scholarly journals Can Innovation Agglomeration Reduce Carbon Emissions? Evidence from China

2021 ◽  
Vol 18 (2) ◽  
pp. 382
Author(s):  
Jianqing Zhang ◽  
Haichao Yu ◽  
Keke Zhang ◽  
Liang Zhao ◽  
Fei Fan
Keyword(s):  
Carbon Emissions ◽  
Energy Intensity ◽  
Spillover Effect ◽  
Decisive Role ◽  
Close Correlation ◽  
Mediating Effect ◽  
Promoting Effect ◽  
Degree Correlation ◽  
Surrounding Areas ◽  
The Impact

Innovation agglomeration plays a decisive role in improving the input–output scale and marginal output efficiency of factors. This paper takes carbon emissions as the unexpected output and energy consumption as the input factor into the traditional output density model. The dynamic spatial panel Durbin model is used to analyze the mechanism for innovation agglomeration and energy intensity to affect carbon emissions from 2004 to 2017 in thirty Chinese provinces. Then, we test the possible mediating effect of energy intensity between innovation agglomeration and carbon emissions. The major findings are as follows. (1) The carbon emission intensity has time-dependence and positive spatial spillover effect. That is, there is a close correlation between current and early carbon emissions, and there is also a high-degree correlation between regional and surrounding areas’ carbon emissions. (2) Carbon emissions keep a classical inverted U-shaped relation with innovation agglomeration, as well as with energy intensity. However, the impact of innovation agglomeration on carbon emissions in inland regions of China does not appear on the right side of the inverted U-shaped curve, while carbon emissions are subject to a positive nonlinear promoting effect from energy intensity. (3) When the logarithm of innovation agglomeration is more than 3.0309, it first shows the inhibition effect on energy intensity. With the logarithm of innovation agglomeration exceeding 5.0100, it will show the dual effect of emission reduction and energy conservation. (4) Energy intensity could work as the intermediary variable of innovation agglomeration’s influence on carbon emissions. Through its various positive externalities, innovation agglomeration can produce a direct impact on carbon emissions, and through energy intensity, it can also affect carbon emissions indirectly.

scholarly journals Factors Affecting Energy-Related Carbon Emissions in Beijing-Tianjin-Hebei Region

2017 ◽  
Vol 2017 ◽  
pp. 1-17 ◽  
Author(s):  
Jing-min Wang ◽  
Yu-fang Shi ◽  
Xue Zhao ◽  
Xue-ting Zhang
Keyword(s):  
Carbon Emissions ◽  
Carbon Emission ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Energy Structure ◽  
Factors Affecting ◽  
Economic Output ◽  
Level Model ◽  
The Impact ◽  
The Relationship

Beijing-Tianjin-Hebei is a typical developed region in China. The development of economy has brought lots of carbon emissions. To explore an effective way to reduce carbon emissions, we applied the Logarithmic Mean Divisia Index (LMDI) model to find drivers behind carbon emission from 2003 to 2013. Results showed that, in Beijing, Tianjin, and Hebei, economic output was main contributor to carbon emissions. Then we utilized the decoupling model to comprehensively analyze the relationship between economic output and carbon emission. Based on the two-level model, results indicated the following: (1) Industry sector accounted for almost 80% of energy consumption in whole region. The reduced proportion of industrial GDP will directly reduce the carbon emissions. (2) The carbon factor for CO2/energy in whole region was higher than that of Beijing and Tianjin but lower than that of Hebei. The impact of energy structure on carbon emission depends largely on the proportion of coal in industry. (3) The energy intensity in whole region decreased from 0.79 in 2003 to 0.40 in 2013 (unit: tons of standard coal/ten thousand yuan), which was lower than national average. (4) The cumulative effects of industrial structure, energy structure, and energy intensity were negative, positive, and negative, respectively.

scholarly journals The Impact of Economy on Carbon Emissions: An Empirical Study Based on the Synergistic Effect of Gender Factors

2019 ◽  
Vol 16 (19) ◽  
pp. 3723 ◽  
Author(s):  
Shiran Li ◽  
Hongbing Deng ◽  
Kangkang Zhang
Keyword(s):  
Carbon Emissions ◽  
Carbon Emission ◽  
Negative Impact ◽  
Probability Model ◽  
National Level ◽  
Bayesian Posterior Probability ◽  
Model Structure ◽  
Surrounding Areas ◽  
The Impact ◽  
Peripheral Regions

