scholarly journals Analysis of China’s Primary Energy Structure and Emissions Reduction Targets by 2030 Based on Multiobjective Programming

2017 ◽  
Vol 2017 ◽  
pp. 1-8 ◽  
Author(s):  
Feng Ren ◽  
Long Xia
Keyword(s):  
Carbon Emissions ◽  
Multiobjective Programming ◽  
Markov Chain Model ◽  
Primary Energy ◽  
Emissions Reduction ◽  
Energy Structure ◽  
Chain Model ◽  
Policy Recommendations ◽  
Ecological Protection ◽  
Carbon Emissions Reduction

China’s energy issues and carbon emissions have become important global concerns. The purpose of this paper is to analyze the fulfillment of China’s commitment to carbon emissions reduction by 2030. We establish the Markov chain model to analyze the transition of primary energy structure and carbon emissions in China by 2030 without artificial intervention and build three multiobjective optimization models to analyze China’s energy structure and emissions reduction targets by 2030 under three scenarios (scenario of energy structure optimization, scenario of energy intensity optimization, and scenario of energy structure-intensity optimization). The findings show that the proportions of coal, oil, natural gas, and nonfossil energy will reach 17.89%, 11.52%, 49.43%, and 21.16%, respectively; the total decreases in CO2 intensity reach 43.11%, 61.78%, and 60.64%, respectively; the CO2 emissions under these three scenarios are 25.092, 16.859, and 17.359 billion tons. In other words, China’s emissions reduction targets cannot be easily achieved. In order to keep pace with China’s overall mitigation agenda, we put forward the policy recommendations. Through these analyses and discussions, we hope to make contributions to policy stimulation in energy, carbon emissions, and ecological protection.

scholarly journals Study on Transition of Primary Energy Structure and Carbon Emission Reduction Targets in China Based on Markov Chain Model and GM (1, 1)

2016 ◽  
Vol 2016 ◽  
pp. 1-8 ◽  
Author(s):  
Feng Ren ◽  
Lihong Gu
Keyword(s):  
Markov Chain ◽  
Carbon Emissions ◽  
Markov Chain Model ◽  
Primary Energy ◽  
Emissions Reduction ◽  
Energy Structure ◽  
Chain Model ◽  
Total Energy Consumption ◽  
Carbon Emissions Reduction ◽  
Emissions Intensity

The improvement of the primary energy structure has been considered as one of the important measures to achieve the carbon emissions reduction targets in China. This current paper constructed a Markov chain model, which was used to forecast the transition of primary energy structure. GM (1, 1) model and a linear regression model were used to predict the total energy consumption in 2020 and 2030. Then, the CO2emissions intensity was calculated, and the realization of carbon emissions reduction targets in China was analyzed. The findings indicated that (1) China’s nonfossil energy share in primary energy cannot be achieved naturally. (2) Part of the carbon emissions intensity in China’s commitments was not binding actually. (3) The realization of the carbon emissions peak and the reduction target of carbon emissions intensity in 2030 would need the policy intervention. In the last part of this paper, policy recommendations on carbon emissions reduction in China were provided.

scholarly journals Climate-Related Development Finance, Energy Structure Transformation and Carbon Emissions Reduction: An Analysis From the Perspective of Developing Countries

2022 ◽  
Vol 9 ◽  
Author(s):  
Nan Li ◽  
Beibei Shi ◽  
Lei Wu ◽  
Rong Kang ◽  
Qiang Gao
Keyword(s):  
Developing Countries ◽  
Economic Development ◽  
Carbon Emissions ◽  
Climate Adaptation ◽  
Developed Countries ◽  
Emissions Reduction ◽  
Energy Structure ◽  
Development Finance ◽  
Related Development ◽  
Carbon Emissions Reduction

