scholarly journals The Energy Conservation and Emission Reduction Effects of Economic Agglomeration: A Spatial Perspective Based on China's Province-level Data

2021 ◽  
Author(s):  
Tianyu Luo ◽  
Hongmin Chen
Keyword(s):  
Energy Conservation ◽  
Emission Intensity ◽  
Emission Reduction ◽  
Carbon Emission ◽  
Energy Intensity ◽  
Collaborative Governance ◽  
Mediating Effect ◽  
Spatial Data Analysis ◽  
Carbon Emission Intensity ◽  
Economic Agglomeration

Abstract Based on the data of 30 provinces in China from 1995 to 2017, this paper combines exploratory spatial data analysis method, dynamic spatial Durbin model, and intermediary effect model to explore the spatial influence mechanism between economic agglomeration, energy intensity, and carbon emission intensity. The research results provide a basis for China's early realization of energy conservation and emission reduction goals, economical green development, and regional development strategy selection. Firstly, the results show that China's carbon emission intensity has apparent spatial agglomeration and path dependence characteristics. Secondly, the economic agglomeration has the dual effect of energy saving and emission reduction. Furthermore, there is a significant inverted N-curve relationship between economic agglomeration and carbon emission intensity and carbon emissions, and a significant U-shaped curve relationship exists between economic agglomeration and energy intensity. Finally, economic agglomeration can indirectly affect carbon emission intensity through the mediating effect of energy intensity, and there is a significant inverted U-shaped curve relationship between energy intensity and carbon emission intensity. Therefore, promoting mutual coordination of environmental policies and building a regional collaborative governance mechanism is an effective way to achieve a win-win situation for the environment and economy of Beautiful China.

scholarly journals Agglomeration Production, Industry Association and Carbon Emission Performance: Based on Spatial Analysis

2020 ◽  
Vol 12 (18) ◽  
pp. 7234 ◽  
Author(s):  
Hui Peng ◽  
Yifan Wang ◽  
Yisha Hu ◽  
Hong Shen
Keyword(s):  
Energy Conservation ◽  
Emission Reduction ◽  
Carbon Emission ◽  
Large Scale ◽  
Trade Association ◽  
Emission Performance ◽  
Performance Factors ◽  
Agglomeration Externalities ◽  
Economic Agglomeration ◽  
Merchandise Trade

Current emission reduction policies have struggled to adapt to the reality of industrial spatial agglomeration and increasing industrial linkages. In response, this paper incorporates new economic geography factors such as agglomeration production and industrial (trade) association into the analysis framework of carbon emission performance factors through China’s provincial panel data and conducts empirical research. It has been found that large-scale industrial production under economic agglomeration is conducive to improving carbon emission performance and that different forms of agglomeration at different degrees of agglomeration correspond to different carbon emission performances. As the degree of agglomeration increases, the effect of reducing emissions by specialized agglomeration decreases while the effect of reducing emissions by diversified agglomeration increases. Specialized agglomeration externalities and diversified agglomeration externalities can coexist at the same time, depending on the appropriate degree of agglomeration. There is a strong negative environmental efficiency effect in the provinces with close commodity trade links, which has triggered environmental dumping and pollution transfer between provinces. In the work of energy conservation and emission reduction, we must attach great importance to the hidden carbon in domestic merchandise trade and the resulting intergovernmental environmental game, and furthermore, give full play to the “self-purification” effect of aggregate production on energy conservation and emission reduction.

scholarly journals A Study on The Driving Factors and Spatial Spillover of Carbon Emission Intensity in The Yangtze River Economic Belt under Double Control Action

2019 ◽  
Vol 16 (22) ◽  
pp. 4452 ◽  
Author(s):  
Xuhui Ding ◽  
Zhongyao Cai ◽  
Qianqian Xiao ◽  
Suhui Gao
Keyword(s):  
Technological Innovation ◽  
Emission Intensity ◽  
Emission Reduction ◽  
Yangtze River ◽  
Carbon Emission ◽  
Spillover Effects ◽  
The Yangtze River ◽  
Low Carbon ◽  
Spatial Spillover ◽  
Carbon Emission Intensity