The study of carbon emissions is of great significance for environmental change and economic development. Gender factors is an important perspective to examine the path of carbon emissions. Based on the panel data of 30 provinces in China from 2005 to 2016, this paper selects the optimal spatial measurement model structure by using the Bayesian posterior probability model structure selection method, and studies the impact of economy on carbon emissions and the influence mechanism of gender-based “synergy effect” on carbon emissions from the National level and regional levels. The research shows that the increase of economic promotes the increase of carbon emission in this region, but it has a restraining effect on the carbon emission in the surrounding areas. Moreover, gender factors have a significant positive effect on the region at the National level and the Eastern and Northeastern regions, but not significantly in other ones, and have a significant negative impact on carbon emissions in surrounding areas. Overall, the influence intensity of economy on carbon emission increases with the increase of gender in the National level and the Eastern and Northeastern, while the influence intensity of economy of peripheral regions on carbon emission in Central Region decreases with the increase of gender factors in peripheral regions.

scholarly journals The Impact of Government Competition on Regional R&D Efficiency: Does Legal Environment Matter in China’s Innovation System?

2018 ◽  
Vol 10 (12) ◽  
pp. 4401 ◽  
Author(s):  
Baocheng He ◽  
Jiawei Wang ◽  
Jiaoyang Wang ◽  
Kun Wang
Keyword(s):  
Local Governments ◽  
High Efficiency ◽  
Eastern China ◽  
Innovation System ◽  
Spillover Effect ◽  
Western China ◽  
Legal Environment ◽  
Promoting Effect ◽  
Spatial Panel Data ◽  
The Impact

Local governments are encouraged to compete in R&D investments and activities in China’s innovation system. We aim to understand the influence of government competition on regional R&D efficiency. We are also interested in examining how the attributes of legal environment act as a moderating variable for the relationship between government competition and R&D efficiency. We developed Tobit spatial models with spatial panel data of 30 provinces of China in 2008–2016. The results show that: (1) There exists spatial dependence of R&D efficiency, and the regions with high efficiency have “spillover effect” on the surrounding areas. (2) Government competition has a significant promoting effect on R&D efficiency and/or R&D efficiency spillover. Specifically, government competition has both R&D efficiency promotion and R&D efficiency “spillover” promotion in Eastern China, only R&D efficiency positive spillover promotion in Middle-area and R&D efficiency promotion but negative spillover in Western China. (3) The impact of government competition on efficiency is affected by the legal environment, and the promotion effect of government competition only exists in good legal environment. The results of this study reveal an important way to improve R&D efficiency by establishing a new R&D competition mechanism for local government which is oriented by efficiency and ruled by the legal environment.

scholarly journals National Carbon Accounting—Analyzing the Impact of Urbanization and Energy-Related Factors upon CO2 Emissions in Central—Eastern European Countries by Using Machine Learning Algorithms and Panel Data Analysis

Energies ◽  
2021 ◽  
Vol 14 (10) ◽  
pp. 2775
Author(s):  
Florian Marcel Nuţă ◽  
Alina Cristina Nuţă ◽  
Cristina Gabriela Zamfir ◽  
Stefan-Mihai Petrea ◽  
Dan Munteanu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Data Analysis ◽  
Panel Data ◽  
Co2 Emissions ◽  
Carbon Emissions ◽  
Urban Areas ◽  
Energy Intensity ◽  
Panel Data Analysis ◽  
Machine Learning Algorithms ◽  
The Impact

The work at hand assesses several driving factors of carbon emissions in terms of urbanization and energy-related parameters on a panel of emerging European economies, between 1990 and 2015. The use of machine learning algorithms and panel data analysis offered the possibility to determine the importance of the input variables by applying three algorithms (Random forest, XGBoost, and AdaBoost) and then by modeling the urbanization and the impact of energy intensity on the carbon emissions. The empirical results confirm the relationship between urbanization and energy intensity on CO2 emissions. The findings emphasize that separate components of energy consumption affect carbon emissions and, therefore, a transition toward renewable sources for energy needs is desirable. The models from the current study confirm previous studies’ observations made for other countries and regions. Urbanization, as a process, has an influence on the carbon emissions more than the actual urban regions do, confirming that all the activities carried out as urbanization efforts are more harmful than the resulted urban area. It is proper to say that the urban areas tend to embrace modern, more green technologies but the road to achieve environmentally friendly urban areas is accompanied by less environmentally friendly industries (such as the cement industry) and a high consumption of nonrenewable energy.

