Study on the Passenger Transportation Energy Demand and Carbon Emission of Jilin Province Based on LEAP Model

2012 ◽  
Vol 518-523 ◽  
pp. 2243-2246 ◽  
Author(s):  
Zhuo Ma ◽  
Yong Xuan Wang ◽  
Hai Yan Duan ◽  
Xian En Wang ◽  
De Ming Dong
Keyword(s):  
Energy Demand ◽  
Carbon Emission ◽  
Jilin Province ◽  
Baseline Scenario ◽  
Low Carbon ◽  
Transportation Industry ◽  
Transportation Energy ◽  
Passenger Transportation ◽  
Term Energy

With the continuous development of the economic economy, the demands for automobiles in Jilin province increase constantly. The carbon emission control of transportation department will become one of the key fields for greenhouse gas control in Jilin province. This paper employs the LEAP Model, through setting Baseline scenario and Low-carbon scenario, to imitate the long-term energy demand and carbon emission of urban passenger transport in Jilin province. Then after the comparative analysis, this paper investigates the major impact elements and feasible paths for Jilin’s transportation industry carbon emission.

scholarly journals Energy Demand and Carbon Emission Peak Forecasting of Beijing Based on Leap Energy Simulation Method

2020 ◽  
Keyword(s):  
Sustainable Development ◽  
Energy Consumption ◽  
Carbon Emissions ◽  
Energy Demand ◽  
Carbon Emission ◽  
Simulation Method ◽  
Negative Influence ◽  
Low Carbon ◽  
Peak Value

<p>The long-term forecasting of the energy demand is an important issue of an area’s sustainable development, especially for mega cities such as Beijing. Beijing is changing its energy supply strategy to depend on energy imports from other provinces due to the city’s long-term low carbon sustainable development plan. Beijing has promised that it will reach the peak value of energy consumption by 2050 and the peak value of the carbon emissions by 2030. To understand whether this can be achieved, this study built an energy demand simulation model using the LEAP with different development scenarios. The results show that, the peak value of Beijing’s energy demand is between 108.25 and 131.74 Mtce during the period of 2044 to 2048, while the peak value of carbon emissions is between 134 and 139.38 million tons in 2025. We also find that adjusting the industry structure and improving the tertiary industry’s energy usage efficiency can be efficient ways to reduce energy consumption. These approaches not only reduce the negative influence of the economic development, but also achieve the energy saving and carbon emission reducing requirements. This study provides an interpretation of the implications for the future energy and climate policies of Beijing.</p>

scholarly journals Smart carbon monitoring platform under IoT-Cloud architecture for small cities in B5G

2021 ◽  
Author(s):  
He Zhang ◽  
Jianxun Zhang ◽  
Rui Wang ◽  
Yazhe Huang ◽  
Mengxiao Zhang ◽  
...  
Keyword(s):  
Real Time ◽  
Carbon Emission ◽  
Emission Measurement ◽  
Low Carbon ◽  
Time Data ◽  
Small Cities ◽  
Real Time Data ◽  
Monitoring Platform ◽  
Term Data

AbstractWith the rapid development of the Internet of Things (IoT) in the 5G age, the construction of smart cities around the world consequents on the exploration of carbon reduction path based on IoT technology is an important direction for global low carbon city research. Carbon dioxide emissions in small cities are usually higher than that in large and medium cities. However, due to the huge difference in data environment between small cities and Medium-large sized cities, the weak hardware foundation of the IoT, and the high input cost, the construction of a small city smart carbon monitoring platform has not yet been carried out. This paper proposes a real-time estimate model of carbon emissions at the block and street scale and designs a smart carbon monitoring platform that combines traditional carbon control methods with IoT technology. It can exist long-term data by using real-time data acquired with the sensing device. Therefore, the dynamic monitoring and management of low-carbon development in small cities can be achieved. The contributions are summarized as follows: (1) Intelligent thermoelectric systems, industrial energy monitoring systems, and intelligent transportation systems are three core systems of the monitoring platform. Carbon emission measurement methods based on sample monitoring, long-term data, and real-time data have been established, they can solve the problem of the high cost of IoT equipment in small cities. (2) Combined with long-term data, the real-time correction technology, they can dispose of the matter of differences in carbon emission measurement under diverse scales.

scholarly journals Abatement cost for selectivity negative emissions technology in power plant Indonesia with aim/end-use model

2021 ◽  
Vol 894 (1) ◽  
pp. 012011
Author(s):  
Z D Nurfajrin ◽  
B Satiyawira
Keyword(s):  
Emission Reduction ◽  
Energy Demand ◽  
Reduction Potential ◽  
Abatement Cost ◽  
Energy Sector ◽  
Cost Curve ◽  
Baseline Scenario ◽  
Low Carbon ◽  
Ghg Emission ◽  
End Use

