The Scale, Structure, and Influencing Factors of Total Carbon Emissions from Households in 30 Provinces of China—Based on the Extended STIRPAT Model. A study on the pushback control strategy of departing aircraft from the perspective of reducing carbon emissions.
Modeling and Simulation of Carbon Emission-Related Issues
- Introduction
- Summary Information of 20 Papers in the Special Issue
- Review of the Special Issue 1. Carbon Emissions
- Concluding Remarks
Influential Factors and Scenarios of China's Power Industry Carbon Emission Forecast: Based on the Generalized Divizia Index Model and Monte Carlo Simulation. Allocation of carbon emissions quotas in China's electricity industry based on the principle of fairness. Energies.
The Scale, Structure and Influencing Factors of Total Carbon Emissions from Households in 30 Provinces
Literature Review
The research methods on the factors influencing the CO2 emissions of households are mainly of two types. In the fourth part, the results of the total CO2 emissions of household consumption and the results of the model construction are analyzed.
Research Methods and Data Explanation
In this formula, t(t L, 2015) is the sample observation period. It is the total carbon emission of the residents' consumption. PSIZE is the size of the population. CHI is for children aged 0-14. If this indicator is low, it will inhibit the households' total CO2 emissions.
Results and Analysis
Scale and structural change of China's total household consumption carbon emissions from 2006 to 2015. Contribution of five energy sources to China's household consumption energy carbon emissions.
Discussion
The industrial structure and carbon emissions of China's household consumption are moving in the opposite direction. To measure the products' carbon emissions from China's household consumption in consecutive years, this paper adopts the Consumer Lifestyle Approach (CLA), which measures the products' carbon emissions from terminal consumption.
Conclusions and Suggestions 1. Conclusions
Demographic, economic and technological factors had a significant impact on the consumption of carbon emissions by Chinese residents. Carbon emissions from domestic energy consumption in the household sector: A survey of measurement and driving factors. China Popul.
Comparing Urban and Rural Household CO 2
Emissions—Case from China’s Four Megacities
Beijing, Tianjin, Shanghai, and Chongqing
- Materials and Methods 1. Household CO 2 Emissions
- Results
- Discussion
- Conclusions
For example, Beijing's urban indirect HCEs were more than 13 times those of rural households in 2015. Panel Assessment of Urbanization, Energy Consumption, and CO2 Emissions: A Regional Analysis in China. Energy Policy.
Influencing Factors and Decoupling Elasticity of China’s Transportation Carbon Emissions
Estimation of Carbon Emissions in China’s Transportation Industry
According to China's current statistical standards and the "Industrial Classification for National Economic Activities" published by the National Bureau of Statistics, the transportation industry in this article includes transportation, warehousing and postal services. This article uses the official classification, and the definition of the transport sector in this article is also widely used in other studies, such as that of Zhao et al. The fossil fuel energy consumed by the transport sector mainly includes raw coal, coke, gasoline, diesel oil and natural gas.
Methods
In equation (14), %Δ(CO2/P) represents the percentage change in carbon emissions per inhabitant of the transport industry; %Δ(CO%ΔGDP2/P)t represents per capita decoupling elasticity between transport economic growth and carbon emissions; and %Δ(CO%ΔE2/P) represents per incorporates emission reduction elasticity between transport energy consumption and carbon emissions. Economic recession, carbon emissions decrease, but the decrease in carbon emissions is greater than the economic recession. The research methods and models for the influencing factors and the decoupling of carbon emissions from transport development are summarized in Figure 1.
Analysis of Results
As a result, this objectively optimizes the energy structure of the transport industry and promotes a decrease in carbon emissions. The added value of the transport per per capita has a more stable effect on reducing carbon emissions. 31] on decoupling the development of the transportation industry and carbon emissions in Guangdong Province and the whole country.
Discussion and Analysis
However, the decoupling of transport development from carbon emissions has shown poor decoupling in recent years. Currently, there are studies on the factors of carbon emissions in the transport industry, such as Tunisia and Morocco. Currently, there are few studies on the decoupling of transport development and carbon emissions in other countries.
Conclusions and Suggestions 1. Main Conclusions
Analysis on the driving factors of the growth of carbon emissions in China's transport sector. Jianghuai Tribe. An analysis of the decoupling factors and the impact of carbon emissions from the transport sector in the Beijing-Tianjin-Hebei area, China. Sustainability2017,9, 722.
Decoupling Greenhouse Gas Emissions from Crop Production: A Case Study in the Heilongjiang Land
Materials and Methods 1. Carbon Footprint Calculation
All the above emission factors for agricultural inputs or resources are presented in Table 1. DI = %ΔCF/%ΔY = (CFj/CFj−1−1)/(Yj/Yj−1−1), (9) where %ΔCFis the percentage change in GHG emissions from plant production and CFjandCFj−1 indicate the GHG emissions in a target year and base yearj−1; %Δ is the percent change in crop yield, and YjandYj−1denote the crop yield in a target year and base yearj−1, respectively. Data for quantifying GHG emissions from agricultural inputs or resources were collected from the National Agricultural Product Cost-Benefit Survey.
Results and Analysis
We see a better relationship between crop production and greenhouse gas emissions in the HLRA than in other parts of the world. Here we take the JSJ branch and the SH branch of the HLRA for comparative analysis (Figures 6 and 7). In contrast, rice plant area and rice yield in the SH branch each accounted for 2% of the HLRA total.