With the frequent occurrence of extreme weather in cities, economic, ecological and social activities have been greatly impacted. The adverse effects of global extreme climate and effective governance have attracted more and more attention of scholars. Considering the differences between developed and developing countries in climate response capacity, a key issue is how to encourage developed countries to provide adequate assistance to developing countries and enhance their enthusiasm to participate in addressing climate change challenges. Given this background, we evaluated the carbon emission reduction effects of developing countries before and after a “quasi-natural experiment” which involved obtaining the assistance of climate-related funding from developed countries. Specifically, we analyzed the assistance behavior for recipient countries and found that climate assistance can effectively reduce the carbon emissions level of recipient countries, and this result has a better impact on non-island types and countries with higher levels of economic development. Furthermore, the achievement of this carbon emissions reduction target stems from the fact that climate assistance has promoted the optimization of the energy structure of recipient countries and promoted the substitution of renewable energy for coal consumption. In addition, climate-related development finance plays a significant role in promoting the scientific and technological level of recipient countries, especially the development impact of the adaptive climate-related development finance. Therefore, this paper suggests that the direction of climate assistance should focus more on island countries and countries with low economic development level, and pay more attention to the “coal withdrawal” of recipient countries and climate adaptation field.

scholarly journals Information Disclosure, Coal Withdrawal and Carbon Emissions Reductions: A Policy Test Based on China’s Environmental Information Disclosure

2021 ◽  
Vol 13 (17) ◽  
pp. 9758
Author(s):  
Nan Li ◽  
Beibei Shi ◽  
Rong Kang
Keyword(s):  
Carbon Emissions ◽  
Information Disclosure ◽  
Public Awareness ◽  
Clean Energy ◽  
Environmental Information ◽  
Emissions Reduction ◽  
Energy Structure ◽  
Emissions Reductions ◽  
Carbon Emissions Reduction ◽  
Environmental Information Disclosure

How to better explore a diversity of emissions reduction paths has become the key to China achieving carbon peak and carbon neutralization goals as well as transforming the existing energy structure as soon as possible. Based on this, from the perspective of information flow, this study used the differences-in-differences method (DID) to identify the “net effect” of the carbon emissions reduction caused by China’s environmental information disclosure. The results showed the following: first, environmental information disclosure could effectively promote regional carbon emissions reductions and had a better effect on the central and western regions and low carbon emissions density regions. Second, the achievement of carbon emissions reduction targets was mainly attributed to the positive impact of information disclosure in the process of “coal withdrawal.” Finally, this study also found that environmental information disclosure helped to promote the positive effect of clean energy development on “coal withdrawal,” and the promotion of public awareness regarding environmental supervision helped to strengthen the external impact of environmental information disclosure on regional carbon emissions reduction.

scholarly journals An Examination of Green Credit Promoting Carbon Dioxide Emissions Reduction: A Provincial Panel Analysis of China

2021 ◽  
Vol 13 (13) ◽  
pp. 7148
Author(s):  
Wenjie Zhang ◽  
Mingyong Hong ◽  
Juan Li ◽  
Fuhong Li
Keyword(s):  
Carbon Dioxide ◽  
Carbon Emissions ◽  
Carbon Dioxide Emissions ◽  
Industrial Structure ◽  
Emissions Reduction ◽  
Green Finance ◽  
Carbon Emissions Reduction ◽  
Negative Effect ◽  
Emissions Intensity ◽  
The Impact

The implementation of green finance is a powerful measure to promote global carbon emissions reduction that has been highly valued by academic circles in recent years. However, the role of green credit in carbon emissions reduction in China is still lacking testing. Using a set of panel data including 30 provinces and cities, this study focused on the impact of green credit on carbon dioxide emissions in China from 2006 to 2016. The empirical results indicated that green credit has a significantly negative effect on carbon dioxide emissions intensity. Furthermore, after the mechanism examination, we found that the promotion impacts of green credit on industrial structure upgrading and technological innovation are two effective channels to help reduce carbon dioxide emissions. Heterogeneity analysis found that there are regional differences in the effect of green credit. In the western and northeastern regions, the effect of green credit is invalid. Quantile regression results implied that the greater the carbon emissions intensity, the better the effect of green credit. Finally, a further discussion revealed there exists a nonlinear correlation between green credit and carbon dioxide emissions intensity. These findings suggest that the core measures to promote carbon emission reduction in China are to continue to expand the scale of green credit, increase the technology R&D investment of enterprises, and to vigorously develop the tertiary industry.

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.

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