Scenario analysis of energy consumption and related emissions in the transportation industry-a case study of Shaanxi Province

In the context of pursuing carbon neutrality and balancing the use of fossil fuels with renewable energy, the transportation industry faces the challenge of accurately predicting energy demand, related emissions, and assessing the effectiveness of energy technologies and policies. This is crucial for formulating energy management plans and reducing carbon dioxide (CO2) and atmospheric pollutant emissions. Currently, research on energy consumption and emission forecasting primarily relies on energy consumption quantities and emission factors, which lack precision. This study employs the low emissions analysis platform (LEAP) model, utilizing a "bottom-up" modeling approach combined with scenario analysis to predict and analyze the energy demand and related emissions in the transportation industry. Compared to previous studies, the methodological framework proposed in this research offers higher precision and can explore energy-saving and emission-reduction pathways for different modes of transport, providing a valuable energy forecasting tool for transport policy formulation in other regions. The forecast results indicate that under the business-as-usual (BAU) scenario, by 2049, the energy consumption and related emissions in Shaanxi Province's transportation industry are expected to increase by 1.15 to 1.85 times compared to the baseline year. In the comprehensive (CP) scenario, the industry is projected to reach a carbon peak around 2033. The study also finds that energy consumption and emissions predominantly originate from private passenger vehicles, highway freight, and civil aviation passenger, which have the greatest potential for emission reduction under the transport structure optimized (TSO) scenario. Therefore, policymakers should consider regional development characteristics, combine different transportation modes, and specifically analyze the emission reduction potential of the transportation industry in various regions, formulating corresponding reduction policies accordingly.

[Coordinated Control of Carbon Emission Reduction and Air Quality Improvement in the Industrial Sector in Hunan Province]

Climate warming and air pollution are the main environmental problems in China. This study used China's Carbon Accounting Database, energy economic model, and air quality model to analyze the potential carbon emission peaking path and synergistic air quality improvement gain in the industrial sector in Hunan Province. Based on China's Carbon Accounting Database and the local industry/energy statistical yearbooks in Hunan, the total CO2 emissions in Hunan Province in 2019 were 310.6 Mt, of which the industrial sector accounted for over 70% of the emissions, mainly from the production and supply of electricity, steam, and heat; the production of non-metallic minerals; and the smelting and pressing of ferrous metals. Three potential industrial carbon emission peaking scenarios were analyzed using the LEAP energy economic model, including the business-as-usual scenario (peaking by 2030), moderate emission reduction scenario (peaking by 2028), and aggressive emission reduction scenario (peaking by 2025), by employing different economic growth rates, energy technology progress, and energy structures of the industrial sector. Furthermore, by combining the anthropogenic air pollutant emission inventory and the regional air quality model WRF-Chem, we analyzed the air quality improvement associated with various carbon emission peak paths. The results showed that the annual mean concentrations of major air pollutants had decreased in the three scenarios, especially in the Chang-Zhu-Tan Region. The aggressive emission reduction scenario was the most effective scenario, followed by the moderate emission reduction scenario and the business-as-usual scenario. Manufacturing was the sector with the most significant synergistic effect of pollution and carbon reduction. When carbon emission peaks were achieved, the annual average concentrations of PM2.5 and PM10 in Hunan Province could be synergistically reduced by 0.6-1.8 μg·m-3 and 1.8-8.9 μg·m-3, respectively. Our findings offer important insights into carbon emission peaking and can provide useful information for potential mitigation actions.

[Medium and Long-term Carbon Emission Projections and Emission Reduction Potential Analysis of the Lingang Special Area Based on the LEAP Model]

