scholarly journals Exploring the Driving Forces and Reduction Potential of Industrial Energy-Related CO2 Emissions during 2001–2030: A Case Study for Henan Province, China

2019 ◽  
Vol 11 (4) ◽  
pp. 1176 ◽  
Author(s):  
Lei Liu ◽  
Ke Wang ◽  
Shanshan Wang ◽  
Ruiqin Zhang ◽  
Xiaoyan Tang
Keyword(s):  
Co2 Emissions ◽  
Co2 Emission ◽  
Driving Forces ◽  
Henan Province ◽  
Policy Implications ◽  
Energy Structure ◽  
Annual Growth Rate ◽  
Industrial Emissions ◽  
Economic Output ◽  
Industrial Energy

In China, the industrial sector is the main contributor to economic development and CO2 emissions, especially for the developing regional provinces. This study employs the Logarithmic Mean Divisia Index (LMDI) approach to decompose industrial energy-related CO2 emission into eight factors during 2001–2015 for Henan Province. Furthermore, the future CO2 emissions under different scenarios (Business as Usual (BAU), Efficiency Improvement (EI), Structural Optimization (SO), R&D Input (RD), and Comprehensive Policy (CP) scenarios) over 2016–2030 are projected. The results indicate that among these factors, the economic output, R&D intensity, investment intensity, and energy structure are the drivers for increasing CO2 emissions over the entire period, with the contribution of 293, 83, 80, and 1% of the total CO2 emissions changes, respectively. Conversely, the energy intensity, R&D efficiency, and industrial internal structure can decrease CO2 emissions with contributions of –86, –163, and –108% to the changes, respectively. Under the five scenarios, CO2 emissions in 2030 will reach 1222, 1079, 793, 987, and 638 Mt with an annual growth rate of 4.7%, 3.8%, 1.8%, 3.3%, and 0.4%, respectively. In particular, the CO2 emission peak for SO and CP scenarios is observed before 2030. Finally, some policy implications are suggested to further mitigate industrial emissions.

Decomposition analysis of energy-related CO2 emissions in South Africa

2015 ◽  
Vol 26 (1) ◽  
pp. 67-73 ◽  
Author(s):  
Ming Zhang ◽  
Shuang Dai ◽  
Yan Song
Keyword(s):  
South Africa ◽  
Co2 Emissions ◽  
Co2 Emission ◽  
Driving Forces ◽  
Dominant Role ◽  
Decomposition Analysis ◽  
Structure Effect ◽  
Energy Structure ◽  
Index Method ◽  
Intensity Effect

South Africa has become one of the most developing countries in the world, and its economic growth has occurred along with rising energy-related CO2 emission levels. A deeper understanding of the driving forces governing energy-related CO2 emissions is very important in formulating future policies. The LMDI (Log Mean Divisia Index) method is used to analyse the contribution of the factors which influence energy-related CO2 emissions in South Africa over the period 1993-2011. The main conclusions drawn from the present study may be summarized as follows: the energy intensity effect plays the dominant role in decreasing of CO2 emission, followed by fossil energy structure effect and renewable energy structure effect; the economic activity is a critical factor in the growth of energy-related CO2 emission in South Africa.

scholarly journals Industrial Energy-Related CO2 Emissions and Their Driving Factors in the Yangtze River Economic Zone (China): An Extended LMDI Analysis from 2008 to 2016

2020 ◽  
Vol 17 (16) ◽  
pp. 5880
Author(s):  
Linlin Ye ◽  
Xiaodong Wu ◽  
Dandan Huang
Keyword(s):  
Co2 Emissions ◽  
Influencing Factors ◽  
Yangtze River ◽  
Energy Intensity ◽  
Driving Factors ◽  
Energy Structure ◽  
The Yangtze River ◽  
Economic Output ◽  
Industrial Energy ◽  
Economic Zone

As the world’s largest developing country in the world, China consumes a large amount of fossil fuels and this leads to a significant increase in industrial energy-related CO2 emissions (IECEs). The Yangtze River Economic Zone (YREZ), accounting for 21.4% of the total area of China, generates more than 40% of the total national gross domestic product and is an important component of the IECEs from China. However, little is known about the changes in the IECEs and their influencing factors in this area during the past decade. In this study, IECEs were calculated and their influencing factors were delineated based on an extended logarithmic mean Divisia index (LMDI) model by introducing technological factors in the YREZ during 2008–2016. The following conclusions could be drawn from the results. (1) Jiangsu and Hubei were the leading and the second largest IECEs emitters, respectively. The contribution of the cumulative increment of IECEs was the strongest in Jiangsu, followed by Anhui, Jiangxi and Hunan. (2) On the whole, both the energy intensity and R&D efficiency play a dominant role in suppressing IECEs; the economic output and investment intensity exert the most prominent effect on promoting IECEs, while there were great differences among the major driving factors in sub-regions. Energy structure, industrial structure and R&D intensity play less important roles in the IECEs, especially in the central and western regions. (3) The year of 2012 was an important turning point when nearly half of these provinces showed a change in the increment of IECEs from positive to negative values, which was jointly caused by weakening economic activity and reinforced inhibitory of energy intensity and R&D intensity.