It is greatly important to promote low-carbon green transformations in China, for implementing the emission reduction commitments and global climate governance. However, understanding the spatial spillover effects of carbon emissions will help the government achieve this goal. This paper selects the carbon-emission intensity panel data of 11 provinces in the Yangtze River Economic Belt from 2004 to 2016. Then, this paper uses the Global Moran’s I to explore the spatial distribution characteristics and spatial correlation of carbon emission intensity. Furthermore, this paper constructs a spatial econometric model to empirically test the driving path and spillover effects of relevant factors. The results show that there is a significant positive correlation with the provincial carbon intensity in the Yangtze River Economic Belt, but this trend is weakening. The provinces of Jiangsu, Zhejiang, and Shanghai are High–High agglomerations, while the provinces of Yunnan and Guizhou are Low–Low agglomerations. Economic development, technological innovation, and foreign direct investion (FDI) have positive effects on the reduction of carbon emissions, while industrialization has a negative effect on it. There is also a significant positive spatial spillover effect of the industrialization level and technological innovation level. The spatial spillover effects of FDI and economic development on carbon emission intensity fail to pass a significance test. Therefore, it is necessary to promote cross-regional low-carbon development, accelerate the R&D of energy-saving and emission-reduction technologies, actively enhance the transformation and upgrade industrial structures, and optimize the opening up of the region and the patterns of industrial transfer.

scholarly journals Influential Factors Regarding Carbon Emission Intensity in China: A Spatial Econometric Analysis from a Provincial Perspective

2020 ◽  
Vol 12 (19) ◽  
pp. 8097
Author(s):  
Li-Ming Xue ◽  
Shuo Meng ◽  
Jia-Xing Wang ◽  
Lei Liu ◽  
Zhi-Xue Zheng
Keyword(s):  
Spatial Econometrics ◽  
Emission Intensity ◽  
Emission Reduction ◽  
Carbon Emission ◽  
Spillover Effect ◽  
Influential Factors ◽  
Energy Structure ◽  
Driving Mechanism ◽  
Emission Mitigation ◽  
Carbon Emission Intensity

Emission reduction strategies based on provinces are key for China to mitigate its carbon emission intensity (CEI). As such, it is valuable to analyze the driving mechanism of CEI from a provincial view, and to explore a coordinated emission mitigation mechanism. Based on spatial econometrics, this study conducts a spatial-temporal effect analysis on CEI, and constructs a Spatial Durbin Model on the Panel data (SDPM) of CEI and its eight influential factors: GDP, urbanization rate (URB), industrial structure (INS), energy structure (ENS), energy intensity (ENI), technological innovation (TEL), openness level (OPL), and foreign direct investment (FDI). The main findings are as follows: (1) overall, there is a significant and upward trend of the spatial autocorrelation of CEI on 30 provinces in China. (2) The spatial spillover effect of CEI is positive, with a coefficient of 0.083. (3) The direct effects of ENI, ENS and TEL are significantly positive in descending order, while INS and GDP are significantly negative. The indirect effects of URB and ENS are significantly positive, while GDP, ENI, OPL and FDI are significantly negative in descending order. Economic and energy-related emission reduction measures are still crucial to the achievement of CEI reduction targets for provinces in China.

scholarly journals Energy saving and emission reduction of fossil energy based on low carbon economy and its consumption structure optimization

2019 ◽  
Vol 14 (3) ◽  
pp. 381-385 ◽  
Author(s):  
Yan Li ◽  
Guilin Dai
Keyword(s):  
Energy Saving ◽  
Emission Intensity ◽  
Emission Reduction ◽  
Carbon Emission ◽  
Total Carbon ◽  
Energy Structure ◽  
Low Carbon ◽  
Parallel Acquisition ◽  
Low Carbon Economy ◽  
Carbon Emission Intensity

