Emissions In Transport Sector Research Articles

Use of Solar Energy to Increase the Sustainability of Shared Micromobility

This study continues the examination of potential ways to decrease greenhouse gas (GHG) emissions in the urban transportation sector by using shared scooters in Riga. Despite claims by shared scooter operators that these vehicles are environmentally friendly, shared e-scooters contribute an additional 239.9 tonnes of CO2 in Riga. While energy is not the largest source of CO2 emissions for shared scooters, it is one factor that could be improved by switching to locally produced green energy. This study revaluates the CO2 emissions from shared scooters based on an additional year of data. It also analyses the possibility of generating enough energy on-site to provide chargers with PV energy.For this purpose, the study utilizes the last seven years of solar energy data in Latvia to model the potential energy flows that can be used to charge the electric scooters. This data is then combined with information from 3 million scooter trips from the 2021-2022 scooter seasons, analysing the trips and the energy used during scooter use. It was determined that, on average, 59.6Wh were used per trip, with a standard deviation of 18.9 Wh. The model was tested with various levels of PV cell areas and energy storage systems to determine the minimum parameters required for the scooters to be provided with enough energy without the need for additional grid charging.The results demonstrate that it is indeed possible to provide enough solar energy to ensure continuous charging of the electric scooters for 98.8% of the cases during the scooter operation period with just 0.2m² of solar cells per scooter. In conclusion, while this approach does not completely offset the CO2 emissions generated by the short scooter lifespan, it is a step towards greener urban transport. Further research will be carried out to optimize the charging locations for the evaluation of practical PV charging viability.

Tourism Transport-Related CO2 Emissions and Economic Growth: A Deeper Perspective from Decomposing Driving Effects

This study explores the intricate dynamics of CO2 emissions stemming from transport within the tourism sector. It aims to unravel the multidimensional aspects of how transport-related tourism contributes to CO2 emissions and to elucidate the complex relationship between regional economic growth and CO2 emissions from transport-related tourism. Our study employed Logarithmic Mean Divisia Index (LMDI) and Panel Vector Autoregression (PVAR) models to analyze data from 30 Chinese provincial regions between 2010 and 2018. The tourism transport-related CO2 emissions were decomposed into four separate driving effects using the LMDI approach. Then, PVAR models were constructed to reveal dynamic interactions between each driving effect and per capita gross regional product (GRP). Our results demonstrate a decrease in both energy structure effect (tourism transport-related CO2 emissions from energy structure) and energy intensity effect (tourism transport-related CO2 emissions from energy intensity) during the pre-COVID decade. Notably, the positive impulse response of energy structure effect to per capita GRP is observed. However, we found no evidence of a cointegrated relationship between energy intensity effect and regional economic growth, although other factors demonstrated connections. These findings echo the necessity to integrate sustainable practices into the tourism transportation business, especially in the area of energy structure, in order to mitigate adverse environmental effects from tourism. This paper disseminates the main drivers of CO2 emissions in the tourism transport sector and their interrelationship with regional economic growth. It not only guides tourism policymakers in targeting efforts to reduce carbon footprints, but also sets a new benchmark for future studies on CO2 emissions.

The Current State of the National Electric Vehicle Infrastructure Program Funding

Reducing the transportation sector's greenhouse gas (GHG) emissions by transitioning away from fossil fuels and its impact on communities and the environment continues to receive considerable attention. In 2022, the U.S. transportation sector was responsible for generating the largest share of the United States' GHG emissions at approximately 37 percent of total emissions.1 Electric vehicles (EVs) represent a viable option that can contribute significantly to reducing transportation sector emissions. As EVs become increasingly prevalent, ensuring the availability of adequate charging infrastructure to support these vehicles is a top priority of the Biden Administration and many states.

Grid Chaos: An uncertainty-conscious robust dynamic EV load-altering attack strategy on power grid stability

Electric Vehicles (EVs) and their charging infrastructure have witnessed rapid adoption into the smart grid owing to their ability to reduce the transportation sector's emissions. However, this hasty deployment has left the grid prone to attacks initiated through the charging ecosystem. This paper develops a family of robust dynamic load-altering attacks that manipulate the EV load to destabilize the power grid. The attack strategy is formulated based on feedback control theory and Linear Matrix Inequalities (LMIs) and incorporates mathematical modeling of the power grid uncertainties. The effectiveness of our proposed robust EV attack strategy is demonstrated through extensive simulations of attack scenarios against the New England (NE) 39-bus grid. The results demonstrate that the proposed attacks were able to destabilize the grid frequency inducing a deviation of 1.5 Hz and forcing the generators to trip after compromising as little as 6.6% of the total grid load. We also present a two-tier Deep Learning (DL) attack detection mechanism. We optimize this mechanism to achieve optimal results against dynamic and switching attacks. The proposed mechanism was evaluated against several other models and proved to be superior with an accuracy of 99.9%.

