scholarly journals Temporal-Spatial Differentiation and Optimization Analysis of Cultivated Land Green Utilization Efficiency in China

Land ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 158 ◽  
Author(s):  
Qianru Chen ◽  
Hualin Xie
Keyword(s):  
Influencing Factors ◽  
Carbon Emissions ◽  
Spatial Differentiation ◽  
Utilization Efficiency ◽  
Added Value ◽  
Scale Model ◽  
Eastern Region ◽  
Cultivated Land ◽  
Land Utilization ◽  
Extension System

Cultivated land is closely related to national food security, rural economic development and social stability. The cultivated land pollution and carbon emissions caused by chemical fertilizers, pesticides, film residues, etc., in the process of cultivated land utilization pose a serious threat to the cultivated land ecosystem in China. The comprehensive analysis on the cultivated land green utilization efficiency (GUECL), its influencing factors, and optimization direction provides a valuable basis for the green utilization of cultivated land. Based on a panel data of 30 provinces (cities or districts) in China from 2001 to 2016, the GUECL in China under the constraints of pollution and carbon emissions was measured by using a super-efficient SBM-VRS (slack based model-variable return to scale) model. The influencing factors and optimization directions of the GUECL were analyzed through the Tobit model and slack values, respectively. The results show that the GUECL in China rose with fluctuations from 2001 to 2016. Since 2014, the eastern region has surpassed the western region and has become the region with the highest mean GUECL value. The room for resource conservation and pollution reduction varies in different regions of China. Farmers’ dependence on cultivated land and agricultural added value are positively related to the GUECL in China. Farmers’ occupational differentiation, agricultural machinery density, and agricultural disaster rate have had negative effects on the GUECL in China. The loss of the GUECL in China is mainly due to the redundancies of land input, pollution emission, and mechanical input. By analyzing these influencing factors and optimization directions, it is concluded that improving rural land transfer market and agricultural infrastructure construction, establishing a new agricultural technology extension system, and vigorously cultivating new professional farmers are the targeted measures to improve the GUECL.

scholarly journals The evolution of cultivated land utilization efficiency and its influencing factors in Nanyang Basin

2021 ◽  
Vol 36 (3) ◽  
pp. 688
Author(s):  
Zheng-xin JI ◽  
Xiu-li WANG ◽  
Ling LI ◽  
Xiao-ke GUAN ◽  
Lin YU ◽  
...  
Keyword(s):  
Influencing Factors ◽  
Utilization Efficiency ◽  
Cultivated Land ◽  
Land Utilization

scholarly journals The Spatial and Temporal Characteristics of Cultivated Land Utilization in Chengdu-Chongqing Urban Agglomeration

2018 ◽  
Vol 175 ◽  
pp. 04001
Author(s):  
Xiao Yuejie ◽  
Tang Honga
Keyword(s):  
Comprehensive Evaluation ◽  
Dynamic Change ◽  
Efficiency Index ◽  
Urban Agglomeration ◽  
Utilization Efficiency ◽  
Cultivated Land ◽  
Land Utilization ◽  
Ecological Benefits ◽  
Ecological Benefit ◽  
Environment Control

Based on the data of economic, social and ecological benefits of cultivated land in Chengdu-Chongqing urban agglomeration from 2005 to 2014, this paper applies multi-factor comprehensive evaluation model and ArcGIS spatial analysis method to explore the dynamic change and regional difference of the cultivated land use efficiency of Chengdu-Chongqing urban agglomeration. The results showed that the overall efficiency index of cultivated land utilization in Chengdu-Chongqing urban agglomeration, except the economic benefit index, is rising steadily year by year. Among them, the fluctuation of ecological benefit is the most significant, and in 2010 and 2013, there appeared obvious trough. The utilization efficiency of cultivated land in different cities is relatively large, and in terms of distribution pattern, northwest is high, southeast low. The farmland utilization comprehensive efficiency high value district is Ziyang city, Chengdu city and Meishan city. In the future, the Chengdu-Chongqing urban agglomeration should pay more attention to the improvement of agro-ecological environment, control the application of pesticides and fertilizers, and promote the coordinated development of the economic, social and ecological benefits of cultivated land.

scholarly journals Provincial Carbon Emissions Efficiency and Its Influencing Factors in China