Based on the existing statistical data of the Lingang Special Area in Shanghai and considering its future socio-economic development， industrial structure， and technological development， a LEAP-Lingang model was developed to analyze the evolution trends of energy demand and carbon emissions under the baseline scenario， low-carbon scenario， and enhanced low-carbon scenario. To enhance the prediction accuracy of the model， the Logistic population growth model was used to predict future population data， and the learning curve model was used to simulate the cost evolution trend of related carbon reduction technologies. In addition， an economic evaluation model for carbon reduction technologies was developed， and the economic costs and emission reduction potential of typical carbon reduction technologies were evaluated by drawing a marginal emission reduction cost curve. The results showed that under the enhanced low-carbon scenario， the renewable energy accounted for 69% of the primary energy consumption， and the electric energy accounted for 91% of the terminal energy demand in 2060. The Lingang Special Area could achieve carbon peak by 2030， and the carbon emissions in 2060 were predicted to decrease by 94% compared to that in the baseline scenario. In terms of contribution to emission reduction， clean energy substitution， industrial structure optimization， and terminal energy efficiency improvement played a key role in reducing carbon emissions near the port. In the medium term （until 2035）， they were predicted to contribute 35.1%， 27.3%， and 16.2% of carbon emissions， respectively， and in the long term （until 2060）， they should contribute 50.6%， 8.75%， and 7.7% of carbon emissions， respectively. Regarding specific carbon reduction technologies， hydrogen power generation； water electrolysis for hydrogen； and carbon capture， utilization， and storage （CCUS） technology were of great significance for achieving net-zero emissions， but the costs of emission reduction were relatively high. The research results can provide ideas and references for the low-carbon and green development of the Lingang Special Area and related areas.

[Study of Peak Carbon Emission of a City in Yangtze River Delta Based on LEAP Model]

Based on the LEAP model framework, a LEAP-X sub-sector calculation model suitable for X City was constructed in this study. Four scenarios including a baseline scenario, low-carbon scenario, enhanced low-carbon scenario, and peak in 2023 scenario were set up to predict and analyze the carbon emission situation. The calculation and analysis results showed that it could achieve the carbon peak before 2030 only under the enhanced low-carbon scenario and peak in 2023 scenario. The peak year of the enhanced low-carbon scenario was around 2025 with a peak carbon emission of approximately 170 million tons, but the peak time may actually be delayed. Industry was the largest sector of carbon emissions, and the petrochemical industry was the largest portion of industry, the proportion of which was always maintained at approximately 30% under different scenarios. However, the proportion of power generation and steel industry decreased annually, whereas the proportion of the net imported power gradually increased. Industrial structure optimization and energy structure adjustment were the main driving factors of carbon peak in X City. Carbon emissions per unit of GDP will fall by approximately 41% in 2030 compared with that in 2020 under the enhanced low-carbon scenario.

Forecasting the energy demand and CO2 emissions of industrial sectors in China's Beijing-Tianjin-Hebei region under energy transition

As the world's greatest energy consumer, China's energy consumption and transition have become a focus of attention. The most significant location for regional integration in the north of China is the Beijing-Tianjin-Hebei region, where the industrial sector dominates its energy consumption. Forecasting the energy demand and structure of industrial sectors in China's Beijing-Tianjin-Hebei region may help to promote the energy transition and CO2 emission mitigation. This study conducts a model based on the year 2020 using the Long-Range Energy Alternatives Planning System (LEAP) software and sets two scenarios (baseline scenario and emission peak scenario) to forecast the future energy demand and CO2 emissions of industrial sectors in China's Beijing-Tianjin-Hebei region until the year 2035. Moreover, the industrial sectors are classified into traditional high-energy-consuming industries, emerging manufacturing industries, daily-related light industries, and other industries. The forecasting results show that (1) The industrial energy demand of the entire Beijing-Tianjin-Hebei region will grow from 234 Mtce in 2020 to 317 Mtce in 2035, and the corresponding energy structure will shift from coal-based to electricity-based; (2) at the provincial level, all three provinces will experience an increase in industrial energy demand between 2020 and 2035, with Hebei experiencing the fastest average annual growth rate of 2.18% and the largest share of over 80%, and Beijing experiencing the highest average annual electrification rate of 70%; (3) at the industrial sector level, the electricity and natural gas will gradually replace other energy sources as the main energy source for industry. The most representative industrial sub-sector in Beijing, Tianjin, and Hebei provinces are all traditional high-energy-consuming industries, which will account for more than 90% of the total energy demand in both Tianjin and Hebei by 2035.