scholarly journals Research on the impact of economic growth and technological innovation on carbon emissions from the perspective of space

2021 ◽  
Vol 275 ◽  
pp. 02014
Author(s):  
Bingwen Bao ◽  
Beiqiao Lin
Keyword(s):  
Economic Growth ◽  
Technological Innovation ◽  
Carbon Emissions ◽  
Positive Response ◽  
Geographic Distance ◽  
Spillover Effect ◽  
Economic Activities ◽  
Vector Autoregressive ◽  
Response Characteristics ◽  
The Impact

As many economic activities have a spatial spillover effect on carbon emissions, this paper selects the data of various provinces and cities from 2003 to 2017 to construct a spatial vector autoregressive model to analyze and study the effects of economic growth and technological innovation on carbon emissions from a spatial perspective. The results of the study found that carbon emissions, economic growth, and technological innovation generally showed positive response characteristics after being impacted by each other in time, but they weakened over time. In space, carbon emissions will also increase with geographic distance, and the response received will continue to weaken.

scholarly journals Can Compulsory Ecological Compensation for Land Damaged by Mining Activities Mitigate CO2 Emissions in China?

2021 ◽  
Vol 9 ◽  
Author(s):  
Siyao Wang ◽  
Nazmiye Balta-Ozkan ◽  
Julide Yildirim ◽  
Fu Chen ◽  
Yinghong Wang
Keyword(s):  
Energy Intensity ◽  
Land Reclamation ◽  
Regional Cooperation ◽  
Spillover Effect ◽  
Chinese Government ◽  
Kuznets Curve ◽  
Coal Production ◽  
Ecological Compensation ◽  
Carbon Neutrality ◽  
The Impact

Chinese government has proposed a national contribution plan that involves achieving the peak CO2 emissions by 2030 and carbon neutrality by 2060. To explore the pathway of achieving carbon neutrality, we tried to use resources taxes and land reclamation deposits as compulsory ecological compensation (CEC). In order to test if CEC can affect CO2 emissions, energy intensity was selected as the intermediate variable. We found that the CO2 emissions trend in China is consistent with environmental Kuznets curve hypothesis and proved that CEC displayed a spillover effect on energy intensity. Likely, energy intensity presented a spillover effect on CO2 emissions. Therefore, CEC will spatially affect CO2 emissions. The generalized spatial two-stage least-squares estimate model was used to identify the impact mechanism of coal production on energy intensity with CEC as the instrumental variable. The results indicated that reducing coal production in neighboring regions may cause the mitigation of local CO2 emissions. Finally, regression analyses carried out by region suggested regional cooperation should be carried out in the process of carbon mitigation.

scholarly journals The Impact of Economic Growth, FDI and Energy Intensity on China’s Manufacturing Industry’s CO2 Emissions: An Empirical Study Based on the Fixed-Effect Panel Quantile Regression Model

Energies ◽  
2019 ◽  
Vol 12 (24) ◽  
pp. 4800 ◽  
Author(s):  
Chao-Qun Ma ◽  
Jiang-Long Liu ◽  
Yi-Shuai Ren ◽  
Yong Jiang
Keyword(s):  
Economic Growth ◽  
Co2 Emissions ◽  
Carbon Emissions ◽  
Co2 Emission ◽  
Manufacturing Industry ◽  
Energy Intensity ◽  
Quantile Regression Model ◽  
Panel Quantile Regression ◽  
The Impact ◽  
Positive Effect