Environmental Impact and Carbon Footprint Assessment of Taiwanese Agricultural Products
A Case Study on Taiwanese Dongshan Tea
Materials and Methods 1. Study Scope and Goal
Although PAS 2050, TS-Q 0010 and the Standard for Product Lifecycle Accounting and Reporting and ISO14067 all consider specific emissions and removals considerations to be given for land-use change, renewable energy and carbon storage, and delayed emissions, these approaches are different and incomplete [46]. To quantify the greenhouse gas impact of a product, PAS 2050, TS-Q 0010, Standard for Product Lifecycle Accounting and Reporting and ISO14067 provide principles and requirements. This assessment method demonstrates a useful implementation of a combined midpoint and damage approach that includes all types of Life Cycle Inventory results from 13 midpoint categories to four damage categories.
Results and Discussion 1. Carbon Footprint Analysis Results
The figure shows that the largest categories of environmental impact are human health, climate change, resources at the raw material stage, and climate change at the use and production stage. This study found that 96% of the impact of resource categories comes from the raw material stage, with non-renewable energy being the main middle ground. This study found that 97% of the impact of human health categories originates from the raw material phase, with the largest middle point being respiratory inorganics, which contribute 76%.
Conclusions and Perspectives
A comprehensive life cycle assessment (LCA) of three apricot plantation systems located in the Metapontino area (southern Italy). Life cycle assessment of fossil energy consumption and greenhouse gas emissions in Chinese pear production.J. Life cycle assessment of organic and conventional apple supply chains in northern Italy.J.
A Study on the Strategy for Departure Aircraft
Pushback Control from the Perspective of Reducing Carbon Emissions
- Airport Surface Operation Data Analysis and Definition 1. Airport Surface Monitoring Data Analysis
- Runway Capacity Analysis
- Aircraft Departure Time Prediction 1. Factors Affecting Departure Taxiing Time
- Pushback Strategy for Departure on the Airport Surface 1. Implementation of Control Strategies
- Conclusions
Figure 3 shows the take-off speed for flights from departure runway 18 R as a function of the number of departure flights on the airport's surface. Therefore, the number of departing aircraft on the airport surface can be used as a predictor variable for the aircraft's departure taxi time. Consistency can thus be achieved between the aircraft's departure check-in time and the number of departing aircraft on the airport's surface.
Evaluation of the CO 2 Emissions Reduction Potential of Li-ion Batteries in Ship Power Systems
- Methodology
- Sensitivity Analysis
- Lithium Iron Phosphate and Lithium Titanate Batteries Comparison
- Evaluation of CO 2 Emissions Reduction per Part of the Mission
- Conclusions, Managerial Implications and Discussion
The energy capacity of the battery system is fixed at 1000 kWh, and the return efficiency is 92%. The results for three additional cases per part of the mission are shown in Figure 14. Port loading and standby have the largest reductions in CO2 emissions compared to other parts of the mission.
A Relational Analysis Model of the Causal Factors Influencing CO 2 in Thailand’s Industrial Sector under
VARIMAX-ECM Model
The Forecasting Model 1. Unit Root Test
However, to observe a deviation from the long-term co-integration of j(j r), denoted as vectorβXt−1, the mean of the above deviation must be zero. In addition, equation (15) explains the relationship between the deterministic region of the VARIMAX-ECM model and the long-term cointegrating vector, which can be classified into five situations. There are usually two popular patterns for forming primary and secondary assumptions about the number of long-run cointegrations.
Empirical Analysis
Table 3 illustrates the parameters of the VARIMAX-ECM Model at a statistically significant level of 1% and 5%. Based on the findings of the study, it has shown that the VARIMAX-ECM model used by the author is the most effective model. Therefore, the VARIMAX-ECM model is used to predict CO2 emissions in the next step.
Conclusions and Discussion
Energy consumption and CO2 emissions in China's cement industry: a perspective of LMDI degradation analysis. Energy policy. Analysis of energy-related greenhouse gas emissions in China's iron and steel industry. Energy policy. Energy consumption, economic growth and CO2 emissions in the countries of the Middle East and North Africa. Energy policy.
Influencing Factors and Scenario Forecasts of Carbon Emissions of the Chinese Power Industry: Based on a
Carlo Simulation
Methods
Ei×CVi×CCFi×COFi In Equation (1), represents carbon emissions by the power industry in 104 tons; indicates the type of energy use. This paper presents a factor analysis of carbon emissions by the power industry for 2000–2015 as a sampling interval. A summary of the research methods used for the decomposition and scenario prediction of carbon emissions for China's power industry is summarized in Figure 1.
Result Analysis
From 2010 to 2015, the carbon intensity of energy consumption played a catalytic role in reducing carbon emissions from the energy sector, reaching 100 million tons. Average annual change in carbon emissions from the energy sector for different scenarios from 2017 to 2030. Compared to the baseline scenario, carbon emissions will continue to decrease in the low-carbon scenario.
Discussion and Analysis
This paper includes the breakdown of the factors of carbon emissions in the energy industry and sets relevant scenarios for forecasting. There are other aspects of carbon emissions in the energy industry that could be studied further. In addition, draw lessons from countries and improve China's carbon emissions trading system, combined with China's reality.
Main Conclusions and Recommendations 1. Main Conclusions
Thus, in the current state of development, China's power industry's carbon emissions are likely to continue to increase. Study on the Driving Forces of the Growth of Carbon Emissions in China's Economic Development.J. An Empirical Analysis of the Influencing Factors of Carbon Emission Intensity in China's Power Industry.Elec.
Can China Achieve the 2020 and 2030 Carbon Intensity Targets through Energy
Structure Adjustment?
Materials and Methods
The model's adjustment value ˆY(0) for primary energy consumption in China from 1953 to 2016 is obtained based on the predictive value reduction formula. It is well known that the primary energy consumption is affected by many factors and that the system is complicated. Therefore, in order to achieve a higher prediction accuracy, we apply GRNN to perform the primary energy consumption forecast.