2019 ◽  
Vol 11 (8) ◽  
pp. 2355 ◽  
Author(s):  
Wang ◽  
Wang ◽  
Zhang ◽  
Dang
Keyword(s):  
Panel Data ◽  
Influencing Factors ◽  
Carbon Emissions ◽  
Carbon Emission ◽  
Industrial Structure ◽  
Scale Economy ◽  
Green Economy ◽  
Eastern Region ◽  
Tobit Regression ◽  
Emission Performance

We calculated provincial carbon emissions efficiency and related influencing factors in China with the purpose of providing a reference for other developing countries to develop a green economy. Using panel data covering the period from 2004–2016 from 30 provinces in China, we calculated the carbon emission performance (CEP) and the technology gap ratio of carbon emission (TGR) with the data envelopment analysis (DEA) method and the meta-frontier model separately to analyze provincial carbon emissions efficiency in China. No matter which indicator was employed, we found that distinct differences exist in the eastern, the central, and the western regions of China, and the eastern region has the highest carbon emission performance, followed by the central and the western regions. Then, the panel data Tobit regression model was employed to analyze the influencing factors of carbon emissions efficiency, and we found that scale economy, industrial structure, degree of opening up, foreign direct investment (FDI), energy intensity, government interference, ownership structure, and capital-labor ratio have different impacts on the carbon emission efficiency in different regions of China, which indicates different policies should be implemented in different regions.

scholarly journals Temporal Evolution and Influencing Factors of Energy Consumption and Related Carbon Emissions from the Perspective of Industrialization and Urbanization in Shanghai, China

2018 ◽  
Vol 10 (9) ◽  
pp. 3054 ◽  
Author(s):  
Pingxing Li ◽  
Wei Sun
Keyword(s):  
Energy Consumption ◽  
Influencing Factors ◽  
Carbon Emissions ◽  
Temporal Evolution ◽  
Added Value ◽  
Living Standard ◽  
Total Energy Consumption ◽  
Consumption Level ◽  
Economic Output ◽  
Per Capita

Improvements of manufacturability and living standard driven by industrialization and urbanization typically cause a spike in total energy consumption (TEC) and related carbon emissions (TCEM). However, there have been few comparative studies to include industrial and residential energy consumption (IEC and REC, respectively) and related carbon emissions (ICEM and RCEM, respectively). Since China is a major emerging industrial country wherein urbanization is still ongoing, the present study was conducted in an attempt to analyze the temporal evolution of China’s continued energy consumption and related carbon emissions regarding both industrialization and urbanization. The influencing factors of TCEM, RCEM and ICEM are determined via the log-mean divisia index (LMDI) model. The results showed that both TEC and TCEM gradually increased (apart from a slight decrease in 2014); REC and RCEM increased steadily with no sharp peak; while IEC and ICEM declined sharply. TCEM was positively affected by economic output, consumption level, and population size; the influence of consumption level became more and more significant. Per capita GDP and per capita expenditure were the most significant driving factors for RCEM, while industrial added value (IAV) was the main driving factor for ICEM. The temporal evolution and influencing factors of energy consumption and carbon emissions had stage-related characteristics in accordance with Shanghai’s three stages of development. The Shanghai case study provided a comprehensive understanding of energy consumption and related carbon emissions from the dual perspective of industrialization and urbanization.

scholarly journals Spatial Pattern Characteristics and Influencing Factors of Green Use Efficiency of Urban Construction Land in Jilin Province

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Huisheng Yu ◽  
Ge Song ◽  
Tong Li ◽  
Yanjun Liu
Keyword(s):  
Influencing Factors ◽  
Jilin Province ◽  
Utilization Efficiency ◽  
Scale Efficiency ◽  
Eastern Region ◽  
Construction Land ◽  
The Sustainable Development ◽  
Urban Construction ◽  
Temporal And Spatial Characteristics ◽  
The Impact