Since the reform and opening-up, China’s CO2 emissions have increased dramatically, and it has become the world’s largest CO2 emission and primary energy consumption country. The manufacturing industry is one of the biggest contributors to CO2 emission, and determining the drivers of CO2 emissions are essential for effective environmental policy. China is also a vast transition economy with great regional differences. Therefore, based on the data of China’s provincial panel from 2000 to 2013 and the improved STIRPAT model, this paper studies the impact of economic growth, foreign direct investment (FDI) and energy intensity on China’s manufacturing carbon emissions through the fixed-effect panel quantile regression model. The results show that the effects of economic growth, FDI and energy intensity on carbon emissions of the manufacturing industry are different in different levels and regions, and they have apparent heterogeneity. In particular, economic growth plays a decisive role in the CO2 emissions of the manufacturing industry. Economic growth has a positive impact on the carbon emissions of the manufacturing industry; specifically, a higher impact on high carbon emission provinces. Besides, FDI has a significant positive effect on the upper emission provinces of the manufacturing industry, which proves that there is a pollution paradise hypothesis in China’s manufacturing industry, but no halo effect hypothesis. The reduction of energy intensity does not have a positive effect on the reduction of carbon emissions. The higher impact of the energy intensity of upper emission provinces on carbon emissions from their manufacturing industry, shows that there is an energy rebound effect in China’s manufacturing industry. Finally, our study confirms that China’s manufacturing industry has considerable space for emission reduction. The results also provide policy recommendations for policymakers.

scholarly journals Electricity Consumption in China: The Effects of Financial Development and Trade Openness

2021 ◽  
Vol 13 (18) ◽  
pp. 10206
Author(s):  
Ruijun Duan ◽  
Peng Guo
Keyword(s):  
Financial Development ◽  
Trade Openness ◽  
Spillover Effect ◽  
Electricity Consumption ◽  
Critical Issue ◽  
Positive Change ◽  
Promoting Effect ◽  
The Impact ◽  
Panel Vector Autoregression

As China is facing the double pressure of economic growth as well as energy-saving and reduction of emissions, reducing electricity consumption without affecting economic development is a challenging and critical issue. Based on 31 provincial panel’s data in China from 2004 to 2018, this study empirically analyzes the direction and degree of the impact of financial development and trade openness on electricity consumption using the spatial econometric approach and panel vector autoregression (PVAR) model. The results indicate that China’s electricity consumption presents a significant spatial spill over effect, and the spatial agglomeration of electricity consumption in local regions is mainly HH clusters. A 1% positive change in financial development causes an increase of 0.089% in electricity consumption, but a 1% rise in financial development reduces electricity consumption of neighboring regions by 0.051%. A 1% positive change in trade openness decreases electricity consumption by 0.051%, while the spatial spillover effect of trade openness is not significant. It is also found that financial development has a long-term promoting effect on electricity consumption, while trade openness has a long-term inhibiting effect on electricity consumption.

scholarly journals Research on the promoting effect of servitization on export technological sophistication of manufacturing enterprises

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255891
Author(s):  
Yuanhong Hu ◽  
Sheng Sun ◽  
Min Jiang ◽  
Yixin Dai
Keyword(s):  
Value Chain ◽  
Production Efficiency ◽  
Spillover Effect ◽  
Global Value Chain ◽  
Mechanism Analysis ◽  
Financial Industry ◽  
Promoting Effect ◽  
Manufacturing Enterprises ◽  
Input Source ◽  
The Impact

Based on multiple micro databases involving Chinese manufacturing enterprises and World Input-Output Database, this article investigates the impact of China’s manufacturing servitization on export technological sophistication from 2000 to 2010. The results show that manufacturing servitization has an inverted U-shaped impact on export technological sophistication. From the perspective of heterogeneity at the enterprise level and industry level, manufacturing servitization has an inverted U-shaped impact on export technological sophistication for mixed trade enterprises, central and western located enterprises, domestic and foreign enterprises, and knowledge-intensive industries, the nonlinear impact is in the promotion range. Besides, manufacturing servitization with domestic and foreign service input source has an inverted U-shaped impact on export technological sophistication, manufacturing servitization with the domestic consumption-oriented service input source and foreign production-oriented service input source have a promoting effect. Servitization with financial industry and technical research and development service source has a promoting effect, while servitization with transportation service input source has an inverted U-shaped effect. Overall global value chain participation level and simple global value chain participation have a positive moderating effect on the impact, especially for enterprises with lower production efficiency. Mechanism analysis confirms that the "spillover" effect and "cost" effect are important channels for manufacturing servitization to promote export technological sophistication.

Sign in / Sign up

Export Citation Format

Share Document