Can China achieve its 2030 and 2060 CO2 commitments? Scenario analysis based on the integration of LEAP model with LMDI decomposition

China's ambitious targets of peaking its Carbon dioxide (CO₂) emissions on or before 2030 and achieving carbon neutrality by 2060 have been a topic of discussion in the international community. This study innovatively combines the logarithmic mean Divisia index (LMDI) decomposition method and the long-range energy alternatives planning (LEAP) model to quantitatively evaluate the CO₂ emissions from energy consumption in China from 2000 to 2060. Using the Shared Socioeconomic Pathways (SSPs) framework, the study designs five scenarios to explore the impact of different development pathways on energy consumption and related carbon emissions. The LEAP model scenarios are based on the result of LMDI decomposition, which identifies the key influencing factors on CO₂ emissions. The empirical findings of this study demonstrate that the energy intensity effect is the primary factor of the 14.7 % reduction in CO₂ emissions observed in China from 2000 to 2020. Conversely, the economic development level effect has been the driving factor behind the increase of 50.4 % in CO₂ emissions. Additionally, the urbanization effect has contributed 24.7 % to the overall change in CO₂ emissions during the same period. Furthermore, the study investigates potential future trajectories of CO₂ emissions in China up to 2060, based on various scenarios. The results suggest that, under the SSP1 scenarios. China's CO₂ emissions would peak in 2023 and achieve carbon neutrality by 2060. However, under the SSP4 scenarios, emissions are expected to peak in 2028, and China would need to eliminate approximately 2000 Mt of additional CO₂ emissions to reach carbon neutrality. In other scenarios, China is projected to be unable to meet the carbon peak and carbon neutrality goals. The conclusions drawn from this study offer valuable insights for potential policy adjustments to ensure that China could fulfill its commitment to peak carbon emissions by 2030 and achieve carbon neutrality by 2060.

Carbon Reduction of the Three-Year Air Pollution Control Plan under the LEAP Model Using a GREAT Tool in Panzhihua, China

In the context of global warming and climate change, various international communities have set different reduction targets for carbon emissions. In 2020, China proposed that CO₂ emissions will peak by 2030 and reached a critical period in which carbon reduction is a key strategic direction. Sichuan Academy of Environmental Sciences published the "Panzhihua Three-Year Iron Fist Gas Control Action Plan" in 2021. The measures implemented in the plan only address general considerations of conventional pollutants in the atmosphere. This study established the Panzhihua LEAP model based on the GREAT tool and built four simulation scenarios, including pollutant treatment upgrade (PTU), traffic improvement (TI), boiler remediation (BR), and baseline scenarios for industrial sources, mobile sources, and industrial boilers in policy implementation. It provided a supportive basis for the development of environmental protection measures in Sichuan province to increase the efficiency of carbon emission reduction. The quantitative analysis of the simulation results for the five years from 2020 to 2024 was conducted to discuss the intrinsic links between carbon emissions and energy consumption, market storage, and demand under different scenarios. It concluded that the BR and TI scenarios benefit carbon reduction, while the PTU scenario negatively impacts it. This study provided recommendations for analyzing the carbon footprint at a city-wide level, quantifying the relationship between the implementation of relevant environmental measures and carbon emissions, which are available for policy development that incorporates carbon reduction considerations and offers relevant support for future research.

[Forecasting of Emission Co-reduction of Greenhouse Gases and Pollutants for the Road Transport Sector in Lanzhou Based on the LEAP Model]

With the continuous increase in transportation activities, the transportation sector has become an important source of global greenhouse gases. In 2019, road vehicles accounted for nearly three-quarters of the CO₂ emissions of the entire transportation sector and will be the key to achieving carbon peaks in the transportation sector. At the same time, air pollutants emitted by road vehicles are also one of the threats to the environment and human health. Based on the long-range energy alternatives planning system (LEAP) model, we constructed the baseline (BAU) scenario, low-carbon (LC) scenario, and enhanced low-carbon (ELC) scenario for the development of the road transport sector in Lanzhou from 2015 to 2040 and simulated energy consumption and emission co-reduction of greenhouse gases and pollutants under policies and measures. The results showed that the energy consumption and CO₂ emissions of the LC scenario will peak in 2026, whereas those in the ELC scenario will peak in 2020. In these two scenarios, pollutant emissions such as NO_x, CO, HC, PM_2.5, and PM₁₀ began to decline sharply between 2015 and 2017, and the downward trend will slow down gradually around 2023. Based on the feasibility of measures and the cost of abatement, the LC scenario can be used as a road vehicle carbon peak scenario in Lanzhou. In this scenario, the reduction rates of energy consumption, CO₂, NO_x, CO, HC, PM_2.5, and PM₁₀ emissions will reach -24.17%, -26.57%, -55.38%, -65.91%, -72.87%, -76.66%, and -77.18% compared with those under the BAU scenario by 2040. At present, the road vehicles in Lanzhou City should focus on structural optimization measures such as clean-energy use of public transportation, electrification of small passenger cars, and phasing out old cars, as well as vigorously promoting low-carbon travel and improving energy efficiency accompanying the development of automotive technology. These efforts will effectively control CO₂ and pollutant emissions by road vehicles, and carbon peaks will be achieved as soon as possible. In addition, it is necessary to pay attention to the changes in vehicle types during the implementation of these measures, which most contribute CO₂ and various pollutants, in order to make the measures more targeted by changing the number or the market share of new energy of focused vehicle types.