Exploring Urban Compactness and Greenhouse Gas Emissions in the Road Transport Sector: A Case Study of Big Cities in South Korea

This study examined the relationship between urban compactness and greenhouse gas (GHG) emissions in the road transport sector in South Korea, focusing on 84 cities, particularly 27 metropolitan areas with populations of approximately 500,000. We developed an urban compactness index (UCI) using Moran’s I, entropy, and the Gini coefficient, integrating city size into the analysis. Cities were categorized into five groups based on their size to analyze GHG emissions and regional variations in compactness comparatively. Our results revealed a significant inverse relationship between UCI and per capita road transport GHG emissions, which was more pronounced in larger cities. Specifically, cities with a population over 1 million displayed reduced per capita road transport GHG emissions in compact urban structures. In conclusion, these findings suggest that larger cities can effectively reduce per capita road transport GHG emissions through urban planning for compact development. Additionally, planners need to consider city size when analyzing the UCI and formulating urban planning strategies aimed at achieving carbon neutrality.

Environmental performance assessment in the transport sector using nonparametric frontier analysis: A systematic literature review

The increasing environmental issues relating to carbon dioxide emissions are a primary concern globally and have triggered excessive research to investigate possible ways to reduce such emissions, especially in the transport sector, as initiated by Sustainable Development Goal (SDG) 13. This study investigates the importance of the nonparametric frontier analysis methodology, particularly Data Envelopment Analysis, in measuring environmental performance in the transport sector, emphasising how EU countries are working on meeting the recommendations of SDG 13. Researchers and policymakers have indisputably identified the transport sector as the primary source of global emissions. This paper aims to underline the significant environmental trends in the transport sector, including research topics, key works, research methods, future research direction, and recommendations to explore possible global or regional research agendas. In this regard, we mainly focus on various techniques used to measure the environmental performance within the transport sector. This research considers 186 articles from 46 journals. The survey's main findings show the ever-increasing attention paid to studying the transport sector's emissions, emphasising road and passenger car CO2 emissions as the major source of emissions in the transport sector. In the existing literature, the top three frequently adopted methodologies for measuring environmental performance in transportation include Data Envelopment Analysis, emission analysis, and simulation. This study shows research gaps and future directions on environmental performance assessment within the transport sector, particularly maritime and aviation.

Driving factors and clustering analysis of expressway vehicular CO2 emissions in Guizhou Province, China.

Expressways are essential for intercounty trips of passenger travel and freight mobility, which are also an important source of vehicular CO2 emissions in transportation sector. This study takes the expressway system of Guizhou Province as the research objective, and establishes the multi-year expressway vehicular CO2 emission inventories at the county level from 2011 to 2019. We employ the extended STIRPAT model incorporating ridge regression to identify driving factors from six different aspects, and then utilize the affinity propagation cluster method to conduct the differentiation research by dividing Guizhou's counties into four clusters. Based upon clustering analysis, localized and targeted policies are formulated for each cluster to reduce expressway vehicular CO2 emissions. The results indicate that generally: (1) Guizhou's expressway vehicular CO2 emissions manifest a continuously upward trend during 2011-2019. Small-duty passenger vehicle (SDV), light-duty truck (LDT), and heavy-duty truck (HDT) contribute to the largest CO2 emissions in eight vehicle types. (2) GDP and population are the foremost two positive driving factors, followed by urbanization rate and expressway length. The proportion of secondary industry is also a positive driver, but that of tertiary industry exhibits an opposite effect. (3) Regional disparity exists in four county clusters of Guizhou Province. Efficient policies are proposed, such as improving the layout and infrastructure of transportation hubs, promoting multimodal integration, and implementing industrial upgrading as per regional advantages. Sustainable expressway vehicular CO2 emission reduction is realized from both the source of industry and low-carbon modes of transport.

