Can the Financial Industry ‘Anchor’ Carbon Emission Reductions?

2022 ◽  
pp. 0958305X2110618
Author(s):  
Shuhong Wang ◽  
Xiaojing Yi
Keyword(s):  
Carbon Emissions ◽  
Emission Intensity ◽  
Carbon Emission ◽  
Central Government ◽  
Economic Loss ◽  
Positive Influence ◽  
Financial Industry ◽  
Industry Development ◽  
Carbon Emission Intensity ◽  
Using Data

Existing research is ambiguous about the relationship between the financial industry development scale and carbon emission reduction targets. Therefore, using data from 30 provinces and municipalities directly under the central government (excluding Tibet, Hong Kong, Macao, and Taiwan) from 2009–2018, this study divides the reduction targets into emission quantity and intensity to investigate this relationship. Using the improved STIRPAT equation, the pooled OLS and other estimation technique in robustness test, we found that the financial industry development scale is positively related to emission quantity and negatively related to emission intensity. The financial industry development scale inhibits carbon emission intensity through the mediating role of the technology market development degree, which also has a moderating effect on the scale. The study also discusses the regional differences in the scale's impact on carbon emission intensity, its compensation effect on the economic loss caused by carbon emissions, and the positive influence of policy implementation on carbon emission intensity. We provide suggestions to reduce carbon emissions and achieve carbon neutrality.

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.

scholarly journals Crop Production and Agricultural Carbon Emissions: Relationship Diagnosis and Decomposition Analysis

2021 ◽  
Vol 18 (15) ◽  
pp. 8219
Author(s):  
Jianli Sui ◽  
Wenqiang Lv
Keyword(s):  
Economic Growth ◽  
Agricultural Economic ◽  
Carbon Emissions ◽  
Emission Intensity ◽  
Crop Production ◽  
Carbon Emission ◽  
Decomposition Analysis ◽  
Lmdi Method ◽  
Carbon Emission Intensity ◽  
Decoupling Analysis

Modern agriculture contributes significantly to greenhouse gas emissions, and agriculture has become the second biggest source of carbon emissions in China. In this context, it is necessary for China to study the nexus of agricultural economic growth and carbon emissions. Taking Jilin province as an example, this paper applied the environmental Kuznets curve (EKC) hypothesis and a decoupling analysis to examine the relationship between crop production and agricultural carbon emissions during 2000–2018, and it further provided a decomposition analysis of the changes in agricultural carbon emissions using the log mean Divisia index (LMDI) method. The results were as follows: (1) Based on the results of CO2 EKC estimation, an N-shaped EKC was found; in particular, the upward trend in agricultural carbon emissions has not changed recently. (2) According to the results of the decoupling analysis, expansive coupling occurred for 9 years, which was followed by weak decoupling for 5 years, and strong decoupling and strong coupling occurred for 2 years each. There was no stable evolutionary path from coupling to decoupling, and this has remained true recently. (3) We used the LMDI method to decompose the driving factors of agricultural carbon emissions into four factors: the agricultural carbon emission intensity effect, structure effect, economic effect, and labor force effect. From a policymaking perspective, we integrated the results of both the EKC and the decoupling analysis and conducted a detailed decomposition analysis, focusing on several key time points. Agricultural economic growth was found to have played a significant role on many occasions in the increase in agricultural carbon emissions, while agricultural carbon emission intensity was important to the decline in agricultural carbon emissions. Specifically, the four factors’ driving direction in the context of agricultural carbon emissions was not stable. We also found that the change in agricultural carbon emissions was affected more by economic policy than by environmental policy. Finally, we put forward policy suggestions for low-carbon agricultural development in Jilin province.

scholarly journals Regional Differences and Dynamic Evolution of Carbon Emission Intensity of Agriculture Production in China

2020 ◽  
Vol 17 (20) ◽  
pp. 7541
Author(s):  
Jiaxing Pang ◽  
Hengji Li ◽  
Chengpeng Lu ◽  
Chenyu Lu ◽  
Xingpeng Chen
Keyword(s):  
Carbon Emissions ◽  
Agricultural Production ◽  
Emission Intensity ◽  
Regional Differences ◽  
Carbon Emission ◽  
Dynamic Evolution ◽  
Western Region ◽  
Carbon Emission Intensity ◽  
Emissions Intensity ◽  
The Difference

