How Does Land Urbanization Promote CO2 Emissions Reduction? Evidence From Chinese Prefectural-Level Cities

The process of land urbanization may result in a great change in land use structure, land use intensity, and efficiency, which could further lead to an increase in carbon dioxide (CO2) emissions. Despite rich literature on the link between urbanization and CO2 emissions, the mechanism through which land urbanization promotes CO2 emissions reductions has not been fully investigated. To address this gap, this study theoretically and empirically explores the mechanism of land urbanization’s influence on CO2 emissions by integrating land use optimization and high-quality industrial development into a unified framework. Firstly, the theoretical mechanism analysis indicates that low-level industrial development and land use management promote the increase of CO2 emissions per unit of land at the extensive land use stage; however, high-quality industrial development and land use optimization lower CO2 emissions per unit of land at the intensive land use stage. Subsequently, a STIRPAT model and a spatial adaptive semi-parametric model are employed to verify the relationship between the land urbanization rate and total CO2 emissions. The results indicate that the land urbanization rate and total CO2 emissions present an inverted U-shaped relationship. In addition, the mediating mechanism of the advanced industrial structure, CO2 emissions per unit of GDP, and CO2 emissions per unit of land, are studied using the mediating effect model. Results indicate that CO2 emissions reduction can be achieved by promoting the advanced industrial structure, reducing CO2 emissions per unit of GDP or reducing CO2 emissions per unit of land. Ultimately, this study showed that the Chinese government may reduce CO2 emissions by promoting land use structure optimization, land use intensity regulation, land use efficiency improvement, and adjusting energy consumption structure, upgrading industrial structure, and promoting emission efficiency technologies.

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Probing the carbon emissions in 30 regions of China based on symbolic regression and Tapio decoupling

10.21203/rs.3.rs-303069/v1 ◽

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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LAND USE OPTIMIZATION USING THE FUZZY MATHEMATICAL-SPATIAL APPROACH: A CASE STUDY OF CHELGERD WATERSHED, IRAN

Journal of Environmental Engineering and Landscape Management ◽

10.3846/16486897.2017.1350688 ◽

2018 ◽

Vol 26 (2) ◽

pp. 75-87 ◽

Cited By ~ 4

Author(s):

Moslem Heydari ◽

Afshin Honarbakhsh ◽

Mahdi Pajoohesh ◽

Maryam Zangiabadi

Keyword(s):

Land Use ◽

Industrial Development ◽

Social Issues ◽

Fuzzy Model ◽

Land Use Optimization ◽

Spatial Approach ◽

Proposed Model ◽

Dry Farming ◽

Production Resources

In recent years, inappropriate land use, urban and industrial development along with different pollutions emanating from it gives rise to loss of natural resources and further leads to destructive floods, soil erosion, sedimentation and other various environmental, economic and social damages. Thus, management and planning are essential for the proper utilization, protection and revival of these resources. This study aimed to develop a mathematical-spatial optimum utilization model using FGP – MOLA in watershed including environmental and economic objectives while considering social issues. The results showed that the proposed model can lead to economic growth to 37% and decreasing the environmental damages to 2.4%. Under optimized condition, the area allocated to dry farming lands will decrease about 12% and gardens will increase about 423% and the other land uses remain unchanged too. In addition to, the results demonstrated the usefulness and efficiency of the proposed fuzzy model due to its flexibility and capability to simultaneously provide both optimum values and location of production resources.

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Exploring the Effects of Industrial Structure, Technology, and Energy Efficiency on China’s Carbon Intensity and Their Contributions to Carbon Intensity Target

Sustainability ◽

10.3390/su12198016 ◽

2020 ◽

Vol 12 (19) ◽

pp. 8016

Author(s):

Feng Wang ◽

Min Wu ◽

Jiachen Hong

Keyword(s):

Energy Efficiency ◽

Energy Saving ◽

Historical Development ◽

Industrial Development ◽

Industrial Structure ◽

Electricity Production ◽

Mitigation Measures ◽

Carbon Intensity ◽

Intermediate Input ◽

Residential Energy

To achieve the national carbon intensity (NCI) target, China should adopt effective mitigation measures. This paper aims to examine the effects of key mitigation measures on NCI. Using the input-output table in 2017, this paper establishes the elasticity model of NCI to investigate the effects of industrial development, intermediate input coefficients, energy efficiency, and residential energy saving on NCI, and further evaluates the contributions of key measures on achieving NCI target. The results are shown as follows. First, the development of seven sectors will promote the increase of NCI while that of 21 sectors will reduce NCI. Second, NCI will decrease significantly with the descending of intermediate input coefficients of sectors, especially electricity production and supply. Third, improving energy efficiency and residential energy saving degree could reduce NCI, but the latter has limited contribution. Fourth, the development of all sectors will reduce NCI by 10.11% in 2017–2022 if sectors could continue the historical development trends. Fifth, assuming that sectors with rising intermediate input coefficients would keep their coefficients unchanged in the predicting period and sectors with descending coefficients would continue the historical descending trend, the improvement of technology and management of all sectors will reduce NCI by 14.02% in 2017–2022.