Fuel Cell Hybrid Electric Vehicles: Fuel Cell experimental characterization and modeling towards the development of a Hardware-in-the-Loop platform for advanced powertrain design

Greenhouse gas emissions in transport sector is a critical issue, with automobiles and trucks responsible for emitting nearly 5.9Gt of CO2. Addressing climate change is then of paramount importance to avoid irreversible consequences. Advanced technologies are being developed to achieve this goal. Among them, Fuel Cell Hybrid Electric Vehicles (FCHEVs) offer several benefits, including wide applicability and range, short refueling times, and no tailpipe emissions, that can only be exploited if optimizing their design. This requires time and cost-consuming iterative processes, and Digital Twins (DTs) and Hardware-in-the-Loop (HIL) test benches are then gaining importance with respect to prototype manufacturing. In this study, the basis for a HIL application is posed, including a 2kW Fuel Cell (FC) and a bidirectional DC power supply used to emulate the electric motor. After characterizing the FC, DTs have been calibrated and validated through experimental tests; DTs have then been included into a FCHEV microcar model and its performances have been analyzed. Results have shown high accuracy in representing the real FC performance, with a 5% maximum error from the energy standpoint. Moreover, HIL approach has been proved to be fundamental for catching peculiar transient phenomena, significantly impacting on the powertrain design and performance.

An interpretable multi-stage forecasting framework for energy consumption and CO2 emissions for the transportation sector

The transportation sector is deemed one of the primary sources of energy consumption and greenhouse gases throughout the world. To realise and design sustainable transport, it is imperative to comprehend relationships and evaluate interactions among a set of variables, which may influence transport energy consumption and CO2 emissions. Unlike recent published papers, this study strives to achieve a balance between machine learning (ML) model accuracy and model interpretability using the Shapley additive explanation (SHAP) method for forecasting the energy consumption and CO2 emissions in the UK's transportation sector. To this end, this paper proposes an interpretable multi-stage forecasting framework to simultaneously maximise the ML model accuracy and determine the relationship between the predictions and the influential variables by revealing the contribution of each variable to the predictions. For the UK's transportation sector, the experimental results indicate that road carbon intensity is found to be the most contributing variable to both energy consumption and CO2 emissions predictions. Unlike other studies, population and GDP per capita are found to be uninfluential variables. The proposed multi-stage forecasting framework may assist policymakers in making more informed energy decisions and establishing more accurate investment.

Understanding the challenge of decoupling transport-related CO2 emissions from economic growth in developing countries

The transition to net zero requires full decarbonization of the transport sector, currently one of the leading sources of emissions globally. Transport-related carbon emissions are growing fastest in the developing world. This makes it particularly critical to understand whether low- and middle-income countries are making progress towards decoupling transport sector emissions from economic growth, as well as the extent to which their current emissions trajectory is being shaped by structural factors versus more amenable policy choices. This paper assembles and analyzes a comprehensive dataset on transport-related emissions with a uniquely broad coverage of developing countries. The paper employs the Tapio decoupling model over the period 1990–2018 to demonstrate that high-income countries are almost twice as likely to have reached relative decoupling as low and middle-income countries (70 vs 36 percent, while the latter are almost twice as likely to be in a state of negative decoupling as the former (17 versus 41 percent). This paper conducts index-decomposition and econometric analysis to shed light on the factors driving transport-related carbon emissions. Index decomposition reveals that there have been only relatively modest reductions in the transport emissions intensity of GDP since 1990 and that these have not been large enough to offset economic growth in middle-income countries and demographic growth in low-income countries. Regression analysis further shows that urbanization and industrialization are important correlates of transport-related emissions, while the correlation of policy choices with reduced emissions is rather weak.

Matheuristics for a multi-day electric bus assignment and overnight recharge scheduling problem