Abstract Energy saving and emission reduction have been not only a slogan but also a policy in this modern society where the phenomenon of greenhouse is exacerbated. In this study, calculation method of carbon emission and integrated parallel acquisition technique (IPAT) scenario prediction model were combined to predict the changes of total carbon emissions, energy structure distribution, and carbon emission intensity under three measures of energy saving and emission reduction in the next ten years in Shandong, China. The results showed that the total carbon emission increased year by year, and the coal ratio and carbon emission intensity decreased under the natural scenario; the total carbon emission in the weakly constrained scenario would increase annually until 2029, the amplitude was smaller than that of the natural scenario, while the coal ratio and carbon emission intensity would decrease, and the amplitude was larger than that of the natural scenario. Under the strongly constrained scenario, the total carbon emission would increase annually before 2025, and the amplitude was smaller than the weakly constrained scenario, while the coal ratio and carbon emission intensity would decrease, and the amplitude was larger than the weakly constrained scenario.

scholarly journals Impact of Green Innovation Efficiency on Carbon Emission Reduction in the Guangdong-Hong Kong-Macao GBA

2021 ◽  
Vol 13 (23) ◽  
pp. 13450
Author(s):  
Lingming Chen ◽  
Congjia Huo
Keyword(s):  
Hong Kong ◽  
Linear Relationship ◽  
Spatial Correlation ◽  
Carbon Emissions ◽  
Emission Intensity ◽  
Emission Reduction ◽  
Carbon Emission ◽  
Green Innovation ◽  
Carbon Emission Reduction ◽  
Carbon Emission Intensity

Climate change has become a global issue of general concern to human society. It is not only an environmental issue, but also a development issue. As the second largest economy in the world, China has adhered to its commitments in the Paris Agreement and formulated a series of autonomous action targets. In this context, scholars have done a lot of research focusing on carbon emission reduction, but have neglected the spatial correlation of carbon emission, and lack of research on carbon emission reduction in urban agglomerations. The Guangdong-Hong Kong-Macao Greater Bay Area (GBA) has been at the forefront of China in terms of economy, politics, ecology, and civilization by taking advantage of the “one country, two systems” policy. This article innovatively proposes that there is a non-linear relationship between the efficiency of green innovation and the carbon emission intensity of the Guangdong-Hong Kong-Macao GBA, and has passed quantitative verification. Based on the panel data of the Guangdong-Hong Kong-Macao GBA from 2009 to 2019, we used the super-efficiency slacks-based measure (SBM) model to measure the efficiency of green innovation. We used the global Moran index and Theil index to discuss the spatial correlation of carbon emissions and regional differences in carbon emission intensity in the Guangdong-Hong Kong-Macao GBA, respectively. Then, we used the threshold model to verify the nonlinear relationship between the efficiency of green innovation and the intensity of carbon emissions in the Guangdong-Hong Kong-Macao GBA. The results of the study found that the green innovation efficiency of the Guangdong-Hong Kong-Macao GBA is increasing overall, carbon emissions have a certain spatial correlation, and the correlation is low overall. The impact of green innovation efficiency on carbon emission intensity has a non-linear relationship and there is an “inverted U” pattern between the two, and there is an inflection point in green innovation efficiency. Based on this, this article proposes carbon emission reduction measures within a reasonable range, and looks forward to future research directions and complement the research deficiencies.

scholarly journals Construction and Application Analysis of Carbon Emission Influence Factor Model of Energy Consumption in Mining Industry

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Lili Wei ◽  
Xiwen Feng ◽  
Guangyu Jia
Keyword(s):  
Energy Consumption ◽  
Emission Intensity ◽  
Carbon Emission ◽  
Energy Intensity ◽  
Mining Industry ◽  
Energy Structure ◽  
Low Carbon ◽  
Greenhouse Gas Inventory ◽  
Carbon Emission Intensity ◽  
Low Carbon Development