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Enriching plant diversity in grasslands by large-scale experimental sward disturbance and seed addition along gradients of land-use intensity

Journal of Plant Ecology ◽

10.1093/jpe/rtw062 ◽

2016 ◽

pp. rtw062 ◽

Cited By ~ 3

Author(s):

Valentin H. Klaus ◽

Deborah Schäfer ◽

Till Kleinebecker ◽

Markus Fischer ◽

Daniel Prati ◽

...

Keyword(s):

Land Use ◽

Plant Diversity ◽

Large Scale ◽

Land Use Intensity ◽

Seed Addition

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Development of Land Use Regression Model for Seasonal Variation of Nitrogen Dioxide (NO2) in Lahore, Pakistan

Sustainability ◽

10.3390/su13094933 ◽

2021 ◽

Vol 13 (9) ◽

pp. 4933

Author(s):

Saimar Pervez ◽

Ryuta Maruyama ◽

Ayesha Riaz ◽

Satoshi Nakai

Keyword(s):

Land Use ◽

Air Pollution ◽

Regression Model ◽

Urban Areas ◽

Industrial Development ◽

Ambient Air ◽

Ambient Air Pollution ◽

Land Use Classification ◽

Land Use Regression ◽

Air Exposure

Ambient air pollution and its exposure has been a worldwide issue and can increase the possibility of health risks especially in urban areas of developing countries having the mixture of different air pollution sources. With the increase in population, industrial development and economic prosperity, air pollution is one of the biggest concerns in Pakistan after the occurrence of recent smog episodes. The purpose of this study was to develop a land use regression (LUR) model to provide a better understanding of air exposure and to depict the spatial patterns of air pollutants within the city. Land use regression model was developed for Lahore city, Pakistan using the average seasonal concentration of NO2 and considering 22 potential predictor variables including road network, land use classification and local specific variable. Adjusted explained variance of the LUR models was highest for post-monsoon (77%), followed by monsoon (71%) and was lowest for pre-monsoon (70%). This is the first study conducted in Pakistan to explore the applicability of LUR model and hence will offer the application in other cities. The results of this study would also provide help in promoting epidemiological research in future.

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Variation in regrowth ability in relation to land-use intensity in three common grassland herbs

Journal of Plant Ecology ◽

10.1093/jpe/rtab001 ◽

2021 ◽

Author(s):

Anna Kirschbaum ◽

Oliver Bossdorf ◽

J F Scheepens

Keyword(s):

Land Use ◽

Phenotypic Plasticity ◽

Temporal Variation ◽

Temporal Stability ◽

Common Garden ◽

Grassland Management ◽

Land Use Intensity ◽

Evolutionary Changes ◽

Biomass Removal ◽

Reproductive Biomass

Abstract Aims Plant populations in managed grasslands are subject to strong selection exerted by grazing, mowing and fertilization. Many previous studies showed that this can cause evolutionary changes in mean trait values, but little is known about the evolution of phenotypic plasticity in response to land use. In this study, we aimed to elucidate the relationships between phenotypic plasticity – specifically, regrowth ability after biomass removal – and the intensity of grassland management and levels of temporal variation therein. Methods We conducted an outdoor common garden experiment to test if plants from more intensively mown and grazed sites showed an increased ability to regrow after biomass removal. We used three common plant species from temperate European grasslands, with seed material from 58 – 68 populations along gradients of land-use intensity, ranging from extensive (only light grazing) to very intensive management (up to four cuts per year). Important findings In two out of three species, we found significant population differentiation in regrowth ability after clipping. While variation in regrowth ability was unrelated to the mean land-use intensity of populations of origin, we found a relationship with its temporal variation in P. lanceolata, where plants experiencing less variable environmental conditions over the last 11 years showed stronger regrowth in reproductive biomass after clipping. Therefore, while mean grazing and mowing intensity may not select for regrowth ability, the temporal stability of the environmental heterogeneity created by land use may have caused its evolution in some species.

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

Sustainability ◽

10.3390/su13137148 ◽

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.

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