To limit greenhouse gas emissions in the public transportation sector, transit authorities are constantly increasing the proportion of electric buses (EBs) in their fleets. Due to space and budget constraints, more often than not, the number of chargers that can be installed at the depots is less than the number of EBs in the fleet. Moreover, even if there is one charger for each EB, it is virtually impossible to use all the chargers at the same time due to maximum power grid constraints. Thus, there is a need to devise optimization techniques that help to recharge the buses in an efficient and acute way, taking into account these charging infrastructure capacity constraints. In this study, we consider a multi-day electric bus assignment and overnight recharge scheduling problem that can be briefly stated as follows. Given a set of vehicle blocks (sequences of timetabled bus trips performed by the same bus on a given day) to be operated over several days, a set of identical EBs, and a set of chargers available at the depot whose charging function is a piecewise linear function, the problem consists in assigning an EB to each block and to schedule the overnight recharging operations at the depot such that the EBs never run out of energy when performing their blocks and the depot charging capacity is never exceeded. The objective is twofold: minimizing the total charging costs and minimizing the impact of the charging decisions in the long-term health of the batteries. We first model this problem as a mixed integer linear program (MILP) that can be solved with a commercial solver. Then, to yield faster computational times, we develop two multi-phase matheuristics based on the MILP. To show the interest of considering a multiple-day horizon, we introduce two other matheuristics that mimic current industrial practices and solve the problem sequentially, one day at a time. To evaluate all these algorithms, we use a set of 264 instances generated from data of an EB operator.

Measurement report: Source apportionment and environmental impacts of volatile organic compounds (VOCs) in Lhasa, a highland city in China

Abstract. Hypoxia and adverse health outcomes might be affected by O3 pollution in the highland city of Lhasa. NOx emissions can amplify the role of volatile organic compounds (VOCs) in the secondary production of O3 under the conditions of high ultraviolet (UV) radiation levels and unfavourable dispersion patterns in the Lhasa River valley. Here, online C2–C11 VOC measurements, accompanied by other parameters concerning the O3 chemical budget, were first obtained and employed to identify the key VOC species and key sources of VOCs in terms of the loss rate against OH radicals (LOH), ozone formation potential (OFP), secondary organic aerosol potential (SOAP), and toxicity. Oxygenated VOCs (OVOCs) not only were the most abundant VOCs but also dominated LOH, OFP, and toxicity. Isoprene and anthropogenic VOCs were further identified as precursors of these OVOCs. Aromatics accounted for 5 % of the total VOCs (TVOCs) but contributed 88 % to the SOAP and 10 % to the toxicity. As the primary oxidative intermediates of aromatics were not well characterized by our measurements, the environmental impact of aromatics could be underestimated by our data. Source appointment and ternary analysis of benzene, toluene, and ethylbenzene confirmed the combined contribution of traffic emissions, solvent usage, and biomass burning. This suggests that O3 precursors are mainly from sources associated with residents' lives, except for solvent usage emissions which contribute to aromatics. Preliminary comparisons between source spectrums of transport sector emissions with positive matrix factorization (PMF)-decomposed ones and our measured ones suggest that vehicle emission patterns of VOCs at high altitude generally follow the ground-level impression. More quantitative data are required to further confirm this point though. Emission reduction strategy analysis for O3 pollution control highlighted multiple benefits of the simultaneous reduction in NOx originating from diesel vehicle emissions, biomass burning, and background sources (possibly dominated by incense burning). The notable biogenic emission contribution to the OFP was also first confirmed in our study, and this highlights the side effects of the government's pursuit of a greener city.

Life Cycle Assessment of a novel functionally integrated e-axle compared with powertrains for electric and conventional passenger cars

Road transport significantly contributes to climate change and air pollution. Efforts to reduce transport sector emissions include deploying battery electric vehicles and designing their powertrains for improved performance. The European H2020 funded Functionally Integrated E-axle Ready for Mass Market Third GENeration Electric Vehicles (FITGEN) developed a novel functionally integrated e-axle (the FITGEN e-axle) for electric vehicles. This paper presents the environmental performance of the FITGEN e-axle. Using the Life Cycle Assessment (LCA) methodology, the study compares the FITGEN e-axle to the 2018 State-of-the-Art (SotA) e-drive, besides diesel and petrol-fuelled powertrains. The FITGEN powertrain reduces climate impacts by 10 % and energy consumption by 17 %, compared with the 2018 SotA e-drive due to the efficiency improvements and components integration. It also outperforms the 2018 SotA e-drive in several other impact categories, such as human toxicity (4–10 %), land use (19 %), and mineral depletion (8 %). However, the FITGEN powertrain only outperforms diesel and petrol powertrains in climate change and fossil resource scarcity impact categories. These findings imply that more efforts are required to improve the environmental profile of electric powertrains. Metal mining and production, especially for copper and aluminium, are critical for toxicity impacts. The sensitivity analysis demonstrates the robustness of the results, with no significant shift in their ranking order. The following aspects should be considered to improve the performance of electric powertrains from a life cycle perspective: improvement of components efficiency, reduced use of electronics and component integration, and use of low-carbon energy mix from their metal mining sites to production and use.