scholarly journals Scenario Analysis on Energy Consumption and CO2 Emissions Reduction Potential in Building Heating Sector at Community Level

2019 ◽  
Vol 11 (19) ◽  
pp. 5392 ◽  
Author(s):  
Chuan Tian ◽  
Guohui Feng ◽  
Shuai Li ◽  
Fuqiang Xu
Keyword(s):  
Energy Consumption ◽  
Co2 Emissions ◽  
Co2 Emission ◽  
Population Data ◽  
Driving Factors ◽  
Policy Implications ◽  
Energy Planning ◽  
Emissions Reduction ◽  
Energy Structure ◽  
Baseline Scenario

Energy consumption and carbon emissions of building heating are increasing rapidly. Taking Liaobin coastal economic zone as an example, two scenarios are built to analyze the potential of energy consumption and CO2 emissions reduction from the aspects of laws, regulations, policies and planning. The baseline scenario refers to the traditional way of energy planning and the community energy planning scenario seeks to apply community energy planning within the zone. Energy consumption and CO2 emission are forecast in two scenarios with the driving factors including GDP growth, changes in population size, energy structure adjustment, energy technology progress, and increase of energy efficiency. To improve accuracy of future GDP and population data prediction, an ARIMA (Autoregressive Integrated Moving Average model) (1,1,1) model is introduced into GDP prediction and a logistics model is introduced into population prediction. Results show that compared with the baseline scenario, energy consumption levels in the community energy planning scenario are reduced by 140% and CO2 emission levels are reduced by 45%; the short-term and long-term driving factors are analyzed. Policy implications are given for energy conservation and environmental protection.

scholarly journals The Regional Inequity of CO2 Emissions per Capita in China

2017 ◽  
Vol 9 (7) ◽  
pp. 228 ◽  
Author(s):  
Ting Liu ◽  
Wenqing Pan
Keyword(s):  
Co2 Emissions ◽  
Emission Reduction ◽  
Co2 Emission ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Theil Index ◽  
Energy Structure ◽  
Co2 Emission Reduction ◽  
Index Method ◽  
Per Capita

This paper combines Theil index method with factor decomposition technique to analyze China eight regions’ inequality of CO2 emissions per capita, and discuss energy structure, energy intensity, industrial structure, and per capita output’s impacts on inequality. This research shows that: (1) The trend of China regional carbon inequality is in the opposite direction to the per capita CO2 emission level. Namely, as the per capita CO2 emission levels rise, regional carbon inequality decreases, and vice versa. (2) Per capita output factor reduces regional carbon inequality, whereas energy structure factor and energy intensity factor increase the inequality. (3) More developed areas can reduce the carbon inequality by improving the energy structure, whereas the divergence of energy intensity in less developed areas has increased to expand the carbon inequity. Thus, when designing CO2 emission reduction targets, policy makers should consider regional differences in economic development level and energy efficiency, and refer to the main influencing factors. At the same time, upgrading industrial structure and upgrading energy technologies should be combined to meet the targets of economic growth and CO2 emission reduction.

scholarly journals Spatio-Temporal Analysis of CO2 Emission Driving Force in Various Provinces in China Using the Extended STIRPAT-GWR Model

2021 ◽  
Author(s):  
Yulin Zhang
Keyword(s):  
Co2 Emissions ◽  
Co2 Emission ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Temporal Analysis ◽  
Energy Structure ◽  
High Effect ◽  
Northern Regions ◽  
Spatio Temporal ◽  
Gwr Model