How to explore the allocation and green utilization level of urban construction land resources has an important role in the sustainable development of the city. Taking 47 counties and cities in Jilin Province as an example, this paper evaluates the green utilization efficiency of urban construction land (GUEUCL) in 2011 and 2015 by using the unexpected output super-SBM model and explores the spatial-temporal differentiation characteristics and influencing factors of GUEUCL by using GIS and machine learning methods. The results show that (1) the GUEUCL in Jilin Province is low, mainly distributed in small- and medium-sized areas, with significant positive spatial correlation. The L-L concentration area is mainly distributed in the eastern region, but the degree of spatial concentration is small, the spatial structure characteristics of the two periods are different, and the spatial heterogeneity is large; (2) the internal factor decomposition shows the impact of pure technical efficiency on the comprehensive efficiency and the restriction ability is stronger than the scale efficiency, that is to say, the factors such as management and technology have a greater impact on the comprehensive efficiency; (3) the relative importance of external factors has always been ranked as socioeconomic factors, urban development factors, and natural science and technology factors. This paper focuses on the temporal and spatial characteristics of each county and city and the influencing factors, which provides a certain value reference for the pilot of ecological construction and the development of ecoenvironmental benefit economic system in Jilin Province.

scholarly journals Study on Land Consolidation Zoning in Hubei Province Based on the Coupling of Neural Network and Cluster Analysis

Land ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 756
Author(s):  
Pengnan Xiao ◽  
Chong Zhao ◽  
Yong Zhou ◽  
Haoyu Feng ◽  
Xigui Li ◽  
...  
Keyword(s):  
Land Reclamation ◽  
Indicator System ◽  
Hubei Province ◽  
Land Evaluation ◽  
Utilization Efficiency ◽  
Land Consolidation ◽  
Cultivated Land ◽  
Land Utilization ◽  
Real Situation ◽  
The Real

Land consolidation zoning is the basis of land reclamation planning and provides a precondition for land management work. Zoning is beneficial to scientific decision-making regarding the use of cultivated land resources, and helps to ensure the quality and production level of cultivated land. Hence, land consolidation zoning is of national significance in terms of safeguarding food security. In this paper, an indicator system for land evaluation and zoning is described based on indicators of natural ecological conditions, socioeconomic conditions, land-use conditions, and the land consolidation potential of Hubei province. The application of self-organizing maps and Hierarchical clustering results in 11 and 12 categories, respectively. After comparing and adjusting these results according to the real situation and key points in the land consolidation of each zone, seven integrated categories are determined. Finally, the land consolidation zoning scheme for these seven integrated partitions is shown to be suitable for Hubei province and in line with the real situation. The proposed scheme offers support for land remediation in a coordinated and orderly way, and provides a scientific basis for improving land utilization efficiency and grain production capacity in Hubei province.

scholarly journals Investigating the Spatiotemporal Disparity and Influencing Factors of Urban Construction Land Utilization Efficiency: Empirical Evidence from Panel Data of China

2021 ◽  
Vol 2021 ◽  
pp. 1-17
Author(s):  
Qingfeng Zhou ◽  
Xianqiang Wu ◽  
Xian Zhang ◽  
Yan Song
Keyword(s):  
Influencing Factors ◽  
Gini Coefficient ◽  
Utilization Efficiency ◽  
Land Utilization ◽  
Urbanization Level ◽  
Construction Land ◽  
Gini Coefficients ◽  
Urban Construction ◽  
Evolution Pattern ◽  
Land Urbanization

This paper corrected the long-term misunderstanding of the land utilization efficiency concept. The Undesirable-Window-DEA model, Dagum Gini coefficient, and spatial panel autoregressive model with fixed effect were used to explore the spatial heterogeneity and influencing factors of urban construction land utilization efficiency in China from 2004 to 2016. The results show the following: (1) China’s overall utilization of urban construction land is still at a low level. It decreased first and then rose, with a “flat V-shaped” evolution pattern. (2) During the study period, the Gini coefficient of urban construction land utilization efficiency for all provinces decreased first, then rose, and decreased again. The utilization efficiency Gini coefficients within provincial grouping were eastern region (0.063), central region (0.101), and western region (0.128). The Gini coefficients within provincial grouping were central versus western (0.121), eastern versus western (0.161), and eastern versus central (0.168). For the contribution to overall inequality of land utilization efficiency, the inequality within groups contributes the most (57.57%), and the inequality between groups accounts for about 25.62%. The overall efficiency is improved, with an evolution pattern of “difference narrowed–differences expanded–difference narrowed.” (3) “economic development,” “industrial structure,” “research development investment,” and “land urbanization level” have significantly positive effect on urban construction land utilization efficiency, while other factors have a negative effect, including “urbanization level of population,” “urban population density,” “cultivated field resources level,” “government influence level,” “land urbanization level,” and “financial dependence level.” This study could provide theoretical support for the implementation of cross-provincial/regional urban construction land quotes reallocation and differential construction land management policies.

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