Peaking Industrial CO2 Emission in a Typical Heavy Industrial Region: From Multi-Industry and Multi-Energy Type Perspectives

Peaking industrial carbon dioxide (CO2) emissions is critical for China to achieve its CO2 peaking target by 2030 since industrial sector is a major contributor to CO2 emissions. Heavy industrial regions consume plenty of fossil fuels and emit a large amount of CO2 emissions, which also have huge CO2 emissions reduction potential. It is significant to accurately forecast CO2 emission peak of industrial sector in heavy industrial regions from multi-industry and multi-energy type perspectives. This study incorporates 41 industries and 16 types of energy into the Long-Range Energy Alternatives Planning System (LEAP) model to predict the CO2 emission peak of the industrial sector in Jilin Province, a typical heavy industrial region. Four scenarios including business-as-usual scenario (BAU), energy-saving scenario (ESS), energy-saving and low-carbon scenario (ELS) and low-carbon scenario (LCS) are set for simulating the future CO2 emission trends during 2018−2050. The method of variable control is utilized to explore the degree and the direction of influencing factors of CO2 emission in four scenarios. The results indicate that the peak value of CO2 emission in the four scenarios are 165.65 million tons (Mt), 156.80 Mt, 128.16 Mt, and 114.17 Mt in 2040, 2040, 2030 and 2020, respectively. Taking ELS as an example, the larger energy-intensive industries such as ferrous metal smelting will peak CO2 emission in 2025, and low energy industries such as automobile manufacturing will continue to develop rapidly. The influence degree of the four factors is as follows: industrial added value (1.27) > industrial structure (1.19) > energy intensity of each industry (1.12) > energy consumption types of each industry (1.02). Among the four factors, industrial value added is a positive factor for CO2 emission, and the rest are inhibitory ones. The study provides a reference for developing industrial CO2 emission reduction policies from multi-industry and multi-energy type perspectives in heavy industrial regions of developing countries.

Synergic Benefits of Air Pollutant Reduction, CO2 Emission Abatement, and Water Saving under the Goal of Achieving Carbon Emission Peak: The Case of Tangshan City, China

The study aims to explore the synergic benefits of reducing air pollutants and CO₂ and water consumption under the carbon emission peak (CEP) policies at a city level. Air pollutants and CO₂ emissions are predicted by the Low Emissions Analysis Platform (LEAP) model, and the water consumption is forecast by the quota method. Two scenarios are constructed with the same policies, but to different degrees: the reference scenario achieves CEP in 2030, and the green and low carbon scenario achieves CEP in 2025. The prediction results show that air pollutant emissions, CO₂ emissions, and water consumption can be obviously decreased by intensifying the CEP policies. The synergic abatement effect was illustrated by the synergic reduction curve. Accelerating the adjustment of economic structure saves the most water, reduces the greatest amount of CO₂ emission, and also obtains the best synergic reduction capability between water consumption and CO₂ emission. Transforming the traditionally long process of steelmaking toward a short electric process reduces the majority of PM_2.5, SO₂, and VOC emissions, while consuming more water. The study provides a new viewpoint to assess and optimize the CEP action plan at city levels.