To fill the shortcomings of traditional research that ignores the driver’s own spatial characteristics and provide a theoretical support to formulate suitable emission reduction policies in different regions across China. In this pursuit, based on the panel data of provincial CO2 emission in 2007, 2012, and 2017, the present study employed the extended environmental impact assessment model (STIRPAT-GWR model) to study the effect of population, energy intensity, energy structure, urbanization and industrial structure on the CO2 emissions in 29 provinces across China. The empirical results show that the effect of drivers on the CO2 emissions exhibited significant variations among the different provinces. The effect of population in the southwest region was significantly lower than that of the central and eastern regions. Provinces with stronger energy intensity effects were concentrated in the central and western regions. The effect of energy structure in the eastern and northern regions was relatively strong, and gradually weakened towards the southeast region. The areas with high urbanization effect were concentrated in the central and the eastern regions. Furthermore, significant changes were observed in the high-effect regions of the industrial structure in 2017. The high-effect area showed a migration from the northwest and northeast regions in 2007 and 2012, respectively, to the southwest and southeast regions in 2017. Urbanization showed the strongest effect on the CO2 emissions, followed by population and energy intensity, and the weakest effect was exhibited by the energy and industrial structure. Thus, the effects of population and energy structure showed a downward trend, in contrary to the effect of urbanization on the CO2 emissions in China.

scholarly journals Dynamics of Japan’s Industrial Production and CO2 Emissions: Causality, Long-Run Trend and Implication

2019 ◽  
Author(s):  
Wahid Murad ◽  
Md. Mahmudul Alam ◽  
Mazharul Islam
Keyword(s):  
Granger Causality ◽  
Co2 Emissions ◽  
Industrial Production ◽  
Co2 Emission ◽  
Industrial Sector ◽  
Industrial Emissions ◽  
Iron And Steel ◽  
Long Run ◽  
Equilibrium Level ◽  
Residential And Commercial Sectors

While CO2 emissions from the residential and commercial sectors of Japan have increased significantly since 1990 the country‟s industrial emissions make up the largest share of those emissions. The historical CO2 emission performance data also indicate that the iron and steel, chemical, paper and pulp and cement were the top four largest industrial emitters, and these top four emitting industries contributed nearly two-third of the industrial sector‟s total CO2 emission amount during 1990-2015. Evidently, any appropriate efforts or strategies guided by an empirical investigation like this are expected to help Japan‟s industrial emitters move toward a more tolerable and less polluted carbon footprint, which is well-matched with the country‟s commitment to Kyoto Protocol. This study is thus an effort to empirically investigate the causality and long-run trend/relationship between Japan‟s industrial production and CO2 emissions and to propose some corporate environmental strategies using the econometric techniques of Vector Error Correction (VEC) and Granger causality. It found that there exists no Granger causality between Japan‟s industrial production and CO2 emissions in any direction. But the VEC estimation reveals that an increase in Japan‟s industrial production by 1% is associated with a 0.08% increase in the country‟s CO2 emissions. It also reveals that any disequilibrium between Japan‟s industrial production and CO2 emissions could take about 0.7 quarters for half of the error to be corrected for. The adjustment rate for Japan‟s industrial production is found to be positive but quite slow at the rate of 0.08% per year. Since Japan‟s CO2 emissions vis-à-vis its industrial production is found to have reached above the long-run equilibrium level, its industrial sector is expected to encounter with stricter government regulations requiring reduction of CO2 emissions to the targeted/equilibrium level in the future.

scholarly journals Analysis of CO2 Drivers and Emissions Forecast in a Typical Industry-Oriented County: Changxing County, China

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1212 ◽  
Author(s):  
Yao Qian ◽  
Lang Sun ◽  
Quanyi Qiu ◽  
Lina Tang ◽  
Xiaoqi Shang ◽  
...  
Keyword(s):  
Co2 Emissions ◽  
Industrial Structure ◽  
Ghg Emissions ◽  
Energy Structure ◽  
Low Carbon ◽  
Stirpat Model ◽  
Carbon Reduction ◽  
Economic Output ◽  
Low Carbon Development ◽  
And Control

Decomposing main drivers of CO2 emissions and predicting the trend of it are the key to promoting low-carbon development for coping with climate change based on controlling GHG emissions. Here, we decomposed six drivers of CO2 emissions in Changxing County using the Logarithmic Mean Divisia Index (LMDI) method. We then constructed a model for CO2 emissions prediction based on a revised version of the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model and used it to simulate energy-related CO2 emissions in five scenarios. Results show that: (1) From 2010 to 2017, the economic output effect was a significant, direct, dominant, and long-term driver of increasing CO2 emissions; (2) The STIRPAT model predicted that energy structure will be the decisive factor restricting total CO2 emissions from 2018 to 2035; (3) Low-carbon development in the electric power sector is the best strategy for Changxing to achieve low-carbon development. Under the tested scenarios, Changxing will likely reach peak total CO2 emissions (17.95 million tons) by 2030. Measures focused on optimizing the overall industrial structure, adjusting the internal industry sector, and optimizing the energy structure can help industry-oriented counties achieve targeted carbon reduction and control, while simultaneously achieving rapid economic development.

