scholarly journals Driving Factor Analysis and Forecasting of CO2 Emissions from Power Output in China Using Scenario Analysis and CSCWOA-ELM Method

Energies ◽  
2018 ◽  
Vol 11 (10) ◽  
pp. 2709 ◽  
Author(s):  
Weijun Wang ◽  
Weisong Peng ◽  
Jiaming Xu ◽  
Ran Zhang ◽  
Yaxuan Zhao
Keyword(s):  
Co2 Emissions ◽  
Influencing Factors ◽  
Power Output ◽  
Policy Implications ◽  
Emissions Reduction ◽  
Development Scenarios ◽  
Learning Machine ◽  
Set Up ◽  
Grey Relational ◽  
Average Contribution

With power consumption increasing in China, the CO2 emissions from electricity pose a serious threat to the environment. Therefore, it is of great significance to explore the influencing factors of power CO2 emissions, which is conducive to sustainable economic development. Taking the characteristics of power generation, transmission and consumption into consideration, the grey relational analysis method (GRA) is adopted to select 11 influencing factors, which are further converted into 5 main factors by hierarchical clustering analysis (HCA). According to the possible variation tendency of each factor, 48 development scenarios are set up from 2018–2025, and then an extreme learning machine optimized by whale algorithm based on chaotic sine cosine operator (CSCWOA-ELM) is established to predict the power CO2 emissions respectively. The results show that gross domestic product (GDP) has the greatest impact on the CO2 emissions from power output, of which the average contribution rate is 1.28%. Similarly, power structure and living consumption level also have an enormous influence, with average contribution rates over 0.6%. Eventually, the analysis made in this study can provide valuable policy implications for power CO2 emissions reduction, which can be regarded as a reference for China’s 14th Five-Year development plan in the future.

Passenger travel CO2 emissions in US urbanized areas: Multi-sourced data, impacts of influencing factors, and policy implications

2014 ◽  
Vol 36 ◽  
pp. 231-241 ◽  
Author(s):  
Rabi G. Mishalani ◽  
Prem K. Goel ◽  
Andrew J. Landgraf ◽  
Ashley M. Westra ◽  
Dunke Zhou
Keyword(s):  
Co2 Emissions ◽  
Influencing Factors ◽  
Policy Implications ◽  
Passenger Travel ◽  
Urbanized Areas

scholarly journals The Impact of Direct and Indirect COVID-19 Related Demand Shocks on Sectoral CO2 Emissions: Evidence from Major Asia Pacific Countries

2021 ◽  
Vol 13 (16) ◽  
pp. 9312 ◽  
Author(s):  
Muhammad Jawad Sajid ◽  
Ernesto D. R. Santibanez Gonzalez
Keyword(s):  
Co2 Emissions ◽  
Negative Impact ◽  
Extraction Methods ◽  
Policy Implications ◽  
Asia Pacific ◽  
Emissions Reduction ◽  
Case Scenario ◽  
Worst Case ◽  
Demand Shocks ◽  
The Impact

COVID-19’s demand shocks have a significant impact on global CO2 emissions. However, few studies have estimated the impact of COVID-19’s direct and indirect demand shocks on sectoral CO2 emissions and linkages. This study’s goal is to estimate the impact of COVID-19’s direct and indirect demand shocks on the CO2 emissions of the Asia-Pacific countries of Bangladesh, China, India, Indonesia, and Pakistan (BCIIP). The study, based on the Asian Development Bank’s COVID-19 economic impact scenarios, estimated the impact of direct and indirect demand shocks on CO2 releases using input–output and hypothetical extraction methods. In the no COVID-19 scenario, China emitted the most CO2 (11 billion tons (Bt)), followed by India (2 Bt), Indonesia (0.5 Bt), Pakistan (0.2 Bt), and Bangladesh (0.08 Bt). For BCIIP nations, total demand shocks forced a 1–2% reduction in CO2 emissions under a worst-case scenario. Given BCIIP’s current economic recovery, a best or moderate scenario with a negative impact of less than 1% is more likely in coming years. Direct demand shocks, with a negative 85–63% share, caused most of the CO2 emissions decrease. The downstream indirect demand had only a 15–37% contribution to CO2 emissions reduction. Our study also discusses policy implications.

scholarly journals Scenario Analysis on Energy Consumption and CO2 Emissions Reduction Potential in Building Heating Sector at Community Level

2019 ◽  
Vol 11 (19) ◽  
pp. 5392 ◽  
Author(s):  
Chuan Tian ◽  
Guohui Feng ◽  
Shuai Li ◽  
Fuqiang Xu
Keyword(s):  
Energy Consumption ◽  
Co2 Emissions ◽  
Co2 Emission ◽  
Population Data ◽  
Driving Factors ◽  
Policy Implications ◽  
Energy Planning ◽  
Emissions Reduction ◽  
Energy Structure ◽  
Baseline Scenario

Energy consumption and carbon emissions of building heating are increasing rapidly. Taking Liaobin coastal economic zone as an example, two scenarios are built to analyze the potential of energy consumption and CO2 emissions reduction from the aspects of laws, regulations, policies and planning. The baseline scenario refers to the traditional way of energy planning and the community energy planning scenario seeks to apply community energy planning within the zone. Energy consumption and CO2 emission are forecast in two scenarios with the driving factors including GDP growth, changes in population size, energy structure adjustment, energy technology progress, and increase of energy efficiency. To improve accuracy of future GDP and population data prediction, an ARIMA (Autoregressive Integrated Moving Average model) (1,1,1) model is introduced into GDP prediction and a logistics model is introduced into population prediction. Results show that compared with the baseline scenario, energy consumption levels in the community energy planning scenario are reduced by 140% and CO2 emission levels are reduced by 45%; the short-term and long-term driving factors are analyzed. Policy implications are given for energy conservation and environmental protection.