LEAP-Based Greenhouse Gases Emissions Peak and Low Carbon Pathways in China's Tourist Industry

China has grown into the world's largest tourist source market and its huge tourism activities and resulting greenhouse gas (GHG) emissions are particularly becoming a concern in the context of global climate warming. To depict the trajectory of carbon emissions, a long-range energy alternatives planning system (LEAP)-Tourist model, consisting of two scenarios and four sub-scenarios, was established for observing and predicting tourism greenhouse gas peaks in China from 2017 to 2040. The results indicate that GHG emissions will peak at 1048.01 million-ton CO₂ equivalent (Mt CO_2e) in 2033 under the integrated (INT) scenario. Compared with the business as usual (BAU) scenario, INT will save energy by 24.21% in 2040 and reduce energy intensity from 0.4979 tons of CO₂ equivalent/10⁴ yuan (TCO_2e/10⁴ yuan) to 0.3761 Tce/10⁴ yuan. Although the INT scenario has achieved promising effects of energy saving and carbon reduction, the peak year 2033 in the tourist industry is still later than China's expected peak year of 2030. This is due to the growth potential and moderate carbon control measures in the tourist industry. Thus, in order to keep the tourist industry in synchronization with China's peak goals, more stringent measures are needed, e.g., the promotion of clean fuel shuttle buses, the encouragement of low carbon tours, the cancelation of disposable toiletries and the recycling of garbage resources. The results of this simulation study will help set GHG emission peak targets in the tourist industry and formulate a low carbon roadmap to guide carbon reduction actions in the field of GHG emissions with greater certainty.

Thailand's long-term GHG emission reduction in 2050: the achievement of renewable energy and energy efficiency beyond the NDC

The sources of greenhouse gas (GHG) emissions in Thailand come from the energy sector, including power generation, transport, industries, buildings, and households. In 2016, the energy sector contributed 77 percent of total GHG emissions. Thailand's energy policies are the essential instrument to deal with GHG emission reduction under the United Nations Framework Convention on Climate Change (UNFCCC). The renewable energy (RE) plans aim at increasing the share of RE in final energy consumption while the energy efficiency (EE) plans aim at improving energy efficiency as well as reducing fossil-fuel consumption. GHG emission mitigation will result in several co-benefits such as increasing energy security and decreasing local air pollutants. Therefore, this study analyzes potentials of GHG emission reduction during 2015–2050 from utilization of renewable energy and increasing energy efficiency using the Long-range Energy Alternative Planning system (LEAP) model. Results include potentials of domestic RE and EE measures to achieve Thailand's nationally determined contribution (NDC). Moreover, it was found that to meet Thailand's first NDC of 20 percent GHG emission reduction target in 2030, targets in the RE plan and the EE plan must be achieved by at least 50 percent and 75 percent, respectively, or targets in the RE plan and the EE plan must be achieved by at least 75 percent and 50 percent. In addition, the extended NDC scenario in 2050 is analyzed in the long-term perspective of Thailand showing 30.4 percent reduction when compared to the BAU. The policy implication includes promotion of energy efficiency, acceleration of the deployment of renewable energy and advanced technologies such as CCS, completion of transmission network for renewable electricity, zoning of biomass sources, and public awareness in climate changes.

The millennium development goals and household energy requirements in Nigeria

Access to clean and affordable energy is critical for the realization of the United Nations’ Millennium Development Goals, or MDGs. In many developing countries, a large proportion of household energy requirements is met by use of non-commercial fuels such as wood, animal dung, crop residues, etc., and the associated health and environmental hazards of these are well documented. In this work, a scenario analysis of energy requirements in Nigeria’s households is carried out to compare estimates between 2005 and 2020 under a reference scenario, with estimates under the assumption that Nigeria will meet the millennium goals. Requirements for energy under the MDG scenario are measured by the impacts on energy use, of a reduction by half, in 2015, (a) the number of household without access to electricity for basic services, (b) the number of households without access to modern energy carriers for cooking, and (c) the number of families living in one-room households in Nigeria’s overcrowded urban slums. For these to be achieved, household electricity consumption would increase by about 41% over the study period, while the use of modern fuels would more than double. This migration to the use of modern fuels for cooking results in a reduction in the overall fuelwood consumption, from 5 GJ/capita in 2005, to 2.9 GJ/capita in 2015.