scholarly journals Carbon Emissions Decomposition and Environmental Mitigation Policy Recommendations for Sustainable Development in Shandong Province

2014 ◽  
Vol 6 (11) ◽  
pp. 8164-8179 ◽  
Author(s):  
Changjian Wang ◽  
Fei Wang ◽  
Hongou Zhang ◽  
Yuyao Ye ◽  
Qitao Wu ◽  
...  
Keyword(s):  
Sustainable Development ◽  
Carbon Emissions ◽  
Shandong Province ◽  
Policy Recommendations ◽  
Mitigation Policy ◽  
Environmental Mitigation

Decomposition of energy-related carbon emissions in Xinjiang and relative mitigation policy recommendations

2014 ◽  
Vol 9 (1) ◽  
pp. 65-76 ◽  
Author(s):  
Changjian Wang ◽  
Xiaolei Zhang ◽  
Fei Wang ◽  
Jun Lei ◽  
Li Zhang
Keyword(s):  
Carbon Emissions ◽  
Policy Recommendations ◽  
Mitigation Policy

The Implementation Approach of LCCF Checklist in Development Control: Challenges for policymakers

2019 ◽  
Vol 4 (12) ◽  
Author(s):  
T B A
Keyword(s):  
Climate Change ◽  
Sustainable Development ◽  
Carbon Emissions ◽  
Local Authority ◽  
Survey Methods ◽  
Local Level ◽  
Control Process ◽  
Low Carbon ◽  
Local Authorities ◽  
Development Control

Global warming, climate change is now affecting the world. The effort of the leaders to achieving the sustainable development is from New Urban Agenda (NUA), Sustainable Development Goals (SDG’s) and local level is local authorities.  SDG’s goal number 13 takes urgent action to combat climate change and its impact also SDG’s number 11 to sustainable cities and communities. The gap of this paper  Different cities face different challenges and issues. Local authorities will play a significant role in undertaking policy initiatives to combat carbon emissions of the city. Low Carbon Cities (LCC) is to reduce carbon emissions in all human activities in cities.  The objective of this paper is by applying the LCCF Checklist in planning permission for sustainable development. The methodology of this research is a mixed-method, namely quantitative and qualitative approach. The survey methods are by interview, questionnaire, and observation. Town planners are the subject matter expert in managing the planning permission submission for the development control of their areas. Descriptive statistical analysis will be used to show the willingness of the stakeholders, namely the developers and planning consultants in implementing of the LCCF. The contribution of this research will gauge readiness at the local authorities level. The findings of the LCCF checklist are identified as important in planning permission into the development control process. Surprisingly, that challenges and issues exist in multifaceted policy implementation the LCCF Checklist in a local authority. Finally based on Subang Jaya Municipal Councils, the existing approach in the application of the LCCF Checklist in the development control process will be useful for development control in a local authority towards sustainable development.  

scholarly journals Probing the carbon emissions in 30 regions of China based on symbolic regression and Tapio decoupling

2021 ◽  
Author(s):  
Haiying Liu ◽  
zhiqun zhang
Keyword(s):  
Influencing Factors ◽  
Carbon Emissions ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Symbolic Regression ◽  
Car Ownership ◽  
Emissions Reduction ◽  
Policy Recommendations ◽  
The World ◽  
Urbanization Rate

Abstract Against the background of energy shortages and severe air pollution, countries around the world are aware of the importance of energy conservation and emissions reduction; China is actively achieving emissions reduction targets. In this study, we use a symbolic regression to classify China's regions according to the degree of influencing factors, and calculate and analyze the inherent decoupling relationship between carbon emissions and economic growth in each region. Based on our results, we divided the 30 regions of the country into six categories according to the main influencing factors: GDP (13 regions), energy intensity (EI; 7 regions), industrial structure (IS; 3 regions), urbanization rate (UR; 3 regions), car ownership (CO; 2 regions), and household consumption level (HCL; 2 regions). Then, according to the order of the average carbon emissions in each region from high to low, these regions were further categorized as type-EI, type-UR, type-GDP, type-IS, type-CO, or type-HCL regions. The decoupling index of each region showed a downward trend; EI and GDP regions were the most notable contributors to emissions, based on which we provide policy recommendations.

scholarly journals Sustainable development in marine economy: Assessing carrying capacity of Shandong province in China

2022 ◽  
Vol 216 ◽  
pp. 105981
Author(s):  
Jing Sun ◽  
Jichao Miao ◽  
Hairong Mu ◽  
Jinhui Xu ◽  
Ningning Zhai
Keyword(s):  
Sustainable Development ◽  
Carrying Capacity ◽  
Shandong Province ◽  
Marine Economy

scholarly journals Constructing an Environmental Friendly Low-Carbon-Emission Intelligent Transportation System Based on Big Data and Machine Learning Methods

2020 ◽  
Vol 12 (19) ◽  
pp. 8118
Author(s):  
Tu Peng ◽  
Xu Yang ◽  
Zi Xu ◽  
Yu Liang
Keyword(s):  
Sustainable Development ◽  
Environmental Pollution ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Intelligent Transportation ◽  
Exhaust Emissions ◽  
Low Carbon ◽  
Vehicle Exhaust ◽  
The Sustainable Development ◽  
Low Carbon Emission

The sustainable development of mankind is a matter of concern to the whole world. Environmental pollution and haze diffusion have greatly affected the sustainable development of mankind. According to previous research, vehicle exhaust emissions are an important source of environmental pollution and haze diffusion. The sharp increase in the number of cars has also made the supply of energy increasingly tight. In this paper, we have explored the use of intelligent navigation technology based on data analysis to reduce the overall carbon emissions of vehicles on road networks. We have implemented a traffic flow prediction method using a genetic algorithm and particle-swarm-optimization-enhanced support vector regression, constructed a model for predicting vehicle exhaust emissions based on predicted road conditions and vehicle fuel consumption, and built our low-carbon-emission-oriented navigation algorithm based on a spatially optimized dynamic path planning algorithm. The results show that our method could help to significantly reduce the overall carbon emissions of vehicles on the road network, which means that our method could contribute to the construction of low-carbon-emission intelligent transportation systems and smart cities.

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