Abstract The Indonesian government has followed up the Paris Agreement with Law No. 16 of 2016 by setting an ambitious emission reduction target of 29% by 2030, and this figure could even increase to 41% if supported by international assistance. In line with this, mitigation efforts are carried out in the energy sector. Especially in the energy sector, it can have a significant impact when compared to other sectors due to an increase in energy demand, rapid economic growth, and an increase in living standards that will push the rate of emission growth in the energy sector up to 6. 7% per year. The bottom-up AIM/end-use energy model can select the technologies in the energy sector that are optimal in reducing emissions and costs as a long-term strategy in developing national low-carbon technology. This model can use the Marginal Abatement Cost (MAC) approach to evaluate the potential for GHG emission reductions by adding a certain amount of costs for each selected technology in the target year compared to the reference technology in the baseline scenario. In this study, three scenarios were used as mitigation actions, namely CM1, CM2, CM3. The Abatement Cost Curve tools with an assumed optimum tax value of 100 USD/ton CO2eq, in the highest GHG emission reduction potential, are in the CM3 scenario, which has the most significant reduction potential, and the mitigation costs are not much different from other scenarios. For example, PLTU – supercritical, which can reduce a significant GHG of 37.39 Mtoe CO2eq with an emission reduction cost of -23.66 $/Mtoe CO2eq.

scholarly journals A hybrid stochastic model based Bayesian approach for long term energy demand managements

2020 ◽  
Vol 28 ◽  
pp. 100462
Author(s):  
Somayeh Ahmadi ◽  
Amir hossien Fakehi ◽  
Ali vakili ◽  
Morteza Haddadi ◽  
Seyed Hossein Iranmanesh
Keyword(s):  
Stochastic Model ◽  
Bayesian Approach ◽  
Energy Demand ◽  
Model Based ◽  
Term Energy

scholarly journals China’s pathway to a low carbon economy

2019 ◽  
Vol 14 (1) ◽  
Author(s):  
Wenjuan Yang ◽  
Rongqin Zhao ◽  
Xiaowei Chuai ◽  
Liangang Xiao ◽  
Lianhai Cao ◽  
...  
Keyword(s):  
Climate Change ◽  
Local Governments ◽  
Carbon Emission ◽  
Carbon Sink ◽  
Policy Recommendations ◽  
Low Carbon ◽  
Low Carbon Economy ◽  
Trading Market ◽  
Carbon Economy

AbstractClimate change has emerged as one of the most important environmental issues worldwide. As the world’s biggest developing country, China is participating in combating climate change by promoting a low carbon economy within the context of global warming. This paper summarizes the pathways of China’s low carbon economy including the aspects of energy, industry, low carbon cities, circular economy and low carbon technology, afforestation and carbon sink, the carbon emission trading market and carbon emission reduction targets. There are many achievements in the implementation of low carbon policies. For example, carbon emission intensity has been reduced drastically along with the optimizing of energy and industry structure and a nationwide carbon trading market for electricity industry has been established. However, some problems remain, such as the weakness of public participation, the ineffectiveness of unified policies for certain regions and the absence of long-term planning for low carbon cities development. Therefore, we propose some policy recommendations for the future low carbon economy development in China. Firstly, comprehensive and long-term planning should be involved in all the low carbon economy pathways. Secondly, to coordinate the relationship between central and local governments and narrow the gap between poor and rich regions, different strategies of carbon emission performance assessment should be applied for different regions. Thirdly, enterprises should cooperate with scientific research institutions to explored low carbon technologies. Finally, relevant institutions should be regulated to realize comprehensive low carbon transition through reasonable and feasible low carbon pathways in China. These policy recommendations will provide new perspectives for China’s future low carbon economy development and guide practices for combating climate change.

scholarly journals Consumers' Response to State Energy Efficient Appliance Rebate Programs

2017 ◽  
Vol 9 (4) ◽  
pp. 227-255 ◽  
Author(s):  
Sébastien Houde ◽  
Joseph E. Aldy
Keyword(s):  
Energy Efficiency ◽  
Energy Efficient ◽  
Energy Demand ◽  
State Energy ◽  
Government Expenditure ◽  
Order Of Magnitude ◽  
Term Energy ◽  
The Impact

Through an evaluation of the 2009 Recovery Act's State Energy Efficient Appliance Rebate Program, this paper examines consumers' response to energy efficiency rebates. The analysis shows that 70 percent of consumers claiming a rebate were inframarginal and an additional 15 percent–20 percent of consumers simply delayed their purchases by a few weeks. Consumers responded to rebates by upgrading to higher quality, but less energy-efficient models. Overall the impact of the program on long-term energy demand is likely to be small. Measures of government expenditure per unit of energy saved are an order of magnitude higher than estimates for other energy efficiency programs. (JEL D12, H31, H71, Q48)

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