The study of the carbon emission intensity of agricultural production is of great significance for the formulation of a rational agricultural carbon reduction policy. This paper examines the regional differences, spatial–temporal pattern and dynamic evolution of the carbon emission intensity of agriculture production from 1991 to 2018 through the Theil index and spatial data analysis. The results are shown as follows: The overall differences in carbon emission intensity of agriculture production presents a slightly enlarging trend, while the inter-regional differences in carbon emissions intensity is decreasing, but the intra-regional difference of carbon emissions intensity presented an expanding trend. The difference in carbon emission intensity between the eastern and central regions is not obvious, and the difference in carbon emission intensity in the western region shows a fluctuating and increasing trend. The overall differences caused by intra-regional differences; the average annual contribution of intra-regional differences is 67.84%, of which the average annual contribution of western region differences is 64.24%. The carbon emission intensity of agricultural production in China shows a downward trend, with provinces with high carbon emission intensity remaining stable, while provinces with low intensity are expanding. The Global Moran’s I index indicates that China’s carbon emission intensity of agricultural production shows a clear trend of spatial aggregation. The agglomeration trend of high agricultural carbon emission remains stable, and the overall pattern of agricultural carbon emission intensity shows a pattern of increasing differentiation from east to west.

Energy technological innovation and carbon emissions mitigation: evidence from China

Kybernetes ◽  
2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shuping Cheng ◽  
Lingjie Meng ◽  
Lu Xing
Keyword(s):  
Technological Innovation ◽  
Carbon Emissions ◽  
Emission Intensity ◽  
Carbon Emission ◽  
Fossil Energy ◽  
Content Type ◽  
Carbon Emission Intensity ◽  
Emissions Mitigation ◽  
The Impact ◽  
Positive Effect

PurposeThe purpose of this paper is to examine the effects of energy technological innovation on carbon emissions in China from 2001 to 2016.Design/methodology/approachConditional mean (CM) methods are first applied to implement our investigation. Then, considering the tremendous heterogeneity in China, quantile regression is further employed to comprehensively investigate the potential heterogeneous effect between energy technological innovation and carbon emission intensity.FindingsThe results suggest that renewable energy technological innovation has a significantly positive effect on carbon emission intensity in lower quantile areas and a negative effect in higher quantile areas. Contrarily, fossil energy technological innovation exerts a negative correlation with carbon emission intensity in lower quantile areas and a positive effect on carbon emission intensity in higher quantiles areas.Originality/valueConsidering that energy consumption is the main source of CO2 emissions, it is of great importance to study the impact of energy technological innovation on carbon emissions. However, the previous studies mainly focus on the impact of integrated technological innovation on carbon emissions, ignoring the impact of energy technological innovation on carbon emissions mitigation. To fill this gap, we construct an extended STIRPAT model to examine the effects of renewable energy technological innovation and fossil energy technological innovation on carbon emissions in this paper. The results can provide a reference for the government to formulate carbon mitigation policies.

scholarly journals Spatiotemporal Analysis of Land Use Patterns on Carbon Emissions in China

Land ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 141
Author(s):  
Qiaowen Lin ◽  
Lu Zhang ◽  
Bingkui Qiu ◽  
Yi Zhao ◽  
Chao Wei
Keyword(s):  
Land Use ◽  
Carbon Emissions ◽  
Emission Intensity ◽  
Local Governments ◽  
Carbon Emission ◽  
Land Development ◽  
Land Use Patterns ◽  
Use Patterns ◽  
Carbon Emission Intensity ◽  
Per Capita

Nowadays, China is the world’s second largest economy and largest carbon emitter. This paper calculates the carbon emission intensity and the carbon emissions per capita of land use in 30 provinces at the national level in China from 2006 to 2016. A spatial correlation model is used to explore its spatiotemporal features. The results show that (1) China’s land use carbon emissions continued to grow from 2006 to 2016. The spatial heterogeneity of carbon emission intensity of land use initially decreased and then increased during this period. The carbon emission of land use pattern reached a peak in 2015 and the land use carbon emission intensity was relatively lower in east China; (2) southern China accounts for a majority of the total Chinese carbon sink. Better economic structure, land use structure and industrial structure will lead to lower carbon emission intensity of land use; (3) carbon emissions per capita of land use in China are affected not only by land development intensity, urbanization level, and energy consumption structure, but also by the population policy. It is significant to formulate differentiated energy and land use policies according to local conditions. This study not only provides a scientific basis for formulating different carbon emission mitigation policies for the local governments in China, but also provides theoretical reference for other developing countries for sustainable development. It contributes to the better understanding of the land use patterns on carbon emissions in China.