Electromobility Prospects in Greece by 2030: A Regional Perspective on Strategic Policy Analysis

Electromobility represents a strong option for reducing carbon emissions in the road transport sector. This study presents a methodology and a simulation tool that project the evolution of the market share of electric vehicles (EVs) in the new car market. The analysis adopts a stylized regional resolution, which accounts for attributes on the NUTS-2 level, such as the population density, GDP/capita, education levels, and current EV charger distribution, to simulate the uptake of BEVs in different regions. The methodology applies discrete choice modelling techniques, considering tangible and intangible factors, including purchasing and operation costs, an estimated cost for range anxiety and public charging, and a market maturity index. The analysis is based on four different scenarios, referring to the updated Greek National Energy Climate Plan. The results reveal that regions with a higher average income, GDP/capita, and population density show a higher uptake of EVs. Overall, the tool implements a method of simulating the market evolution of EVs up to 2030 in reference to regional parameters and, hence, highlights the regions that require the most attention in order to achieve national targets. The results can inform policymakers in developing tailored strategies and financial support to accelerate the adoption of BEVs, particularly in regions where their uptake prospects are lower.

Environmental Sustainability Analysis Using Moderation of Transport Competitiveness for Industrial-Induced EKC

Combating adversities of climate change and promoting clean energy are two important goals of the Sustainable Development Goals (SDGs). The nations of the World have developed various commitments to reduce global warming. The transportation sector share is above twenty percent in total global Greenhouse Gas emissions which is continuously rising over time. The Intergovernmental Panel on Climate Change (IPCC) in its recent report conducted a scenario analysis and suggested reducing net emissions by 2050. The transport competitiveness index indicates the quality and expansion of transport services. Accordingly, this research study uses the data of 121 countries for which transport competitiveness is documented by the World Economic Forum (WEF). The period of the study is confined to 2008-2018. The sample countries are segregated into four groups on the basis of transport sector emissions. The study uses Panel Quantile Regression (PQR) framework to estimate results and to validate the industrial Environmental Kuznets Curve (EKC). The findings indicate that transport competitiveness mitigates transport sector emissions in the majority of groups/categories. Inverted U shape industrial EKC has been authenticated in group-1 and U shape industrial EKC for the other three groups. The moderation of transportation competitiveness specified the flattening of industrial EKC across all quantile groups. The empirical findings indicate that moderation results in the sustainability of industrial EKC at higher levels of industrial growth. Among other variables, the governing ability of institutions and planned population expansion are observed in mitigating emissions. The policy options are discussed depending on the results of the groups.

Testing the impact of renewable energy and oil price on carbon emission intensity in China's transportation sector.

As the largest carbon emitter in the world, with its transportation sector contributing the largest shares of its emission, the need for a low-carbon transition economy has become a policy agenda for China because in order to reach carbon neutrality by 2050, lowering the intensity of carbon emissions in the transportation sector will be crucial. In this regard, we used the "bootstrap autoregressive distributed lag model" to explore the impact of clean energy and oil prices on the intensity of carbon emissions in China's transportation sector. The study found that an increase in oil prices decreases the intensity of carbon emissions in the short and long run. Similarly, an increase in the level of renewable energy and economic complexity declines the intensity of carbon emissions in the transportation sector. On the contrary, the research demonstrates that non-renewable energy contributes positively to carbon emission intensity. Therefore, the authorities must promote green technology to neutralize the transportation system's detrimental effects on China's environmental quality. The implications for successfully promoting carbon emission intensity mitigation in the transportation sector are examined in the conclusion.

Exploratory policy analysis for electric vehicle adoption in European countries: A multi-agent-based modelling approach

To reach climate neutrality goals, European countries need to reduce their transportation sector emissions. To this end, implementing effective incentive policies to accelerate electrical vehicle (EV) adoption plays a vital role. With this study, we highlight the important role of governments, showing that even with the provision of certain mild non-financial incentives, such as charging infrastructure development, EV adoption rates can be significantly increased. We develop a multi-agent model of EV adoption within European countries up to the year 2030. This integrated framework can capture the interplay between technical, financial, and social aspects of the EV adoption process. We find that an annual increase of only 10% in the charging infrastructure incentives over ten years can increase the average European EV adoption rate to 46%. We observe that countries that have both a low level of charging point density and a high population density would benefit more from the provision of charging infrastructure incentives. Countries with lower overall achieved EV shares, on the other hand, are found to be relatively insensitive to such provisions. We also characterize how a higher level of charging point density will lead to more rapid EV adoption.