With the proposal of China’s “double carbon goal,” as a high energy-consuming industry, it is urgent for the mining industry to adopt a low-carbon development strategy. Therefore, in order to better provide reasonable suggestions and references for the low-carbon development of mining industry, referring to the methods and parameters of the 2006 IPCC National Greenhouse Gas Inventory Guidelines and China’s Provincial Greenhouse Gas Inventory Preparation Guidelines (Trial), a carbon emission estimation model is established to estimate the carbon emission of energy consumption of China's mining industry from 2000 to 2020. Then, using the extended Kaya identity, the influencing factors of carbon emission in mining industry are decomposed into energy carbon emission intensity, energy structure, energy intensity, industrial structure, and output value. On this basis, an LMDI model is constructed to analyze the impact of five factors on carbon emission from mining industry. The research shows that the carbon emission and carbon emission intensity of energy consumption in China’s mining industry first rise and then fall and then rise slightly. The carbon emission intensity in recent three years is about 2 tons/10000 yuan. The increase in output value is the main factor to increase carbon emission. The reduction in energy intensity is the initiative of carbon emission reduction. The current energy structure of mining industry is not conducive to carbon emission reduction.

scholarly journals Examining the link between carbon emission, carbon emission intensity and financial development in Nigeria. A Dynamic ARDL Simulation

2021 ◽  
Author(s):  
Kingsley Ikechukwu Okere ◽  
Maxwell Onyemachi Ogbulu ◽  
Obumneke Bob Muoneke ◽  
Favour Chidinma Onuoha ◽  
Agbede Moses Oyeyemi
Keyword(s):  
Emission Intensity ◽  
Co2 Emission ◽  
Carbon Emission ◽  
Energy Intensity ◽  
Economic Globalization ◽  
Bank Credit ◽  
Fossil Energy ◽  
Carbon Emission Intensity ◽  
Long Run ◽  
Short Run

Abstract The need for adequate and consistent policies to mitigate the continuous rise of carbon emission have motivated the energy economist in the past decades to actively involved and explore common economic agents that are driving the rising pattern in the environmental pollution. This study is positioned towards contributing to the on-going debates on this issue by exploring the impact of bank credit to the private sector on aggregate carbon emissions and carbon emission intensity in Nigeria over the period 1971– 2016 using dynamic ARDL simulations. Controlling for the influence of fossil energy intensity of consumption and economic globalization, the study found that bank credit to the private sector has a positive significant long-run increasing effect on aggregate CO2 emission and carbon emission intensity in the economy. Second, the estimated coefficients show that fossil energy intensity of consumption and economic globalization have a significant long-run and short-run increasing impact on aggregate CO2 emission and carbon emission intensity in the economy. In contrast, the population has a significant long-run and short-run reducing effect on aggregate CO2 emission and only the long run reducing effect on carbon emission intensity. Third, economic growth has significant short-run and increasing long-run effects on aggregate CO2 emission and a long run increasing effect on carbon emission intensity. In sum, the results show that the economy is yet to transient to renewable energy.

scholarly journals The Changes of Carbon Emission in China’s Industrial Sectors from 2002 to 2010: A Structural Decomposition Analysis and Input-Output Subsystem

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Guoxing Zhang ◽  
Mingxing Liu
Keyword(s):  
Carbon Emissions ◽  
Emission Intensity ◽  
Carbon Emission ◽  
Decomposition Analysis ◽  
Final Demand ◽  
Good Effect ◽  
Input Output ◽  
Carbon Emission Intensity ◽  
Industrial Sectors ◽  
Three Components

Based on 2002–2010 comparable price input-output tables, this paper first calculates the carbon emissions of China’s industrial sectors with three components by input-output subsystems; next, we decompose the three components into effect of carbon emission intensity, effect of social technology, and effect of final demand separately by structure decomposition analysis; at last, we analyze the contribution of every effect to the total emissions by sectors, thus finding the key sectors and key factors which induce the changes of carbon emissions in China’s industrial sectors. Our results show that in the latest 8 years five departments have gotten the greatest increase in the changes of carbon emissions compare with other departments and the effect of final demand is the key factor leading to the increase of industrial total carbon emissions. The decomposed effects show a decrease in carbon emission due to the changes of carbon emission intensity between 2002 and 2010 compensated by an increase in carbon emissions caused by the rise in final demand of industrial sectors. And social technological changes on the reduction of carbon emissions did not play a very good effect and need further improvement.

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