scholarly journals Nudge Versus Boost: Agency Dynamics Under Libertarian Paternalism

2019 ◽  
Vol 130 (629) ◽  
pp. 1384-1415 ◽  
Author(s):  
Ralph Hertwig ◽  
Michael D Ryall
Keyword(s):  
Cognitive Biases ◽  
Policy Implications ◽  
Best Interests ◽  
Libertarian Paternalism ◽  
The Future ◽  
Set Up ◽  
The Individual

ABSTRACT Thaler and Sunstein (2008) advance the concept of ‘nudge’ policies—non-regulatory and non-fiscal mechanisms designed to enlist people's cognitive biases or motivational deficits so as to guide their behaviour in a desired direction. A core assumption of this approach is that policymakers make artful use of people's cognitive biases and motivational deficits in ways that serve the ultimate interests of the nudged individual. We analyse a model of dynamic policymaking in which the policymaker's preferences are not always aligned with those of the individual. One novelty of our set-up is that the policymaker has the option to implement a ‘boost’ policy, equipping the individual with the competence to overcome the nudge-enabling bias once and for all. Our main result identifies conditions under which the policymaker chooses not to boost in order to preserve the option of using the nudge (and its associated bias) in the future—even though boosting is in the immediate best interests of both the policymaker and the individual. We extend our analysis to situations in which the policymaker can be removed (e.g., through an election) and in which the policymaker is similarly prone to bias. We conclude with a discussion of some policy implications of these findings.

Measuring energy and environmental efficiency interactions towards CO2 emissions reduction without slowing economic growth in central and western Europe

2021 ◽  
Vol 279 ◽  
pp. 111704
Author(s):  
Jijian Zhang ◽  
Ataul Karim Patwary ◽  
Huaping Sun ◽  
Muhammad Raza ◽  
Farhad Taghizadeh-Hesary ◽  
...  
Keyword(s):  
Economic Growth ◽  
Co2 Emissions ◽  
Western Europe ◽  
Environmental Efficiency ◽  
Emissions Reduction

scholarly journals A Carbon Price Prediction Model Based on the Secondary Decomposition Algorithm and Influencing Factors

Energies ◽  
2021 ◽  
Vol 14 (5) ◽  
pp. 1328
Author(s):  
Jianguo Zhou ◽  
Shiguo Wang
Keyword(s):  
Prediction Model ◽  
Extreme Learning Machine ◽  
Influencing Factors ◽  
Search Algorithm ◽  
Carbon Price ◽  
Carbon Trading ◽  
Price Prediction ◽  
Model Based ◽  
Mode Decomposition ◽  
Learning Machine

Carbon emission reduction is now a global issue, and the prediction of carbon trading market prices is an important means of reducing emissions. This paper innovatively proposes a second decomposition carbon price prediction model based on the nuclear extreme learning machine optimized by the Sparrow search algorithm and considers the structural and nonstructural influencing factors in the model. Firstly, empirical mode decomposition (EMD) is used to decompose the carbon price data and variational mode decomposition (VMD) is used to decompose Intrinsic Mode Function 1 (IMF1), and the decomposition of carbon prices is used as part of the input of the prediction model. Then, a maximum correlation minimum redundancy algorithm (mRMR) is used to preprocess the structural and nonstructural factors as another part of the input of the prediction model. After the Sparrow search algorithm (SSA) optimizes the relevant parameters of Extreme Learning Machine with Kernel (KELM), the model is used for prediction. Finally, in the empirical study, this paper selects two typical carbon trading markets in China for analysis. In the Guangdong and Hubei markets, the EMD-VMD-SSA-KELM model is superior to other models. It shows that this model has good robustness and validity.

scholarly journals Grey Relational Grade Based Quantitative Analysis of the Factors Influencing the Load Characteristics of A Power Grid

2018 ◽  
Vol 53 ◽  
pp. 01012 ◽  
Author(s):  
Wei Pan ◽  
Caijia Lei ◽  
Wei Jia ◽  
Hui Gao ◽  
Binghua Fang
Keyword(s):  
Quantitative Analysis ◽  
Influencing Factors ◽  
Power Grid ◽  
Grey System Theory ◽  
Grey System ◽  
Factors Affecting ◽  
Grey Relational Grade ◽  
Load Characteristics ◽  
Grey Relational ◽  
Main Factor

Regarding analysis of load characteristics of a power grid, there are multiple factors that influence the variation of load characteristics. Among these factors, the influence of different ones on the change of load characteristic is somewhat different, thus the degree of influence of various factors needs to be quantified to distinguish the main and minor factors of load characteristics. Based on this, the grey relational analysis in the grey system theory is employed as the basis of mathematical model in this paper. Firstly, the main factors affecting the load characteristics of a power grid are analysed. Then, the principle of quantitative analysis of the influencing factors by using grey relational grade is introduced. Lastly, the load of Guangzhou power grid is selected as the research object, thereby the main factor of temperature affecting the load characteristics is quantitatively analysed, such that the correlation between temperature and load is established. In this paper, by investigating the influencing factors and the degree of influence of load characteristics, the law of load characteristics changes can be effectively revealed, which is of great significance for power system planning and dispatching operation.

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