Research on the Relationship between CO2 Emissions, Road Transport, Economic Growth and Energy Consumption on the Example of the Visegrad Group Countries

This study examines the cointegration and short- and long-run causal relationships between economic growth and CO2 emissions, fuel consumption and the amount of freight transported by road transport in the Visegrad Group countries between 1990 and 2019. The autoregressive distributed lag (ARDL) bound testing methodology was used to develop the results for each tested dependent variable. The results confirmed a long-run cointegration between economic growth and fuel consumption by road transport in the Czech Republic and Slovakia. In addition, a long-run relationship between economic growth and freight volume and CO2 emissions by transport was demonstrated for Slovakia. For Poland, there was only a long-run relationship between energy consumption by road freight transport and CO2 emissions. In the short run, relationships were shown between the volume of freight transported by the road freight transport sector and CO2 emissions for Hungary and Poland. Short-run relationships between economic growth and the volume of freight transported in Poland, the Czech Republic and Hungary are also interesting observations. The obtained results expand the information resources needed to make decisions on the direction of change in road transport. Additionally, the results can be used to prepare a proper environmental policy for road freight transport within the framework of the European Green Deal.

Development of greenhouse gas emissions baseline and identification of carbon offset cost for maritime vessels of a developing country

Maritime transportation has drawn international attention due to the gradual rise and projected growth of Greenhouse Gas (GHG) emissions resulting from fossil fuel consumption. It is alarming that the overall maritime transportation emissions are neither attended to nor mainstreamed under the transportation sector. The actual national inventory of GHG emissions vis-à-vis all types/sizes of maritime vessels is so far not established particularly in developing countries, which clearly indicates the inadequacy of their climate mitigation response. Accurate assessment of GHGs is essential to provide reliable input for climate policy, strategies, and decision-making processes by flag states. Therefore, the establishment of a baseline reference scenario by considering all types/sizes of maritime vessels is crucial to know the actual gravity of the problem, which is still unknown. This entailed the need to explore the actual extent of GHG emissions from the maritime transportation sector. In this context, the present study tried to assess the potential GHG emissions from maritime vessels by undertaking the case of Pakistan and using the top-down approach, which took into account fuel consumption and emission factors for GHGs. It revealed that 2,468,789.21 tonnes of GHGs (CO2e) are being emitted annually from the maritime vessels of Pakistan, which is 4.9% of the overall transport sector emissions of the country. Carbon offset cost of 37, 031, 838.14 US$/annum and approximately 20,020 hectares of mature mangrove forest to remove 2,468,789.21 metric tonnes of CO2e emissions from the atmosphere in a timeline of 1 year are required to become carbon neutral. It is anticipated that this study’s outcome will serve as a baseline reference scenario for national GHG inventory and help in devising climate mitigation responses for maritime vessels by bridging the existing knowledge gap.

Economic and environmental impacts of EVs promotion under the 2060 carbon neutrality target—A CGE based study in Shaanxi Province of China

As the world's largest emitter of CO2, China has committed to achieving carbon neutrality by 2060. For the high emission of the transport sector, the promotion of electric vehicles (EVs) is an important way to reduce CO2 emissions in the road transport sector. Due to the current high proportion of coal-fired power in Shaanxi province, the indirect emission of EVs is greater than the direct emission of fuel vehicles. In this study, two regional dynamic CGE models were constructed to evaluate the economic and environmental impact of EVs promotion and power generation structure optimization under the carbon neutral target in Shaanxi province. The results show that the promotion of EVs and optimization of the power generation structure will reduce the carbon emissions of electricity used in the transport sector from 4.5 million tons to 0.1 million tons. Compared with the baseline scenario, the NOX, SO2, and PM emission in the carbon neutrality scenarios has decreased by 50%, 65%, and 32%, respectively in 2060. Further, the GDP loss of carbon neutrality scenarios compared to the baseline scenario in 2060 is between 7.9% and 12.3%. Fortunately, the promotion of EVs and the promotion of the power generation structure can help to optimize the allocation of carbon emission allowance, which would alleviate the GDP loss caused by the carbon neutrality target. So it is necessary to encourage the development of EVs and vigorously develop renewable energy at the same time in other provinces with similar power structure like Shaanxi.