An Analysis of Changes of Carbon Emissions by Agricultural Production in Sichuan, China in 1997-2010

2013 ◽  
Vol 448-453 ◽  
pp. 4475-4481
Author(s):  
Juan Tan ◽  
Chang Quan Lin
Keyword(s):  
Population Size ◽  
Carbon Emissions ◽  
Agricultural Production ◽  
Emission Intensity ◽  
Carbon Emission ◽  
Sichuan Province ◽  
Negative Effects ◽  
Direct Role ◽  
Carbon Emission Intensity ◽  
Agriculture Modernization

On the basis of the extended Kaya identity and by means of LMDI, the writers analyze quantitatively the 4 factors including carbon emission intensity of planting, agriculture structure, economical level, and population size, etc. thus giving effects to changes of carbon emissions from agricultural production in Sichuan Province from 1997 to 2010.The results show that carbon emission intensity of planting and agriculture structure have brought negative effects to the growth of carbon emissions. These effects will come into being more and more obviously. Economic level played a direct role in growth of carbon emissions, which is considered to be their greatest and everlasting contributor in respect of growth of carbon emissions. The contribution of population size keeps pace with carbon emissions basically. These reflect Sichuan Province have made some achievements in adjusting the agricultural structure in recent years. The rural population have been maintained under stable control in Sichuan. Meanwhile, with its economic development, the agriculture modernization has been improved gradually paying attention to, such as agriculture mechanization, chemization, irrigation and the demand for energy consumption keeps on rising. Finally, carbon emissions have increased in agricultural production of Sichuan Province.

On Carbon Emissions Measure and its Influencing Factors of Yunnan Province: 1998-2008

2011 ◽  
Vol 99-100 ◽  
pp. 539-545
Author(s):  
Ya Zhang ◽  
You Liang Mao
Keyword(s):  
Influencing Factors ◽  
Carbon Emissions ◽  
Emission Intensity ◽  
Carbon Emission ◽  
Yunnan Province ◽  
Low Carbon ◽  
Low Carbon Economy ◽  
Carbon Emission Intensity ◽  
Administrative Area ◽  
Executive Agencies

Coming up with the idea of low-carbon economy, numerous studies both at home and abroad on carbon emissions have emerged, nonetheless of which seldom are studies aiming at specific executive agencies and supervisory authorities of government development plan at provincial administrative area level. This paper, by using calculation formulas in carbon emission calculation guide of IPCC and carbon emission coefficient default value, measured the carbon emissions of Yunnan Province during 1998 and 2008 and analyzed relative influencing factors. The study shows economic growth and industrial restructuring increase the carbon emission intensity which is not remarkably affected by energy restructuring. The key to decrease carbon emission intensity is enhancing energy efficiency.

scholarly journals Decoupling and Decomposition Analysis of Agricultural Carbon Emissions: Evidence from Heilongjiang Province, China

2021 ◽  
Vol 19 (1) ◽  
pp. 198
Author(s):  
Qinyi Huang ◽  
Yu Zhang
Keyword(s):  
Carbon Emissions ◽  
Emission Intensity ◽  
Carbon Emission ◽  
Decomposition Analysis ◽  
Heilongjiang Province ◽  
Grain Production ◽  
Low Carbon ◽  
Intensity Effect ◽  
Carbon Emission Intensity ◽  
Decoupling Analysis

Ensuring food security and curbing agricultural carbon emissions are both global policy goals. The evaluation of the relationship between grain production and agricultural carbon emissions is important for carbon emission reduction policymaking. This paper took Heilongjiang province, the largest grain-producing province in China, as a case study, estimated its grain production-induced carbon emissions, and examined the nexus between grain production and agricultural carbon emissions from 2000 to 2018, using decoupling and decomposition analyses. The results of decoupling analysis showed that weak decoupling occurred for half of the study period; however, the decoupling state and coupling state occurred alternately, and there was no definite evolving path from coupling to decoupling. Using the log mean Divisia index (LMDI) method, we decomposed the changes in agricultural carbon emissions into four factors: agricultural economy, agricultural carbon emission intensity, agricultural structure, and agricultural labor force effects. The results showed that the agricultural economic effect was the most significant driving factor for increasing agricultural carbon emissions, while the agricultural carbon emission intensity effect played a key inhibiting role. Further integrating decoupling analysis with decomposition analysis, we found that a low-carbon grain production mode began to take shape in Heilongjiang province after 2008, and the existing environmental policies had strong timeliness and weak persistence, probably due to the lack of long-term incentives for farmers. Finally, we suggested that formulating environmental policy should encourage farmers to adopt environmentally friendly production modes and technologies through taxation, subsidies, and other economic means to achieve low-carbon agricultural goals in China.

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.

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