scholarly journals Decomposition and Decoupling Analysis of CO2 Emissions Based on LMDI and Two-Dimensional Decoupling Model in Gansu Province, China

2021 ◽  
Vol 18 (11) ◽  
pp. 6013
Author(s):  
Lele Xin ◽  
Junsong Jia ◽  
Wenhui Hu ◽  
Huiqing Zeng ◽  
Chundi Chen ◽  
...  
Keyword(s):  
Co2 Emissions ◽  
Gansu Province ◽  
Western China ◽  
Energy Structure ◽  
Kuznets Curve ◽  
Two Dimensional ◽  
Contribution Rate ◽  
Intensity Effect ◽  
Economic Output ◽  
Secondary Industry

Currently, little attention has been paid to reducing carbon dioxide (CO2) emissions of Gansu, and the two-dimensional decoupling model has been rarely used to study the relationship between the economic development and CO2 emissions, especially in western China (e.g., Gansu). Thus, here, we first used the Logarithmic Mean Divisia Index (LMDI) to decompose the driving factors of Gansu’s CO2 emissions between 2000–2017 and then analyzed the decoupling relationship by using the two-dimensional model. Results showed: (1) Gansu’s CO2 emissions increased from 7805.70 × 104 t in 2000 to 19,896.05 × 104 t in 2017. The secondary industry accounted for the largest proportion in Gansu’s CO2 emissions, followed by the tertiary industry and the primary industry. (2) The economic output showed the dominant driving effect on Gansu’s CO2 emissions growth with the cumulative contribution rate of 201.94%, followed by the effects of industrial structure, population size, and energy structure, and their cumulative contribution rates were 9.68%, 7.81%, and 3.05%, respectively. In contrast, the energy intensity effect presented the most obvious mitigating effect with the cumulative contribution rate of −122.49%. (3) The Environmental Kuznets Curve (EKC) between CO2 emissions and economic growth was demonstrated the inverted U-shape in Gansu. The two-dimensional decoupling status was the low level-weak decoupling (WD-LE) during 2000–2017. Thus, dropping the proportion of the secondary industry, reducing the use of carbon-intensive fuel like coal, introducing advanced technologies, and increasing the investment of new energy might effectively restrain the growth of Gansu’s CO2 emissions.

scholarly journals Research on the Temporal and Spatial Distribution Characteristics of Thermal Anomalies in Henan Province from 2008 to 2019

2021 ◽  
Vol 252 ◽  
pp. 03026
Author(s):  
Qiang Ge ◽  
Wenju Shen ◽  
Shenshen Li ◽  
Kun Cai
Keyword(s):  
Spatial Distribution ◽  
Forest Fires ◽  
Henan Province ◽  
Annual Growth Rate ◽  
Distribution Characteristics ◽  
Industrial Emissions ◽  
Temporal And Spatial Distribution ◽  
Thermal Anomalies ◽  
Spatial Distribution Characteristics ◽  
Temporal And Spatial

Using MODIS standard products, the temporal and spatial distribution characteristics of thermal anomalies in Henan Province in the past 12 years (2008~2019) were studied. The results found that in terms of spatial distribution, thermal anomalies were mostly concentrated in Luohe, Zhumadian, Pingdingshan, Puyang, and Shangqiu. The number of areas under the jurisdiction of Anyang, Hebi, Nanyang and Xinyang is relatively high. On the inter-annual trend, the number of thermal anomalies continued to increase from 2009 to 2013, with an average annual growth rate of 28.3%, and a continuous decline from 2013 to 2018. The decline rate was 18.4%, during which the number reached a peak of 5,843 in 2013. In terms of seasonal changes, the number of summer thermal anomalies is the largest, at 25,361, and thermal anomalies of summer are mostly concentrated in most areas of Zhumadian, Pingdingshan, Puyang and Shangqiu; the number of thermal anomalies in winter is the least, 3974, which are relatively mostly distributed in the mountainous areas of Nanyang and Xinyang. This study helps to understand the forest fires in typical areas in Henan Province, as well as heat caused by straw burning, industrial emissions, etc. Thermal anomalies changes provide technical support for regional disaster prevention and environmental monitoring.

Sign in / Sign up

Export Citation Format

Share Document