon flows of China's service sector between 1997 and 2015 are mapped and a scenario analysis is conducted that accounts for the service sector development plan and carbon emissions reduction targets. The results demonstrate that 13–19% of carbon flows in the Chinese economy are caused by the service sector's demand of other sectors. Controlling the industry scale and carbon intensity of its upstream industries effectively mitigates the dramatic growth of embodied carbon emissions in the service sector. The embodied carbon emissions accounting framework might provide new insights for the definition of emissions reduction responsibility on both a regional and sectoral scale. The further exploration of the service industry from this novel perspective will be helpful in realizing China's overall carbon emissions reduction goals.     

Does the high–tech industry consistently reduce CO2 emissions? Results from nonparametric additive regression model

China is currently the world's largest carbon dioxide (CO₂) emitter. Moreover, total energy consumption and CO₂ emissions in China will continue to increase due to the rapid growth of industrialization and urbanization. Therefore, vigorously developing the high–tech industry becomes an inevitable choice to reduce CO₂ emissions at the moment or in the future. However, ignoring the existing nonlinear links between economic variables, most scholars use traditional linear models to explore the impact of the high–tech industry on CO₂ emissions from an aggregate perspective. Few studies have focused on nonlinear relationships and regional differences in China. Based on panel data of 1998–2014, this study uses the nonparametric additive regression model to explore the nonlinear effect of the high–tech industry from a regional perspective. The estimated results show that the residual sum of squares (SSR) of the nonparametric additive regression model in the eastern, central and western regions are 0.693, 0.054 and 0.085 respectively, which are much less those that of the traditional linear regression model (3.158, 4.227 and 7.196). This verifies that the nonparametric additive regression model has a better fitting effect. Specifically, the high–tech industry produces an inverted “U–shaped” nonlinear impact on CO₂ emissions in the eastern region, but a positive “U–shaped” nonlinear effect in the central and western regions. Therefore, the nonlinear impact of the high–tech industry on CO₂ emissions in the three regions should be given adequate attention in developing effective abatement policies.     

The impacts of population change on carbon emissions in China during 1978–2008

This study examines the impacts of population size, population structure, and consumption level on carbon emissions in China from 1978 to 2008. To this end, we expanded the stochastic impacts by regression on population, affluence, and technology model and used the ridge regression method, which overcomes the negative influences of multicollinearity among independent variables under acceptable bias. Results reveal that changes in consumption level and population structure were the major impact factors, not changes in population size. Consumption level and carbon emissions were highly correlated. In terms of population structure, urbanization, population age, and household size had distinct effects on carbon emissions. Urbanization increased carbon emissions, while the effect of age acted primarily through the expansion of the labor force and consequent overall economic growth. Shrinking household size increased residential consumption, resulting in higher carbon emissions. Households, rather than individuals, are a more reasonable explanation for the demographic impact on carbon emissions. Potential social policies for low carbon development are also discussed.     

Urban form and spatial structure as determinants of per capita greenhouse gas emissions considering possible endogeneity and compensation behaviors

Modifying the form and spatial structure of cities through urban planning can be an effective means to reduce greenhouse gas (GHG) emissions in cities. The supporters of the Compact City Approach to urban sustainability propose dense and centralized urban systems. In the case of population density, they argue that it promotes displacements of foot and public transport, and that typical apartments of compact fabrics require less energy than single-family dwellings. Therefore, high density should lead to low GHG emissions. During the last decade this association has been questioned because: a) there may be compensatory behaviors (more energy consumption and more GHG emissions in mobility and housing during weekends and holidays, and b) the fact of not considering the effects of the endogeneity associated with self-selection. In this paper, we analyze population density as a determinant of mobility and residential GHG emissions in Gran Concepción (Chile) using multivariate regression models. The results obtained indicate that density does not exert a significant impact on GHG emissions in mobility and housing. It is income differences that mostly explain individual GHG emissions variability. This calls into question the possible effectiveness of compactness policies in regional, cultural and climatic contexts different from those of the US and Europe and are excessively oriented towards the maintenance and increase of density in urban centers and slowing down the expansion of suburban neighborhoods.     

Validation of Urban Emission Inventories

Two emission validation methods are presented. The first method focuses on the precision of the emission factors and the accuracy of modelled traffic flows. Emission factors derived from the COPERT II methodology are compared with on-board emission measurements and modelled traffic flow rates are compared with observations. The second validation method focuses on the completeness of the inventory, i.e. coverage of all sources. The method compares measured pollutant fluxes in the urban plume with the downwind transported and dispersed emissions integrated over plume width and mixing height. Both methods seem to indicate that traffic emission factors used in the urban emission inventories show large uncertainties. Besides the lack of measurement precision this is mainly induced by external influence factors like driving behaviour and vehicle maintenance.