Allocation of carbon emission allowances in China's electricity industry based on the principle of fairness. Cap and trade is a method of regulating and eventually reducing the amount of carbon emissions.
Concluding Remarks
The scale, structure and influencing factors of total household carbon emissions in 30 provinces of China - based on the comprehensive STIRPAT model. Energies. Quota Allocation for Carbon Emissions in China's Electricity Industry Based on the Equity Principle. Energies.
The Scale, Structure and Influencing Factors of Total Carbon Emissions from Households in 30 Provinces
Introduction
The study of household carbon emissions and their influencing factors is of great practical importance. The combination of carbon emissions of energy and products in household consumption is called total household carbon emissions.
Literature Review
The research methods of the factors influencing household carbon emissions mainly include two types. In the fourth part, the results of the total CO2 emissions from household consumption and the results of the model building are analyzed.
Research Methods and Data Explanation
In this formula, t(t L, 2015) is the sample observation period. C is the total carbon emission from residents' consumption. PSIZE is the size of the population. CHI is the 0–14 year old children. If this indicator is low, it inhibits total carbon emissions from household consumption.
Results and Analysis
The extent and structural change of total carbon emissions of China's household consumption from 2006 to 2015. Contribution of five energy sources to the energy emission of carbon from China's household consumption.
Discussion
The industrial structure and carbon emissions from Chinese household consumption are moving in the opposite direction. To measure the carbon emissions of Chinese household consumption products in successive years, this document has adopted the Consumer Lifestyle Approach (CLA), which measures the carbon emissions of final consumer products.
Conclusions and Suggestions 1. Conclusions
Demographic, economic and technological factors have had a significant impact on the consumption of carbon emissions by Chinese residents. Carbon emissions from domestic energy use in the household sector: a study of measurement and driving factors. China's 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, the indirect HCEs of urban Beijing were more than 13 times larger than those of rural households in 2015. A panel estimate for 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 paper includes transportation, storage, and postal services. This paper uses the official classification, and the definition of the transportation industry in this paper is also widely used in other studies, such as Zhao et al. The fossil fuel energy consumed by the transportation industry 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 capita in the transport industry; %Δ(CO%ΔBBP2/P)t represents per capita decoupling elasticity between transport economic growth and carbon emissions; and%Δ(CO%ΔE2/P) represents the elasticity of reduction of emissions per capita 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 of 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 reduction in carbon emissions. The per capita value added of transport has a more sustainable effect on reducing carbon emissions. 31] on the decoupling of transportation industry development and carbon emissions in Guangdong Province and across the country.
Discussion and Analysis
However, the decoupling of transport development from carbon emissions has shown a weak decoupling in the past few years. At present, there are studies on the factors of carbon emissions in the transport industry, such as Tunisia and Morocco. At present, there are few studies on the decoupling of transport development and carbon emissions in other countries.
Conclusions and Suggestions 1. Main Conclusions
An analysis of the drivers of carbon emissions growth in China's transportation sector. Jianghuai Trib. Analysis of factors sequestering and influencing carbon emissions from the transportation sector in the Beijing-Tianjin-Hebei area, China. Sustainability 2017,9, 722. Analysis of the separation of elasticity and driving factors between the relationship between carbon emissions in transport and economic growth.J.
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 shown in Table 1. DI = %ΔCF/%ΔY = (CFj/CFj−1−1)/(Yj/Yj−1−1), (9) where%ΔCFis the percentage change in GHG emissions from crop production and CFjandCFj−1 denote 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 observe a better relationship between crop production and GHG emissions in the HLRA than in other regions of the world. Here, we take the JSJ branch and the SH branch of HLRA for comparative analysis (Figures 6 and 7). In contrast, rice planting area and rice yield in the SH branch each account for 2% of the total HLRA.
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 Product Life Cycle Accounting and Reporting Standard and ISO14067 all consider that specific emissions and removals treatments are given to land use change, renewable energy sources and carbon storage, as well as delayed emissions, these approaches are different and incomplete [4]6. For quantifying the GHG impact of a product, PAS 2050, TS-Q 0010, Product Lifecycle Accounting and Reporting Standard and ISO14067 provide principles and requirements. This assessment method demonstrates a viable implementation of a combined midpoint and damage approach, integrating all types of life cycle inventory results from 13 midpoint categories into four damage categories.
Results and Discussion 1. Carbon Footprint Analysis Results
The figure shows that the largest environmental impact categories are human health, climate change, resources at the raw material stage, and climate change at the consumer use and production stages. This study found that 96% of the impact of the resource categories comes from the feedstock phase, with non-renewable energy being the highlight. This study found that 97% of the impact of human health categories comes from the raw material phase, and the largest middle point is respiratory inorganics, which contributes 76%.
Conclusions and Perspectives
Comprehensive Life Cycle Assessment (LCA) of three apricot orchard systems 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 set at 1000 kWh and the round-trip efficiency at 92%. The results for the three additional cases per part of the mission is shown in Figure 14. Loading in port and standby have the highest reduction in CO2 emissions compared to other parts of the mission.
![Figure 1. Global CO 2 emissions ranking according to the Emission Database for Global Atmospheric Research (EDGAR) report [6].](https://thumb-ap.123doks.com/thumbv2/1libvncom/9201186.0/130.723.102.620.519.789/figure-global-emissions-according-emission-database-atmospheric-research.webp)
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. Usually there are two popular patterns for forming primary and secondary assumptions regarding the number of long-term cointegration.
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 showed that the VARIMAX-ECM model used by the author is the most efficient one. 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 from LMDI decomposition analysis.Energy Policy. An analysis of energy-related greenhouse gas emissions in the Chinese iron and steel industry.Energy Policy. Energy consumption, economic growth and CO2 emissions in the Middle East and North African countries. 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 the carbon emissions from the electricity industry in 104 tons; shows the type of energy usage. This paper presents an analysis of energy industry carbon emission factors for 2000-2015 as a sample interval. An overview of the research methods used to decompose and forecast the carbon emission scenario for China's electricity industry is summarized in Figure 1.
Result Analysis
From 2010 to 2015, energy consumption carbon intensity played a catalytic role in reducing carbon emissions by the power industry and reached 100 million tons. Average annual rate of change in carbon emissions by the power industry for different scenarios from 2017 to 2030. Compared to the baseline scenario, carbon emissions will continue to decline in the low-carbon scenario.
Discussion and Analysis
This paper involves factoring carbon emissions in the electricity industry and sets up relevant scenarios for prediction. There are other aspects of carbon emissions in the electricity industry that could be explored further. In addition, draw experience from countries and improve China's CO2 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 number of neurons in the input layer is equivalent to the dimensions of the input vector in the training sample. Studies have shown that primary energy consumption in the industrial sector accounts for more than 70% of the total primary energy consumption in China [45]. Let it be the error of the forecast of primary energy consumption in time from the combined forecasting method, and according to l1+l2=1, equation (25) is as follows:
